UNIVERSIDADE DE LISBOA

FACULDADE DE CIÊNCIAS

DEPARTAMENTO DE BIOLOGIA ANIMAL

EVALUATING ECOSYSTEM SERVICES TRADE-OFFS DUE TO LAND USE

CHANGES: TRANSITION TO AN IRRIGATED AGRICULTURE LANDSCAPE

ANA BRITO LEMOS

MESTRADO EM BIOLOGIA DA CONSERVAÇÃO

DISSERTAÇÃO ORIENTADA POR:

Cristina Branquinho e Pedro Pinho

2017

II

Agradecimentos

Quero em primeiro lugar agradecer aos meus orientadores por todo o apoio, tempo e ajuda que me

deram, por toda a disponibilidade e por tudo o que me ensinaram.

A todo o grupo eChanges, que se mostraram sempre disponíveis para ajudar, em especial à Lena, por

toda a ajuda com os meus chás e por todo o tempo disponibilizado para me ajudar!

Ao Mário Boieiro, por toda a ajuda com as minhas abelhas!

Um agradecimento muito especial à Ana Ilhéu, da EDIA, que colaborou em todas as fases deste projeto,

que sem a sua colaboração não teria sido possível.

Quero também agradecer aos meninos da FCUL, Martinha, Pocinhas, Teté e Zé, por todos os dias em

que trabalhámos juntos e em especial à Rita, pelos nascer do sol a contar passarinhos e os dias passados

a trabalhar no jardim!

Ao Nuno, Rochinha, Sofia e João, por estarem lá desde o inicio! E porque podemos não estar sempre

juntos mas estamos sempre lá!

Ao JD e ao Tomy pela paciência quando os vou chatear.

Aos meninos com quem passei as tardes de Verão depois de dias de trabalho! Por todos os momentos

de convívio que depois davam motivação para continuar a trabalhar no dia seguinte, pelos jantares e

pelas saídas cheias de sorrisos e boa disposição! Em especial à Rita, ao André, à Patrícia, ao Gonçalo,

ao Pedro, à Ju, à Bea, ao Rafa, ao Sousa e ao Tomás, com quem as tardes e as noites divertidas

continuaram bem depois do Verão acabar!

Aos F4, Mike, Pepe e Pocinhas, e ainda à Martinha e à Babs por todos os momentos bons, que foram

muitos, por toda a companhia, por todos os risos e por toda a ajuda e apoio durante este longo ano!

III

Abstract

Ecosystem services are the benefits people obtain from ecosystems. Land-use and related land-cover

modifications caused by agriculture aim at increasing the supply of selected or bundles of ecosystem

services, on which human societies depend. However, agriculture also causes un-intended changes in

ecosystem functioning and associated ecosystem services of which agriculture depends. These services

are provided by varied species, but because agriculture is a major driver of biodiversity loss, those

services are affected. Of special concern is the shift to intensive agriculture, which causes the most

losses of biodiversity and associated ecosystem services. For example, pollinators have a major role in

agriculture food production, yet agricultural expansion and intensification is one of the main drivers for

pollinator loss. The general aim of this work was to evaluate how ecosystem services that support

agriculture were affected by agriculture intensification. To do so we analyzed, in a spatial explicit way,

how habitat quality, pollination, carbon storage and soil-related ecosystem services like nutrient cycling

changed in the transition of an extensive agriculture landscape to irrigated agriculture landscape, in

Alentejo, Portugal. Results showed a decrease in habitat quality and crop pollination services, where

irrigated areas show a lower value for these values than more natural land use types, and variation in the

carbon storage, where areas of permanent irrigated crops show higher amounts of carbon stored than

land uses types with annual crops. Nutrient cycling showed no significant differences between land uses.

Overall, Riparian Galleries provided the highest ecosystem service capacity, while irrigated areas were

the ones with the lowest capacity to provide the ecosystem services analyzed. These results allowed to

evaluate the current situation in the area and therefore suggest measures to minimize the loss of some

important ecosystem services, like the promotion of patches of natural vegetation surrounding areas

where the ecosystem services are most needed.

Keywords: Ecosystem services; Agriculture; InVest, Irrigation; Alqueva

Resumo

Os serviços dos ecossistemas são os benefícios que o ser humano obtém a partir dos ecossistemas e que

são necessários para o seu bem-estar. Estes são classificados pelo Millennium Ecossistem Assessment

em quatro categorias principais: provisionamento, suporte, regulação e culturais. As alterações nos

ecossistemas (por exemplo alteração do tipo de uso do solo ou poluição) podem ter impacto na

capacidade dos ecossistemas produzirem esses serviços. Uma das maiores alterações do uso do solo a

nível mundial foi a substituição de ecossistema naturais por ecossistemas agrícolas (aumento da área

agrícola em 466% desde 1700).

A agricultura fornece e depende de vários serviços do ecossistema. A paisagem agrícola não só suporta

a produção de alimentos, fibras e combustíveis, mas também providencia uma grande variedade de

benefícios públicos como o sequestro de carbono e paisagens esteticamente agradáveis. Exemplos destes

benefícios são a reciclagem de nutrientes, o controlo do microclima local, regulação dos serviços

hidrológicos locais, regulação da abundância de organismos não desejados. Os serviços dos

ecossistemas dependem das espécies presentes no local para poderem ser produzidos. No entanto, a

intensificação da agricultura é uma das principais ameaças a biodiversidade. A agricultura moderna

simplificou os sistemas agrícolas tradicionais e substituiu as funções biológicas originalmente

providenciadas por comunidades diversas de organismos, com uma maior introdução de energia e

agroquímicos no sistema. As formas industriais de agricultura têm como objetivo remover as limitações

à produtividade das plantas. Estas práticas de gestão podem afetar a condição dos ecossistemas através

de impactos, como por exemplo, a erosão do solo, a qualidade da água e a emissão de gases efeito de

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estufa. Estes efeitos podem ser exacerbados em regiões semiáridas, onde é feita irrigação para

ultrapassar a limitação da disponibilidade de água.

Tendo isto em consideração, a qualidade do habitat é um fator importante para a agricultura, uma vez

que uma elevada biodiversidade promove muitos serviços do ecossistema que sustentam a agricultura.

A polinização é outro dos serviços necessários à agricultura, visto que a maioria das plantações

dependem ou beneficiam da presença de polinizadores. No entanto, várias espécies de polinizadores

estão em vias de extinção e as populações de polinizadores estão a diminuir, sendo a expansão e a

intensificação da agricultura uma das suas principais ameaças. O armazenamento de carbono é outro

serviço de ecossistema que pode ser comprometido pelas práticas agrícolas e que simultaneamente pode

ser extremamente valioso para a agricultura. O carbono pode ser encontrado em vários compartimentos

do ecossistema, sendo um deles a biomassa acima do solo (por exemplo árvores). Quando a paisagem é

convertida para agricultura este carbono é perdido. Melhorar o carbono orgânico do solo é uma das

formas da agricultura ajudar a diminuir esta perda de carbono, e pode também promover a produção de

alimentos, sendo dessa forma também um serviço fornecido à agricultura.

O solo tem a capacidade de reciclar os a matéria orgânica e de promover a circulação de nutrientes no

sistema. Os organismos do solo degradam e decompõem a matéria orgânica, promovendo assim, por

exemplo, a fertilidade dos solos. A capacidade de decomposição do solo pode ser alterada devido ao

crescimento de plantas e à fertilidade do solo ser mantida não através da natural circulação de nutrientes

no sistema, mas através da introdução de fertilizantes que podem ter consequências negativas tanto para

os ecossistemas (e.g. poluição dos cursos de água) como para a saúde humana (e.g. problemas

respiratórios). Alem disso, a constante movimentação dos solos pode ter como consequência o aumento

da erosão dos mesmos e ainda a perda de matéria orgânica, podendo afetar assim a capacidade de

circulação de nutrientes.

Avaliar os serviços dos ecossistemas assim como os seus trade-offs pode ser utilizado para demonstrar

o valor relativo desses serviços, tornando mais evidente os custos das atividades que os degradam.

Mapear os serviços do ecossistema e os trade-offs que ocorrem na alteração do tipo de uso do solo, pode

ser de bastante auxílio na sua gestão, especialmente se este mapeamento for realizado com resolução ao

nível da propriedade e da cultura agrícola.

Deste modo, este trabalho tem como objetivo avaliar os trade-offs nos serviços do ecossistema devidos

a alteração do uso do solo numa paisagem que sofreu uma alteração de uma agricultura extensiva para

uma agricultura de irrigação. Para isto, será utilizado o software InVest, recorrendo a dados de uma área

agrícola irrigada pela barragem do Alqueva (bloco de irrigação de Monte Novo). Isto permitirá avaliar

os trade-offs dos serviços do ecossistema na área e melhorar estratégias de gestão dos mesmos.

De forma a recolher os dados necessários para as bases de dados foram realizados tanto pesquisa

bibliográfica como recolha de dados no local de estudo. Amostragens de aves (qualidade do habitat) e

polinizadores (serviço de polinização) foram realizadas no terreno e complementadas com pesquisa

bibliográfica posterior. Para a análise da decomposição foi utilizado um protocolo estandardizado, o Tea

Bag Index. Para isto foi recolhido solo no campo e o protocolo foi realizado no laboratório.

Na transição de uma agricultura extensiva para intensiva observou-se um aumento da área irrigada de

4.75% para 56.32%. Como consequência os resultados mostram alterações ao nível dos vários serviços

estudados. Quanto à qualidade de habitat verificou-se um decréscimo dos valores deste serviço. Os

resultados mostraram também que as galerias ripícolas são o uso do solo com maior índice de qualidade

de habitat. Estas comunidades naturais são de extrema importância, uma vez que podem promover a

conectividade entre outras manchas de habitat. O mapa atual mostra um menor armazenamento de

carbono que o mapa histórico (menos 10%). O uso do solo com maior armazenamento de carbono é as

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galerias ripícolas para os dois mapas. No mapa atual o carbono é armazenado principalmente nas áreas

irrigadas com plantações permanentes, como os olivais e as vinhas. Uma das limitações da avaliação

deste serviço do ecossistema, é ter sido baseado em dados da bibliografia, pelo que seria interessante

reavaliar este serviço com medições realizadas no campo para os vários compartimentos de carbono

existentes. Quanto ao serviço de circulação de nutrientes medido através da taxa de decomposição, não

se encontraram diferenças significativas entre os vários usos do solo em análise. Estes resultados podem

estar a ser verificados pelo pouco tempo que passou desde o inicio da irrigação. Uma vez que o protocolo

utilizado para medir a taxa de decomposição foi conduzido em laboratório, os valores obtidos

representam o potencial de decomposição dos solos e não a sua taxa de decomposição real.

Quando todos os serviços foram considerados no seu conjunto, as galerias ripícolas mostraram ser o uso

do solo com maior capacidade de providenciar o conjunto dos serviços do ecossistema analisados, sendo

que os usos do solo irrigados e com maior impacto humano são aqueles que mostram uma menor

capacidade de fornecer estes serviços. Assim, medidas como o aumento do número de manchas de usos

do solo como galerias ripícolas e montado, de forma a que estes possam aumentar a conectividade das

áreas naturais e fornecer serviços do ecossistema a LULC que não os possuam pode ser uma das medias

a ser implementada de forma a otimizar tanto a agricultura como os serviços do ecossistema no bloco

de irrigação de Monte Novo.

Este estudo mostra que a intensificação agrícola através da substituição de culturas extensivas por

irrigadas levou a alterações nos serviços dos ecossistemas na área de estudo. A construção de um modelo

espacialmente explicito usando programas como o InVest são uma boa ferramenta para analisar e avaliar

as alterações que uma área sofre devido às alterações no uso do solo e devem ser tidos em consideração

quando são planeadas medidas de gestão com objetivo de promover uma agricultura mais sustentável.

Palavras chave: Serviços do ecossistema; agricultura; InVest; Irrigação; Alqueva

VI

Conferences

Ana Lemos, Cristina Branquinho & Pedro Pinho. 2016. Evaluating ecosystem services trade-offs due

to land use changes: transition to an irrigated agriculture landscape at the Silvo-Pastoril World

Congress, Évora 2016 – winning 2nd place for best poster

VII

Index

1 Introduction ................................................................................................................................. 1

1.1 Natural Capital and Ecosystem Services ................................................................................. 1

1.2 Agroecosystems....................................................................................................................... 3

1.3 Drylands and water .................................................................................................................. 5

1.4 From extensive agroecosystems to irrigated agroecosystem ................................................... 6

1.4.1 Habitat Quality ................................................................................................................ 6

1.4.2 Crop Pollination .............................................................................................................. 7

1.4.3 Nutrient Cycling .............................................................................................................. 7

1.4.4 Carbon Storage ................................................................................................................ 8

1.5 Mapping ecosystem services in a spatial explicit way ............................................................ 9

1.6 The study area: the Montado system in the Alentejo .............................................................. 9

1.7 Goals ...................................................................................................................................... 10

2 Methods ..................................................................................................................................... 11

2.1 Study area .............................................................................................................................. 11

2.2 Land-use Land-cover mapping .............................................................................................. 11

2.3 Modeling ecosystem services ................................................................................................ 12

2.3.1 Habitat quality ............................................................................................................... 12

2.3.2 Crop pollination ............................................................................................................. 14

2.3.3 Carbon storage ............................................................................................................... 14

2.3.4 Nutrients cycling ........................................................................................................... 14

2.4 Data analysis.......................................................................................................................... 15

3 Results ....................................................................................................................................... 16

3.1 Mapping of the Land-use Land-cover types (LULC) ............................................................ 16

3.2 Modeling Ecosystem Services ............................................................................................... 18

3.2.1 Habitat quality ............................................................................................................... 18

3.2.2 Crop pollination ............................................................................................................. 22

3.2.3 Carbon storage ............................................................................................................... 26

3.2.4 Nutrient Cycling ............................................................................................................ 30

3.3 Ecosystem services overall comparison and potential trade offs .......................................... 31

4 Discussion ................................................................................................................................. 33

4.1 Modeling Ecosystem Services ............................................................................................... 33

4.1.1 Habitat quality ............................................................................................................... 33

4.1.2 Crop pollination ............................................................................................................. 34

4.1.3 Carbon storage ............................................................................................................... 35

4.1.4 Nutrient Cycling ............................................................................................................ 36

4.2 Limitations ............................................................................................................................ 37

4.3 Ecosystem services overall comparison and potential trade offs .......................................... 38

4.4 How to optimize ecosystem services ..................................................................................... 38

VIII

5 Final Remarks............................................................................................................................ 40

6 Bibliographic References .......................................................................................................... 41

Appendix A. Pollinators database ........................................................................................................ 47

Appendix B. Bird Species identified in the Monte Novo irrigation site .............................................. 48

Appendix C. Carbon Pools database ................................................................................................... 49

IX

List of Figures

Figure 1.1.1 - Examples of ecosystem services flow. A - Crop Pollination service flow from habitat to

cropland. B - Climate change regulation service flow. Spatial connections between vulnerable

agricultural areas and hotspots for climate regulation services (Serna-Chavez et al. 2014). .................. 2

Figure 1.2.1- Land converted to agriculture from 1700 to 2000 and projection of this variation till 2050

(Nelleman et al. 2009)) ............................................................................................................................ 3

Figure 1.2.2 - Agriculture related ecosystem services dynamics. Agriculture can suffer a feedback

impact along a chain of effects, in which many ecosystem services important to sustain agriculture might

be degraded by agriculture itself. ............................................................................................................ 4

Figure 2.1.1 - Study area limits. The study area, the Monte Novo irrigation site, is situated around the

village of São Manços, near the city of Évora. ...................................................................................... 11

Figure 3.1.1 – Distribution of the LULC on the Monte Novo irrigation site. A - before the site was

irrigated – historical LULC map; B- after the site was irrigated (2015) – current LULC map. The areas

presented in grey are the ones that were aggregated in an “other occupations” category due to the small

area of occupation. ................................................................................................................................ 16

Figure 3.1.2 - Comparison of the historical areas with the current areas for each LULC. Each LULC is

divided in two columns, being the first referent to the historical map and the second column referent to

the current map. ..................................................................................................................................... 17

Figure 3.2.1.1 - Diversity totals results – Total species of birds identified per LULC and total individuals

of birds identified per LULC in the Monte Novo irrigation site. N= 3 per LULC type, except for irrigated

corn: N=2, error bars represent the standard deviation. ........................................................................ 18

Figure 3.2.1.2 - Diversity indexes results for birds per sampled LULC for the Monte Novo irrigation

site ......................................................................................................................................................... 19

Figure 3.2.1.3 - InVest results for habitat quality service. The results for the habitat quality index vary

between 0 and 1, being 1 the higher habitat quality and 0 the lowest. A- historical LULC map; B- current

LULC map. ............................................................................................................................................ 20

Figure 3.2.1.4 – Percentage of variation between the historical LULC map and the current LULC map

that resulted from the InVest models. The increase in habitat quality score is shown in a scale of green,

while the decrease in the habitat quality score is shown on a scale of red. ........................................... 21

Figure 3.2.1.5 – Difference in the average habitat quality score for each LULC for the Monte Novo

irrigation site. The areas that did not exist in historical map but occur in the current one are being

compared to their equivalents – the “irrigated olive” is being compared to the “extensive olive” in the

historical map, the “irrigated vine” is being compared with the “extensive vine” in the historical map

and the irrigated crops (“irrigated cereal”, “irrigated forage”, “vegetable crops” and “corn” are being

compared to the “irrigated crops” in the historical map) ...................................................................... 21

Figure 3.2.2.1 - Diversity totals results – Total genera of pollinators identified per LULC and total

individuals of pollinators identified per LULC in the Monte Novo irrigation site ............................... 22

Figure 3.2.2.2 – Diversity indexes results for pollinators per sampled LULC in the Monte Novo

irrigation site ......................................................................................................................................... 23

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Figure 3.2.2.3 - InVest Crop Pollination model results for the Monte Novo irrigation site. The values

shown refer to the pollinator abundance index, which was calculated for each cell of the map when the

model was run, and varies from 0 to 1. A- historical LULC map; B- current LULC map. .................. 24

Figure 3.2.2.4 - Percentage of variation of the pollinator abundance index between the historical LULC

map and the current LULC map. The increase in pollinator abundance index is shown in a scale of green,

while the decrease in the pollinator abundance index is shown on a scale of red. ................................ 25

Figure 3.2.2.5 - Comparison of the average pollinator abundance index per LULC for the Monte Novo

irrigation site. The areas that did not exist in historical map but occur in the current one are being

compared to their equivalents – the “irrigated olive” is being compared to the “extensive olive” in the

historical map, the “irrigated vine” is being compared with the “extensive vine” in the historical map

and the irrigated crops (“irrigated cereal”, “irrigated forage”, “vegetable crops” and “corn” are being

compared to the “irrigated crops” in the historical map) ...................................................................... 25

Figure 3.2.3.1 - InVest results for Carbon storage service (Mg/pixel). A pixel is 900m2. A- historical

LULC map; B- current LULC map. ...................................................................................................... 28

Figure 3.2.3.2 – Percentage of variation in the carbon storage between the historical LULC map and the

current LULC map. The increase in carbon storage index is shown in a scale of green, while the decrease

in the carbon storage is shown on a scale of red. .................................................................................. 29

Figure 3.2.3.3 - Difference of total carbon stored (Gg) per LULC. The areas that did not exist in

historical map but occur in the current one are being compared to their equivalents – the “irrigated olive”

is being compared to the “extensive olive” in the historical map, the “irrigated vine” is being compared

with the “extensive vine” in the historical map and the irrigated crops (“irrigated cereal”, “irrigated

forage”, “vegetable crops” and “corn” are being compared to the “irrigated crops” in the historical map)

............................................................................................................................................................... 29

Figure 3.2.3.4 - Carbon per area for each LULC in the Monte Novo irrigation site. Based on the results

from the InVest carbon storage model .................................................................................................. 30

Figure 3.2.4.1 - Tea bag index results. Variation of the average percentage weigh of the tea bags per

LULC during the sampling period. ....................................................................................................... 30

Figure 3.3.1 - Summary of the ecosystem service delivery distribution in the Monte Novo irrigation

site. The map represents the sum of the provision of each ecosystem service studied for the different

LULC in the area. The capacity of delivery ranges from red (lowest delivery capacity) to green (highest

delivery capacity). ................................................................................................................................. 31

Figure 3.3.2 – Ecosystem service relative importance per LULC in the Monte Novo irrigation site for

the current map. Each ecosystem service was scaled from 0 to 100 and then calculated for each LULC,

therefore LULC with higher values for the ecosystem services than the others are more important in the

provision of that ecosystem service. ...................................................................................................... 32

XI

List of tables

Table 1.2.1 – Ecosystem services to and from agriculture divided according to MEA categories.

Adapted from Zhang et al (2007). ........................................................................................................... 4

Table 2.3.1.1 - Summary of the impacts distances used for each threat in the habitat quality InVest

model. .................................................................................................................................................... 13

Table 2.3.1.2 - Habitat classification for each LULC in the InVest Habitat Quality model. For each

LULC a value from 0 to 1 was attributed according to their capacity to be a habitat for the bio indicator

group in use, birds. ................................................................................................................................ 13

Table 3.2.1.1 - Summary of the diversity index results per LULC ...................................................... 18

Table 3.2.1.2 - Mann Whitney U to compare the habitat quality score for LULC that occur in both

historical and current LULC maps. N = Number of polygons of the LULC in the historical map + number

of polygons of the LULC in the current map. ....................................................................................... 22

Table 3.2.2.1 - Summary of diversity totals and diversity indexes for pollinators for the sampled LULC.

............................................................................................................................................................... 23

Table 3.2.2.2 - Mann Whitney U to compare the habitat quality score for LULC that occur in both

historical and current LULC type. N = Number of polygons of the LULC in the historical map + number

of polygons of the LULC in the current map. ....................................................................................... 26

Table 3.2.3.1 - Average value for Carbon pools (Mg/ha) (Complete table – Appendix B) ................. 27

Table 3.2.4.1 - Kruskal Wallis resuts for the comparison of the decomposition rate from the TBI

protocol for different LULC in the Monte Novo irrigation site. ........................................................... 31

1

1 Introduction

1.1 Natural Capital and Ecosystem Services

Natural Capital, ecosystem services and natural resources are essential to human well-being and

economic prosperity (Potts et al. 2015). Ecosystem services are the direct and indirect benefits that

people derive from ecosystems and their biodiversity (Groot et al. 2010; EME 2012).

Society benefits in a multitude of ways from nature in the form of food production, the provisioning of

clean drinking water, the recycling of wastes, and the pollination of crops amongst many others. One of

the strengths of the ecosystem service concept is that it reveals a broad picture of the costs and benefits

of different management choices (Daily et al. 2009). These ecosystem services are all underpinned by

biodiversity, thus the conservation and sustainable use of biodiversity is a key challenge for all sectors

of society (Potts et al. 2015). It is estimated that 60 percent of the world’s ecosystem are degraded or

used unsustainably, which has led to a decline in the services that ecosystems provide us as well

(Reidsma et al. 2006).

Ecosystem Services can be classified in several ways. Yet, the most common is the one used by the

Millennium Ecosystem Assessment (MEA, 2005a), which states that ecosystem services can be divided

in four main categories:

i) Provisioning Services – these are the products with market value obtained from ecosystems

including food, fiber and fuel. Genetic resources, biochemical, natural medicines,

pharmaceuticals, ornamental resources and fresh water are also included in this category;

ii) Cultural Services – These are nonmaterial services people obtain from ecosystems through

spiritual enrichment, cognitive development, reflection, recreation and aesthetic

experiences;

iii) Regulating Services – Are the benefits, with no market value, obtained from the regulation

of ecosystem processes like air quality maintenance, climate and water regulation, erosion

control, regulation of human diseases, biological control, pollination and storm protection;

iv) Supporting Services – These are the ecosystem services, with no market value, that are

necessary for production of all other ecosystem services. They differ from all other

ecosystem services in that their impacts on people are either indirect or occur over a very

long tie, whereas changes in the other categories have relatively direct or short-term impacts

on people.

Europe uses the Common International Classification of Ecosystem Services (CICES) system, which is

the same system that is used by the European Union Mapping and Assessment of Ecosystems and their

Services framework (Maes et al. 2013). This system was proposed to minimize the problems that arose

with the use of many different classifications of ecosystem services, especially when comparing

different studies or integrating ecosystem services assessments with other data

(Haines-Young & Potschin 2011). CICES classifies ecosystem services in three main groups:

i) Provisioning services: includes all material and energetic outputs from ecosystems

ii) Regulating and maintenance services: all the ways in which ecosystems control or modify biotic

or abiotic parameters that define the environment of people. These ecosystem outputs are not

consumed but affect the performance of other services

iii) Cultural and social services: includes all non-material ecosystem outputs that have symbolic,

cultural or intellectual significance (Haines-Young & Potschin 2011).

Ecosystem services are not homogeneous across the landscape and they are not a static over time

(Fisher et al. 2009). Areas that provide ecosystem services may in many cases differ from those which

2

benefit from those ecosystem services (Syrbe & Walz 2012). Because of this, it is possible to distinguish

several types of spatial relations between ecosystem service providing areas and ecosystem service

benefiting areas (Fisher et al. 2009). Divergences between ecosystem service provision and benefit can

cause trade-offs associated with market failure and social imbalances (Figure 1.1.1)

(Elmqvist et al. 2010).

Figure 1.1.1 - Examples of ecosystem services flow. A - Crop Pollination service flow from habitat to cropland. B - Climate

change regulation service flow. Spatial connections between vulnerable agricultural areas and hotspots for climate regulation

services (Serna-Chavez et al. 2014).

Ecosystem service maps are a useful tool for visualizing the effects of land use change on ecosystem

services and the associated trade-offs (Goldstein & Manning 2008). Besides ecosystem service maps, a

set of indicators facilitates the understanding of the effects of land use on change ecosystem services

(Layke et al. 2012). The explicit quantification and mapping of ecosystem services are considered as

one of the main requirements for the implementation of the ecosystem services concept into

environmental institutions and decision making (Burkhard et al. 2012). This is necessary because

ecosystem services provision depend on the location of each land-cover type. For example, causing an

extra input of nitrogen for agriculture can be either exported outside the system if agriculture is done

nearby a land already saturated with nitrogen (e.g. a corn field), or can be incorporated into the system

if it is located nearby a non-saturated ecosystem (e.g. a forest) (Jackson et al. 2008). These trade-offs

analysis can be done by mapping ecosystem services, which may also provide further information: 1)

how to optimize ecosystem services while benefiting biodiversity (Willemen et al. 2013); 2) what are

3

the trends in provision of ecosystem services and how different drivers affect them over time

(Malinga et al. 2015) ; 3) what are the synergies and trade-offs between multiple ecosystem services

(Queiroz et al. 2015); 4) the costs and benefits of maximizing ecosystem services (Schägner et al. 2013);

and 5) how supply and demand of these ecosystem services varies spatially (Schulp et al. 2014).

Land use and related land cover modifications have a strong impact on ecological functioning.

Alterations of ecological integrity lead to increasing or decreasing supplies of selected or bundles of

ecosystem services, on which human societies depend (Burkhard et al. 2012). Worldwide, there has

been an extensive conversion of land use over the past decades, with loss of natural habitat elements,

being that agriculture has been the major responsible for this changes in the last 300 years

(Arroita et al. 2013). The land-use changes due to agriculture are one of the most important drivers of

biodiversity loss. Together with other environmental changes, such as climate change, pollution, and

biotic invasions, these have degraded biodiversity to such an extent that the contribution of many

ecosystem services to human well-being is becoming increasingly eroded

(Hooper et al. 2005; MEA 2005b; UK National Ecosystem Assessment 2011).

1.2 Agroecosystems

Covering over a third of total global land area, agriculture (croplands and pastures) represents

humankind’s largest engineered ecosystem (Zhang et al. 2007). The area of cultivated land increased

globally 466% from 1700 to 1980, to such an extent that croplands and pastures have become one of the

largest “biomes” on the planet (Figure 1.2.1) (Arroita et al. 2013). Agriculture can be, more or less

intensive, that is with different agricultural practices and techniques that have different degrees of

impacts on the environment (Bennett 1973). Intensification is a consequence of the so-called “Green-

Revolution”, which promoted the expansion on high-yielding crops that depend on the use of synthetic

fertilizers and pesticides, and on the implementation of irrigation and mechanization (Arroita et al. 2013)

to consistently sustain their rates output (McLaughlin & Mineau 1995).

Figure 1.2.1- Land converted to agriculture from 1700 to 2000 and projection of this variation till 2050 (Nelleman et al. 2009))

4

Modern agriculture has simplified traditional agroecosystems and replaced biological functions,

originally provided by diverse communities of organisms, with increased external inputs of energy and

agrochemicals. Industrial forms of modern agriculture aim to remove limitations to plant productivity

mainly by irrigation and adding inorganic nutrients, by crop breeding to improve the genetic basis for

plant productivity, mechanical loosening of the soil structure that allows for better root penetration and

growth and replacing biological pest and weed control with pesticides (Tilman et al. 2001). These

management practices can affect ecosystem conditions through impacts on, for example, soil erosion,

water quality, or greenhouse gas emissions (Kragt & Robertson 2014). Industrial agriculture depends

on expensive inputs from off the farm, many of which generate wastes that harm the environment.

Synthetic chemical pesticides and fertilizers are polluting soil, water and air, harming both the

environment and human health (Horrigan et al. 2002). One example of this was the discovery of the

Haber Bosch process in 1913, that is still used today to produce ammonia (a reactive form of nitrogen

that plants can use), which spurred a leap in global crop yields. However, all this reactive nitrogen then

cycles from one polluting form to another: nitric acid, which causes acid rain; nitrates in waterways;

urban ozone and soot particles which endanger respiratory health (Kaiser 2001). However, these

practices made agriculture a major driver of environmental change, leading to environmental damage

and degradation of several ecosystem services (Sandhu et al. 2010).

Natural ecosystems tend to have more niches, creating more opportunities for different species,

compared to most managed agroecosystems which are simplified and have fewer predatory and parasitic

species and lower genetic diversity within a species (Odum 1997). Still, the natural capital contained in

agricultural landscapes not only supports the production of food, fiber and biofuels, but also provides a

variety of public benefits to society such as carbon sequestration, aesthetic landscapes and biodiversity

conservation (Swinton et al. 2007). Agricultural ecosystems are primarily managed to optimize the

provisioning services of food, fiber and fuel (Zhang et al. 2007). This management should take in

consideration that agrosystems both relies (input) and provides (output) ecosystem services (figure

1.1.2) (Zhang et al. 2007). On the input side, many ecosystem services, like crop pollination, provide

direct benefits to agricultural production (Zhang et al. 2007). On the output side, agriculture supplies a

range of “provisioning” ecosystem services that are traded in commodity markets. Agrosystems may

further sustain supporting services and regulating services (MEA 2005b). In the process, besides

sustaining some supporting and regulating services, agriculture also depends upon a wide variety of

them such as soil fertility and pollination (Zhang et al. 2007), that determine the underlying biophysical

capacity of agricultural ecosystems (Wood et al. 2000) (Table 1.2.1).

Figure 1.2.2 - Agriculture related ecosystem services dynamics. Agriculture can suffer a feedback impact along a chain of

effects, in which many ecosystem services important to sustain agriculture might be degraded by agriculture itself.

5

Table 1.2.1 – Ecosystem services to and from agriculture divided according to MEA categories. Adapted from Zhang et al

(2007).

Ecosystem Services to agriculture Ecosystem Services from

agrosystems

Supporting

services

Soil structure and fertility

Provisioning

services

Food, fiber, and

fuel production

Nutrient cycling Water supply

Water provision

Genetic biodiversity

Supporting

services

Soil

conservation

Regulating

services

Soil retention

Pollination Wildlife habitat

Dung burial

Natural control of plant

pests

Regulating

services

Climate change

mitigation Food sources and habitat

for beneficial insects

Water purification Cultural

Services

Aesthetic

landscapes Atmospheric regulation

Ecosystem

dis-services

Pest damage

Ecosystem dis-

services

Habitat loss

Competition for water

from other ecosystems

Nutrient runoff

Competition for

pollination

Pesticides

poisoning of

non-target

species

1.3 Drylands and water

Drylands are areas where plant production is limited by water availability; the dominant agriculture uses

are livestock grazing and cultivation (MEA 2005a). They are many times thought to be unproductive

and barren, yet they are where nearly 40% of the world’s population lives (White & Nackoney 2003).

Freshwater resources in drylands are limited and variable in supply. This lack of water and its variability

render the ecosystem goods and services in drylands provided by surface water, groundwater, and

wetland habitats critically important (White & Nackoney 2003). The construction of dams for irrigation

is one of the practices many times used to solve this limitation, therefore a large number of dams is

found in dryland areas (MEA 2005b).

The benefits that result from the construction of dams are diverse, such as the provision of water and

electricity in the long term, as well as opportunities for new economic activities and job creation. Other

benefits that often occur are improved conditions for inland navigation, tourism development or

aquaculture (Dias-Sardinha & Ross 2015). On the other hand, the installation of dams entails a number

of negative consequences such as, the physical transformation of rivers and their natural flow, the

profound shock imposed on the river basin ecosystems and human communities that live on its banks

6

and the loss of cultural heritage such as archaeological remains, having even the potential to alter local

climate (Dias-sardinha & Ross 2015). Whether with the purpose of producing hydroelectric power or

agricultural irrigation, dams irreversibly alter the landscape and create a new feature that enables the

realization of, for example, nautical activities where previously it was not possible

(Dias-sardinha & Ross 2015). It also leads to land use changes from extensive to more intensive types

of agriculture. Irrigation schemes may imply several environmental problems from flooding of valleys

and drainage of aquifers for water collection to a rise in the use of chemical compounds, and even loss

of fallows in crop rotation (Perez 1990).

1.4 From extensive agroecosystems to irrigated agroecosystem

Agroecosystems are artificial systems altered to serve human purposes (FAOSTAT 2016). In the

Mediterranean dryland area, the rain fed agriculture is the main form of crop production. This kind of

system is highly diverse with a wide range of rain fed crops, including tree crops (like olives, fruits and

nuts) and field crops (mainly wheat and barley) (Ryan et al. 2006). However, extensive natural areas are

being converted for human use, and management practices are intensifying in already human-dominated

lands. As a result, the irrigated surface has doubled during the last 50 years. Modern agricultural

techniques result in increased productivity, but often at the high environmental cost leading to negative

environmental alterations (Foley et al. 2005).

Biodiversity is highly related with some of the ecosystem services that are of great importance to

agriculture. Therefore, biodiversity conservation is a major tool to promote these ecosystem services.

Biodiversity is linked with ecosystem services on several levels: microorganisms are responsible for

decomposition (recycling of wastes and detoxification service) and nutrient cycling; primary producers

(plants) are responsible for biomass production (provisioning services) and carbon capture (carbon

sequestration and storage service), top predators and parasites play an important role on population

regulation (natural control of pests) and pollinators secure many food crops (crop pollination)

(Mace et al. 2012).

Aiming at quantifying the trade-offs that occurred in the transition from an extensive to intensive

agroecosystem, four ecosystem services were selected to be mapped and quantified: habitat quality, crop

pollination, carbon storage and nutrient cycling. These four ecosystem services are highly related with

agriculture and are important to support and sustain it.

1.4.1 Habitat Quality

Agriculture related ecosystem services are supplied by varied species and functional groups (groups of

species with similar ecophysiological and life-history traits (Moonen & Bàrberi 2008)) over a range of

scales and influenced by human activities both intentionally and unintentionally (Zhang et al. 2007). It

is known that species diversity is affected by environmental drivers such as climate change, invasive

species, land use change or pollution. These drivers lead to led options for management and increase the

vulnerability of ecosystem services once these drivers have a direct impact on ecosystem services, and

affect biodiversity. The loss biodiversity may decrease profitability of ecosystem services, mainly the

ones provided by a specific group of species (Carpenter et al. 2009). Currently there are many evidences

that biodiversity loss reduces the efficiency by which ecological communities capture biological

essential resources, produce biomass, decompose and recycle biologically essential nutrients, therefore

evaluating this losses is highly necessary (Cardinale et al. 2012). Because sampling and evaluating all

the biodiversity for a given area is impracticable, biological indicators can be used to do this evaluation.

Some groups of species are more suitable than others to evaluate changes on their environment. Birds

have some characteristics that make them good indicators of biodiversity and sustainability: 1) They are

high in the food chain, thus integrating changes at other levels; 2) They occupy a broad range of

7

ecosystems and have varied natural histories; 3) a wealth of data has been (or can be) collected by

volunteers and professionals, and bird population sizes and trends, and conservation status, are often

well known relative to other taxa; and 4) they are meaningful to a wide audience including the public

(Heath & Rayment 2003).

1.4.2 Crop Pollination

The expansion of agriculture is leading to negative effects on yields of pollinator-dependent crops by

isolating crop fields from the pollinators diversity that natural habitats harbors (Carvalheiro et al. 2011).

In Europe, the majority of crops depend or benefit from insect pollination, being that an estimated 10%

of the total economic value of European agricultural output for human food amounted to 22€ billion in

2005 was dependent upon insect pollination (Potts et al. 2015). However, pollinator species are

disappearing and pollinator populations are declining (Potts et al. 2015).

Pollinators extinction drivers vary in space and time, interact synergistically, and affect species and/or

functional groups differently (Nieto et al. 2014). The main threats identified were habitat loss as a result

of agriculture intensification (e.g., changes in agricultural practices including the use of pesticides and

fertilizers), urban development, increased frequency of fires and climate change (Nieto et al. 2014).

Some life history traits were associated to the most threatened species: sociality (e.g., bumblebees), host-

plant specialization (e.g. bee species associated to coastal areas). The species richness of bees increases

from north to south in Europe, with the highest species richness being found in the Mediterranean

climate zone. The Iberian, Italian and Balkan peninsulas are important areas of species richness. The

largest numbers of threatened species are located in South-Central Europe and the pattern of distribution

of Data Deficient species (for which there is insufficient information for conservation status) is primarily

concentrated in the Mediterranean region (Nieto et al. 2014).

Declines in species richness of bumblebees have received particular attention, especially in Europe and

North America. Many pollinator-dependent crops rely on bees for yield, and the threats that bees are

facing have raised concerns that crop pollination might also be at risk (Bommarco et al. 2012). Legumes

in general, and especially red clover, which are important nectar and pollen resources for bumblebees,

have become much rarer in the landscape. This reduced availability and increased fragmentation of

resources, is a probable reason why only generalist and highly mobile bumblebee species have been able

to maintain large populations in intensively managed agricultural landscapes (Bommarco et al. 2012).

It is important to consider that pollinators are known to be affected by distance to natural habitat, where

they find refuge and nesting sites, and by floral diversity, where pollinators find their food resources

(Carvalheiro et al. 2011). This distances can be used to produce pollination capacity maps in order to

evaluate where pollinator promotor measures are prioritary.

The most widely managed pollinator in Europe is the honeybee (Apis mellifera), with most wild and

feral colonies already lost (Potts et al. 2015). However, non-honeybee pollinators made an important

indirect contribution to pollination by altering honeybee foraging patterns (Carvalheiro et al. 2011).

Wild pollinators in Europe are dominated by approximately 2000 species of wild bees, like bumblebees

and solitary bees, and hoverflies, with a smaller contribution of butterflies, beetles and other fly species

(Potts et al. 2015).

1.4.3 Nutrient Cycling

Soils, the basic substrate for many ecosystems and human activities, have been considered a black-box

within the ecosystem services framework because of their focus on what happens above ground

(Dominati et al. 2010). Soils are an important determinant of the economic status of the nations, it is

essential to include them in ecosystem service frameworks that inform decision-making and

environmental policies (Dominati et al. 2010).

8

Soil properties can be divided in two major groups: inherent soil properties (these include properties

like slope, depth, cation exchange capacity and clay types) and manageable soil properties (like soluble

phosphate, mineral nitrogen, organic matter contents and macroporosity) (Dominati et al. 2010). The

manageable properties assume more practical importance as they provide the opportunity for

agronomists, farmers and other stake-holders to optimize the provision of ecosystem services from soils

(Dominati et al. 2010). Variations in the soil natural capital can lead to very marked differences in land

use and farming systems and associated environmental footprint (Dominati et al. 2010).

Anthropogenic drivers, such as land use, farming practices and technologies, also influence soil

processes. The type of land use determines the type of disturbance as well as inputs applied to the soil.

Farming practices determine the level of intensity of the disturbances and the amount of inputs to the

soil (Dominati et al. 2010).

Soils provide many ecosystem services, and they fall into the provisioning, regulating and cultural

ecosystem services categories. In this work we will focus primarily on the regulating services that can

be obtained from the soil, mainly the recycling of wastes and detoxification and carbon storage

(Dominati et al. 2010).

Soils can self-detoxify and recycle wastes. Soil biota degrades and decomposes dead organic matter into

simpler forms, that organisms can reuse. Soils can also absorb (physically) or destroy chemical

compounds that can be harmful to humans, or organisms useful to humans (Dominati et al. 2010).

Decomposition can be altered due to plant growth being harvested and soil fertility maintained not

through nutrient recycling, but fertilizers, since fertilizers can negatively impact soil organisms that are

responsible for several processes, including decomposition (Altieri 1999). Therefore, land use-intensity

can change soil decomposition capacity.

1.4.4 Carbon Storage

Perhaps one of the soil most important ecosystem service is the ability of soils to store carbon as stable

organic matter which is a non-negligible benefit when talking about off-setting greenhouse gases

emissions (Dominati et al. 2010). Changes in the vegetation can lead to the release of carbon from drier

regions and they deserve consideration when discussing carbon balance at the global scale (Bonino

2006).

Carbon storage on terrestrial systems largely depends on the sizes of four carbon “pools”: aboveground

biomass, belowground biomass, soil and dead organic matter. Aboveground biomass comprises all

living plant material above the soil, belowground biomass encompasses the living root systems of

aboveground biomass. Soil organic matter is the organic component of soil, and represents the largest

terrestrial carbon pool. Dead organic matter includes litter as well as lying and standing dead wood

(Tallis et al. 2011). Agriculture is defined as an anthropogenic manipulation of Carbon through uptake,

fixation, emission and transfer of Carbon among these different pools (Lal 2004).

Land cover change involving changes in vegetation lead to modifications of the physiochemical

characteristics of the soils, which can also include changes in the soil organic reserves (Bonino 2006).

Therefore, should be considered that increasing soil organic matter improves soil nutrient availability

and water holding capacity, thus increasing plant productivity and reducing surface runoff of water,

which in turn decreases sediment loss and soil erosion (Teixeira et al. 2011). Decreasing water runoff

and soil erosion have positive effects even outside the plot. Sediments, nutrients, organic matter and

pesticides in water contribute to silting, eutrophication and contamination of surface waters

(Teixeira et al. 2011).

All of these suggests that LULC changes and intensification of agricultural practices leads to changes

9

in the carbon storage capacity since it leads to alterations in plant structure, both above and below ground

and also to changes in soil structure (Tallis et al. 2011).

1.5 Mapping ecosystem services in a spatial explicit way

The Integrated valuation of Environmental services and Trade-offs (InVest) tool, developed by the

Natural Capital Project, can be used to map and value the goods and services from nature

(Tallis et al. 2011). This is a spatially explicit modeling tool, based on ecological production function

and economic valuation methods (Nelson et al. 2009). InVest consists of a suite of models that use Land

Use/Land Cover (LULC) patterns to estimate levels and economic values of ecosystem services,

biodiversity conservation, and the market value of commodities provided by the landscape (Nelson et

al. 2009). It can be run at different levels of complexity, making it sensitive to the data availability and

an understanding of system dynamics (Nelson et al. 2009).

1.6 The study area: the Montado system in the Alentejo

The Alentejo region represents about a third of the Portuguese continental land

(Fragoso & Marques 2007). Despite this great extension, the region accounts for only 7% of the total

population of the country, and it has the highest ageing rates in Portugal (Dias-sardinha & Ross 2015).

Alentejo is a Mediterranean-climate semi-arid region, with long dry summers where the temperatures

reach 30-40 ºC. Precipitation during the wet season is irregularly distributed and shows great annual

fluctuations, with frequent torrential rains in winter, varying from 500-650 mm, concentrated in the

period October-March (Correia 1993)

The Montado is the main agro-silvo-pastoral system in the Alentejo region characterized by a savannah-

like physiognomy, and is therefore sometimes designated as an open oak savannah

(Pinto-Correia & Mascarenhas 1999; Correia 1993). The trees are cork and holm oaks (Quercus suber

and Quercus rotundifolia), which can be found in monospecies or mixed stands. Generally, the tree

cover does not follow a regular pattern, and the densities vary from 20 to 80 trees per hectare

(Pinto-Correia & Mascarenhas 1999). This extensive oak forests are interspersed with scattered patches

of productive or abandoned orchards, riparian vegetation and scrubland. This system is particularly

important in terms of vertebrate conservation, due to the diverse wildlife that it supports

(Rosalino et al. 2009). In the traditional Montado, the ground cover is a rotation of

culture/fallow/pasture, with rotation periods depending mainly on the soil quality and on the main goal

of the Montado exploitation (Pinto-Correia & Mascarenhas 1999).

Traditionally in the Alentejo region the low rainfall and open plains condition the agricultural practice

usually governed around large estates, which in turn motivates high population dispersion

(Dias-Sardinha & Ross 2015). Production activities in this area were characterize by extensive livestock

grazing of cattle, sheep, goats, cattle and the Iberian pig. These were combined with cereal crops

cultivated in long rotations with fallow (Pinto-Correia & Godinho 2013). There are two distinct trends

in the current Montado system: extensification and intensification. Both trends can co-occur in a single

holding, in parcels of land having different characteristics. Under extensification, crop cultivation has

been abandoned and stocking rate is reduced, larger areas are used as pastures. On the other hand, in

areas where intensification occurs, there may be an intensification in cultivation or in livestock

production. If crop cultivation is the main objective, the tree density is often reduced and the cultivation

depends on mechanization and deep ploughing (Pinto-Correia & Mascarenhas 1999).

The Alqueva Multi-purpose Dam (EFMA) project emerged in 1957 with the drafting of the Alentejo

Irrigation Plan, which aimed at improving the farming conditions (Dias-Sardinha & Ross 2015). The

EFMA project aimed to provide water for irrigation, urban and industrial uses, electricity production

and the regulation of the Guadiana river flow (Ministério do Ambiente e do Ordenamento do Território

10

2001). Irrigation agriculture allows for increased productivity (EDIA 2005), and it is seen as one of the

main drivers for the development of rural regions, allowing for better socio-economic conditions

(EDIA 2005). It has a major role in the reduction of the vulnerability of the production systems because

it promotes water storage, allowing a regulation of the water availability to the crops. This has particular

importance in a place that is marked by climatic changes and extreme droughts phenomena.

Agricultural expansion and intensification are major drivers of biodiversity loss, biotic homogenization

and changes in community composition and functional diversity (Mclaughlin & Mineau 1995a).

Although current efforts to protect the agricultural resource base may have a positive influence on

environmental quality and, by extension, on the wild biota in agricultural landscapes, this is no guarantee

that biodiversity is being preserved (Mclaughlin & Mineau 1995a).

The Mediterranean drylands have been identified as one of the most prominent regions affected by

climate change, being that an increase in drought events has been registered in this region over the past

40 years, and these events are expected to become more frequent in the future (Schroter et al. 2005).

Water availability is directly connected with the provision of many ecosystem services, reinforcing that

the implementation of dams can have great benefits in drylands (Groot et al. 2010). Changes in

temperature, the most predictable of the climate change impacts, will increase water losses from lakes

and reservoirs and raise evapotranspirative demand for water, while increasing atmospheric moisture

content. Rising temperatures will therefore increase crop water demand, deplete soil moisture faster and

increase irrigation demand (Turral et al. 2010). Therefore, irrigation impacts on the landscape will be

extent to the new irrigated areas.

1.7 Goals

The aim of this work was to evaluate in a spatial explicit way the ecosystem services trade-offs due to

land use changes from an extensive agro-forestry system to irrigated agriculture. This allowed to provide

insights on how to optimize ecosystems service provision while maintaining biodiversity. This was done

using as a case study located in Alentejo, South Portugal, in a plot that has been irrigated since the

construction of the Alqueva dam. To do this several tasks were carried out: 1) Creation of land-cover

map before and after the transition to agriculture; 2) Collect data related to the ecosystem services

associated to each land-cover type identified. The ecosystem services that were accessed were habitat

quality, crop pollinators, carbon storage and nutrient cycling (decomposition); 3) Whenever necessary

(i.e. when no reliable bibliographic data is available) further data was collected in the field regarding

the quantification of ecosystem services for each land-cover type; 4) Use of InVest model to determine

the base-level (historical) and current ecosystem services, and determine what occurred in the transition;

5) Evaluate the trade-offs between the ecosystem services studied for the several LULC in the landscape;

6) Finally, we aim to suggest management practices according to our results in order to promote

ecosystem services, agriculture and conservation of biodiversity.

11

2 Methods

2.1 Study area

This project was conducted in the Monte Novo irrigation plot (Alentejo, Portugal). Climate is typically

Mediterranean with an annual rainfall of 400-600 mm, mostly falling in the wet season. Average

temperature varies from 9.6 (January) to 24.1ºC (August), with an annual medium of 16,5ºC,

temperature records ranging from -2.9 to 46ºC. (averages from 1981-2010, IPMA 2016a). Regarding

the sampling year (2016), the temperature, was close to the average values, with an average minimum

temperature of 10.0 ºC and an average maximum temperature of 22.1 ºC. When it comes to the

precipitation, the month of May had an average 95.5 mm of precipitation. All the values where collected

in the Évora meteorological station (IPMA 2016b).

The area is being irrigated by water from the Alqueva dam since 2009 (EDIA 2011). The Alqueva dam

is located in the Guadiana River and it’s the largest water reservoir in Portugal and Western Europe,

with 250 Km2 (Bettencourt & Grade 2009) (Figure 2.1.1). The Monte Novo irrigation site has a total

surface of 25 000 ha, of which 7 100 ha are occupied by irrigated agriculture (Santos et al. 2011).

Figure 2.1.1 - Study area limits. The study area, the Monte Novo irrigation site, is situated around the village of São Manços,

near the city of Évora.

2.2 Land-use Land-cover mapping

Current and historical Land-Use Land-Cover (LULC) maps were created in a geographic information

system. Two base-maps with local cartography were provided by the local administration (EDIA). A

pre-treatment of those maps was necessary because LULC classes did not correspond between dates

(1990 and 2015). This was done by reclassifying some LULC types adopting the more recent

terminology. To reduce complexity when comparing LULC over time (i.e. in order not to create multiple

very small polygons), and after verifying in aerial photography, we considered that some LULC types

12

did not change between dates: riparian galleries and artificial areas (including main roads and urban

area).

In order to select the LULC to be sampled in the field, we calculated the total area occupied by each

LULC and a selection was made based on the total area occupied (See Results Figure 3.2.2) and on the

importance of the cultures for the region. The LULC selected were Montado, Montado with crops,

Extensive crops, Irrigated crops, Extensive olive, Irrigated olive, Pine and Eucalyptus plantations,

Extensive vine. Irrigated vine, Riparian galleries, Corn, Irrigated forage, Vegetable crops, Irrigated

cereal.

2.3 Modeling ecosystem services

InVest (Tallis et al. 2011) was used for modeling ecosystem services in the historical and current

situation. InVest is composed of several modules, each one running a separate model. The data-base for

each InVest module was created using both bibliographic research and field work. The field data was

collect only for the LULC selected before. To ensure that the bibliographic data collected was a good

representation of our study area, some criteria were implemented for the bibliographic research: the

study area should be similar to ours regarding LULC type (irrigated area), climate (dryland) and should

be as recent as possible (bibliography sources from 1995 to 2016).

For field work some criteria were also implemented to ensure a good representation: the area to sample

(polygon) should be the largest of each culture type and for the nutrient cycling service all should have

the same type of soil. To ensure these we used the land cover maps provided by EDIA and the Soil

categories from SNIAmb. Also, for all InVest models raster’s with 30m resolution cells were used as

input LULC maps. Because each InVest model has its own requirements, they are presented separately

below.

2.3.1 Habitat quality

To evaluate the habitat quality ecosystem service, bird diversity was used as a biological indicator. To

model habitat quality using InVest two important metrics are important: the abundance of birds per

LULC type and the effect of threats on bird’s population (including the strength and distance of impact).

For field work the LULC selected were Riparian galleries, Montado, Extensive agriculture, Irrigated

Olive, Irrigated Vine and Corn. On one plot per LULC, birds were counted within four hours after

sunrise using a single 10 min point count per patch. Both auditive and visual contacts were recorded

(Bibby et al. 2000; (Sutherland et al. 2004). This allowed us to characterize species from each LULC

type and their abundance. Note that for the non-sampled LULC types data from the bibliography were

used.

After bibliographic analysis and taking into consideration the local LULC and species found during

field-work (see Results), three threats were selected to run the model: paved roads, pesticides and

presence of urban areas. Threats were considered to impact the overall bird community, and were not

species specific.

Paved roads are known to affect negatively the presence of biodiversity near them. In the case of birds

some reasons have been proposed for this. One of them is the traffic noise, since it can interfere with

the acoustic communication on which birds depend for establishment and maintenance of territories and

for intra-pair and adult-young communication (Summers et al. 2011). Other causes of disturbance by

roads may be related to vehicle lights and motion or could be direct mortality because of collisions with

traffic (Summers et al. 2011).

Pesticides may lead to a decline on birds in a given area acting in three different ways. First, insecticides

may deplete or eliminate arthropod food supplies, which are exploited by adults and their dependent

13

young during the breeding season and, in doing so, reduce breeding productivity. Secondly, herbicides

may reduce the abundance of non-crop plants that are the hosts for arthropods taken as food by farmland

birds during the breeding season, thereby also reducing breeding productivity. Finally, herbicides may

also deplete or eliminate weed species, which provide either green matter or seeds for herbivorous and

granivores species, respectively, thereby reducing survival of those birds that rely on those food supplies

(Boatman et al. 2004). Therefore, pesticides have a negative impact both in herbivorous and in

insectivorous birds.

The presence of urban areas affects the biomass and the richness of the avian community in a given area

(Chace & Walsh 2004). Taxonomically bird communities in distinctly different habitats are most

different in the least disturbed sites (more richness) and the most similar in the most urbanized sites (less

richness) (Chace & Walsh 2004). Urbanization selects for omnivorous, granivores, cavity and branch

nesting, feed on the ground and medium size species (Chace & Walsh 2004; Pinho et al. 2016).

All the threats selected for the model were based on bibliographic research, being that the impact

distance for each threat was also based on bibliographic research (table 2.3.1.1).

Table 2.3.1.1 - Summary of the impacts distances used for each threat in the habitat quality InVest model.

Threat Impact distance (Km)

Paved roads 0.95

Pesticides 0.10

Urban areas 0.50

To run the model, the LULC needed to be classified from 0 to 1 according to habitat suitability for the

group of organisms that are being used in the model (table 2.3.1.2). Accordingly, to results Montado

and the Riparian galleries presented the highest number of species and abundance (see Results) and were

thus classified with the maximum value.

Table 2.3.1.2 - Habitat classification for each LULC in the InVest Habitat Quality model. For each LULC a value from 0 to

1 was attributed according to their capacity to be a habitat for the bio indicator group in use, birds.

LULC Habitat

Montado 1

Montado with crops 0.9

Extensive crops 0.7

Irrigated crops 0.35

Extensive olive 0.6

Pine and Eucalyptus

plantations 0.6

Extensive Vine 0.6

Riparian galleries 1

Irrigated Olive 0.5

Corn 0.35

Irrigated forage 0.35

Vegetable crops 0.35

Irrigated Vine 0.5

Irrigated cereal 0.4

Urban areas 0.2

14

2.3.2 Crop pollination

To calculate pollination service, the Apidae family was used. This is the main taxa responsible for the

pollination service (Carvalheiro et al. 2011) and the InVest model was design for it. To fine tune the

model to our study area, we needed to know which genera were dominant. For field work the LULC

selected and sampling sites were the same as for the Habitat Quality. The numbering of sampling sites

per LULC varied from 1 to 3. In each sampling site, we used pan traps method. For this, colored dishes

(white, yellow and blue) and a trap liquid were placed during early morning and collected at sunset. In

laboratory, all insects captured were sorted for the target family. These individuals were then identified

to the genera.

Afterwards, bibliographic research was done to associate nest requirements of each genus to the

characteristics of each LULC type. Bibliographic research also allowed us to determine the flowering

season typical of the plants normally found on each LULC type, and the flight season and maximum

flight distance for each genus identified (Appendix A).

Note that the field work performed for this model was only used to identify some of the bee genera

present in the study area. No data regarding in which LULC the genera were found or their abundance

was used to run the model. The abundance index resulting from the InVest model was calculated based

on the nest availability on the study area, flight season and flowering seasons.

2.3.3 Carbon storage

Bibliographic data was collect for all the four carbon pools defined in the model, and for each of the

LULC. A special care was taken to use data from studies made in sites with similar ecosystems as the

one we worked in regarding land-use, climate and soil types. Whenever multiple data was available for

the same LULC/pool, the mean value was used as InVest input (See Results table 1.2.4). Regarding the

soil carbon pool the depth from where the soil was collected in most papers was from a depth up to 30

cm, being that only one paper referred a depth of 60 cm.

2.3.4 Nutrients cycling

For the nutrient cycling service, the LULC selected were Riparian galleries, Montado, Irrigated Forage,

Extensive crops (sp: Tricale) and Vegetable crops.

The international available method for evaluating the potentiation for soil decomposition was used as a

proxy of nutrients cycling, the Tea Bag Index (Keuskamp et al. 2013). The Tea Bag Index is determined

through a litter bag experiment, followed by the measurement of mass loss after incubation (decomposed

material). To do this soil was collected from 1 site per type of LULC. Within each site a composite

sample was made collecting the first 30 cm of soil from 5 nearby locations. The collected soil was

divided, being that 12 samples per LULC were prepared, each one with 1 tea bag in it. The bags were

dried in an incubator at 70ºC degrees and weighted before being incubated in the soil placed in pots, at

an 8 cm depth. Tea bags were then removed sequentially from each treatment after the beginning of the

incubation: two weeks, four weeks, eight weeks and twelve weeks. Pots were watered in a regular basis,

ensuring that the total precipitation given during the incubation period was the same as for their original

location. For that the monthly normal averages were retrieved (IPMA 2016a) and used to calculate the

watering.

After collected, the tea bags were dried and then open and the remaining tea was collected from inside

the bags and stored in paper labeled containers. Separating the tea from the bags was done to ensure that

debris (such as small roots that grow outside the tea bags) were removed prior to weighting. The bags

and tea were then weighed. Then the percentage of tea that was not decomposed was calculated

(Equation 2.3.4.1)

15

% 𝑜𝑓 𝑛𝑜𝑡 𝑑𝑒𝑐𝑜𝑚𝑝𝑜𝑠𝑒𝑑 𝑡𝑒𝑎 = (𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑡𝑒𝑎 𝑏𝑎𝑔 𝑤𝑒𝑖𝑔ℎ𝑡 − 𝑏𝑎𝑔 𝑤𝑒𝑖𝑔ℎ𝑡) − (𝑓𝑖𝑛𝑎𝑙 𝑡𝑒𝑎 𝑏𝑎𝑔 𝑤ℎ𝑒𝑖𝑔ℎ𝑡 − 𝑏𝑎𝑔 𝑤𝑒𝑖𝑔ℎ𝑡)

𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑡𝑒𝑎 𝑏𝑎𝑔 𝑤𝑒𝑖𝑔ℎ𝑡 − 𝑏𝑎𝑔 𝑤𝑒𝑖𝑔ℎ𝑡𝑥 100

Equation 2.3.4.1 – Expression to determine the percentage of not decomposed tea

2.4 Data analysis

For the habitat quality and crop pollination services the Shannon Wiener index and the evenness index

were calculated in order to evaluate diversity. After running each one of the ecosystem services modules

within InVest, the habitat scores obtain for some of the LULC were compared using the Mann-Whitney

U test.

Regarding the nutrient cycling service, to evaluate if there were any differences in the decomposition

rate for the different LULC a Kruskall-Wallis H test was performed for each of the measure periods (2,

4, 8 and 12 weeks). In case the test showed differences, a Mann-Whitney U test was performed

comparing the LULC pairwise. All the statistical analysis were performed using the Microsoft Excel

and IBM SPSS software and a confidence interval of 95% (α=0.05) was used for all statistical analysis.

For all the InVest model results, raster calculation was used to compare the historical result to the current

one, resulting on the differences between both for each ecosystem service.

After all results were gathered, the values for each ecosystem service were scaled (0-1) in order to

evaluate their relative importance for each LULC. These relativize values were then represented in a

radar plot, grouping all ecosystem services. To obtain an overall index of the ecosystem services

provided the relative values of the ecosystem services were then summed (assuming that they all value

the same) and then the LULC were ranked according to this and mapped.

16

3 Results

3.1 Mapping of the Land-use Land-cover types (LULC)

The first task of this project was the production of two maps, one corresponding to the LULC that was

present before the area was irrigated (Figure 3.1.1-A) and another with the current LULC (Figure 3.1.1-

B).

Figure 3.1.1 – Distribution of the LULC on the Monte Novo irrigation site. A - before the site was irrigated – historical

LULC map; B- after the site was irrigated (2015) – current LULC map. The areas presented in grey are the ones that were

aggregated in an “other occupations” category due to the small area of occupation.

To compare both maps it is important to understand the alterations in the size of each LULC and which

are the most representative for each time (figure 3.1.2). It could be verified that in the historical map the

LULC that occupied most of the area were the extensive crops, and in the current map a decrease of the

extensive crop area was observed, with an increase of most of the irrigated LULC. Some of the LULC

A

B

17

that were maintained through time, like the Montado and the riparian galleries, maintained a similar area

size.

Figure 3.1.2 - Comparison of the historical areas with the current areas for each LULC. Each LULC is divided in two columns,

being the first referent to the historical map and the second column referent to the current map.

It can be verified that in the historical LULC map the extensive crops occupied the largest area. In the

current LULC map the irrigated crops occupied the largest area. It should be taken under consideration

that in the historical map only a general class of irrigated crops was defined. However, in the current

LULC map, the irrigated crops were composed of 10 different LULC classes (irrigated cereal, irrigated

forage, irrigated crops, vegetable crops, corn, medicinal and aromatic, ornamental, flowers, fruit trees,

dry fruits, oil seeds and generalized irrigated crops). The same happened for the olive groves and

vineyards, since in the historical map they were only composed of extensive plantations and in the

current map they are composed of both extensive and irrigated plantations.

Before the irrigation of the plot, only 4.75% of the area was covered by irrigated agriculture, being that

73,44% was extensive agriculture (where 4.4% are olive groves and 0.53% are vineyards). In the current

map, the irrigated areas occupy 56.32% of the area (irrigated olive groves occupy 12.83% of the area

and irrigated vineyards occupy 1.17%) and the extensive fields are now only 23.25% of the area.

To run the InVest models, for the current map, some areas were excluded, and grouped together in an

“other activities” class. These classes were selected based on the reduced area they occupied on the

study area. This selection also allowed for the simplification of the comparisons between the historical

0

1000

2000

3000

4000

5000

6000

7000

8000

9000Historical scenario

Current scenario

Oil seeds

Dry fruit

Fruit trees

Ornamental flowers

Medicinal and

AromaticCorn

Vegetable crops

Irrigated Forage

Irrigated cereal

Irrigated Vine

Extensive Vine

Irrigated olive

18

and current maps. Being that the LULC data for the past map was not as diverse as the one for the current

map, all the classes present on the past map were used.

3.2 Modeling Ecosystem Services

3.2.1 Habitat quality

A total of 654 individuals of birds from 44 different species were identified during the fieldwork

(Appendix B and figure 3.2.1.1). The maximum number of species and individuals were detected in

Montado, while the minimum values were observed in irrigated corn. The riparian galleries, extensive

agriculture and irrigated olive had a similar number of individuals detected, but the riparian galleries

showed the highest number of species.

Figure 3.2.1.1 - Diversity totals results – Total species of birds identified per LULC and total individuals of birds identified per

LULC in the Monte Novo irrigation site. N= 3 per LULC type, except for irrigated corn: N=2, error bars represent the standard

deviation.

The data collected on the field allowed the calculation of diversity indexes (Table 3.2.1.1 and figure

3.2.1.2). The Shannon–Wiener index for taxonomic diversity result was 2.84, and the Evenness index

result was 0.75.

Table 3.2.1.1 - Summary of the diversity index results for birds per LULC in the Monte Novo irrigation site

Montado Riparian

galleries

Extensive

agriculture

Irrigated

olive

Irrigated

vine

Irrigated

corn

n 3 3 3 3 3 2

Total

species

found

30 24 14 16 8 3

Total

individuals 158 129 127 108 74 58

Shannon

Winner 2.901 2.635 2.068 2.205 1.65 1.402

Evenness 0.76 0.69 0.55 0.58 0.43 0.37

0

50

100

150

200

0

5

10

15

20

25

30

35

Nu

mb

er o

f in

div

idu

als

Nu

mb

er o

f S

pec

ies

LULC

Total species found Total individuals

19

Figure 3.2.1.2 - Diversity indexes results for birds per sampled LULC for the Monte Novo irrigation site

As it is shown in the figures above, both the diversity indexes show higher values in less disturbed

LULC, like the Montado and the Riparian galleries, than in irrigated LULC. They also show that,

concerning the irrigated crops, the most permanent ones, in this case irrigated olive and irrigated vine,

house more species and more individuals than annual crops, in this case irrigated corn.

After all the data collected, we were able to run the InVest models for the Habitat Quality service (figure

3.2.1.3).

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0

0,5

1

1,5

2

2,5

3

3,5

Even

nes

s

Sh

ann

on

Win

ner

LULC

Shannon Winner Evenness

20

Figure 3.2.1.3 - InVest results for habitat quality service. The results for the habitat quality index vary between 0 and 1, being

1 the higher habitat quality and 0 the lowest. A- historical LULC map; B- current LULC map.

The difference between the two maps is shown in figure 3.2.1.4.

A

B

21

Figure 3.2.1.4 – Percentage of variation between the historical LULC map and the current LULC map that resulted from the

InVest models. The increase in habitat quality score is shown in a scale of green, while the decrease in the habitat quality score

is shown on a scale of red.

The comparison of the historical and current maps showed a slight increase in habitat quality score for

part of the area, while the other part maintained or decreased the habitat quality score. These differences

are summarized per LULC in figure 3.2.1.5.

Figure 3.2.1.5 – Difference in the average habitat quality score for each LULC for the Monte Novo irrigation site. The areas

that did not exist in historical map but occur in the current one are being compared to their equivalents – the “irrigated olive”

is being compared to the “extensive olive” in the historical map, the “irrigated vine” is being compared with the “extensive

vine” in the historical map and the irrigated crops (“irrigated cereal”, “irrigated forage”, “vegetable crops” and “corn” are

being compared to the “irrigated crops” in the historical map)

-1 -0,5 0 0,5 1

Irrigated crops

Extensive crops

Montado

Montado with crops

Pine and Eucaliptus Plantations

Urban Areas

Riparian galleries

Extensive olive

Irrigated Olive

Extensive Vine

Irrigated Vine

Irrigated Cereal

Irrigated Forage

Vegetable crops

Corn

Habitat score

LU

LC

22

The LULC that are present in both the historical and the current LULC maps were compared using the

Mann Whitney U test (table 3.2.1.2).

Table 3.2.1.2 - Mann Whitney U to compare the habitat quality score for LULC that occur in both historical and current

LULC maps. N = Number of polygons of the LULC in the historical map + number of polygons of the LULC in the current

map.

Montado Irrigated

crops

Extensive

crops

Riparian

Galleries

Pine and

Eucalyptus

Plantations

n 40+31 17+18 66+149 34+21 13+6

Mean for the historical

map 0.9999 0.6999 0.3498 0.9998 0.5999

Mean for the current

map 0.9999 0.3499 0.6975 0.9997 0.5999

U 488.000 0.000 53.000 131.000 34.000

p-value 0.102 0.000 0.000 0.000 0.635

Analyzing the results from the Mann Whitney U test it is confirmed that the differences between the

historical and current maps are significant for the Extensive Crops, Irrigates Crops and Riparian galleries

LULC. Consider that the Riparian galleries show a significant difference, yet the difference is very

small. For the Montado and Pine and Eucalyptus Plantations LULC no significant differences were

found.

3.2.2 Crop pollination

A total of 41 bee specimens, from seven different genera (Andrena, Dasypoda, Eucera, Halictus,

Heriades, Lasioglossum, Panurgus), were found in the samples collected from the study site (figure

3.2.2.1). The higher number of bee specimens was found on Irrigated vine, while the lowest was found

on the Riparian galleries and Extensive agriculture. The three irrigated LULC had a similar number of

genera found – three genera for the Irrigated olive and 4 genera for the Irrigated vine and Irrigated corn.

Figure 3.2.2.1 - Diversity totals results – Total genera of pollinators identified per LULC and total individuals of pollinators

identified per LULC in the Monte Novo irrigation site

02468101214161820

0

1

2

3

4

5

Nu

mb

er o

f in

div

idu

als

Nu

mb

er o

f sp

ecie

s

LULC

Total species found Total individuals found

23

The data collected on the field allowed the calculation of diversity indexes (Table 3.2.2.1 and figure

3.2.2.2). The Shannon Wiener index for taxonomic diversity result was 1.26, and the Evenness index

result was 0.65. When comparing the sampled LULC, the highest value for both indexes is observed for

the Irrigated Vine, followed by the Irrigated Corn and Irrigated Olive, while the lowest values are

observed for the Riparian Galleries and Extensive agriculture.

Table 3.2.2.1 - Summary of diversity totals and diversity indexes for pollinators for the sampled LULC.

Montado Riparian

galleries

Extensive

agriculture

Irrigated

olive

Irrigated

vine

Irrigated

corn

Total

species

found

2 1 1 3 4 4

Total

individuals 2 1 1 5 19 13

Shannon

Winner 1.39 0 0 3.73 8.38 6.52

Evenness 0.712414 0 0 1.92 4.30 3.35

Figure 3.2.2.2 – Diversity indexes results for pollinators per sampled LULC in the Monte Novo irrigation site

After the identification, the necessary information regarding each genus (nest preferences, flight season

and flight distance) was collected (Appendix A) in order to run the InVest model (Figure 3.2.2.3).

0

1

2

3

4

5

0123456789

Even

nes

s

Shan

non W

inner

LULC

Shannon Winner Evenness

24

Figure 3.2.2.3 - InVest Crop Pollination model results for the Monte Novo irrigation site. The values shown refer to the

pollinator abundance index, which was calculated for each cell of the map when the model was run, and varies from 0 to 1. A-

historical LULC map; B- current LULC map.

The comparison of the two maps shows a small variation in the pollinator abundance index, being that

the historical map has an average of 0.01833 pollinator abundance index and the current map has an

index of 0.02762. Yet, some areas showed a decrease in the pollinator abundance index (figure 3.2.2.4).

A

B

25

Figure 3.2.2.4 - Percentage of variation of the pollinator abundance index between the historical LULC map and the current

LULC map. The increase in pollinator abundance index is shown in a scale of green, while the decrease in the pollinator

abundance index is shown on a scale of red.

The calculation of the differences between the results for the historical map and the current map allow

to understand where the major alterations occurred. Most of the study area maintained or decreased the

pollinator abundance index. The comparison of the average pollinator abundance index per LULC is

shown in figure 3.2.2.5.

Figure 3.2.2.5 - Comparison of the average pollinator abundance index per LULC for the Monte Novo irrigation site. The areas

that did not exist in historical map but occur in the current one are being compared to their equivalents – the “irrigated olive”

is being compared to the “extensive olive” in the historical map, the “irrigated vine” is being compared with the “extensive

vine” in the historical map and the irrigated crops (“irrigated cereal”, “irrigated forage”, “vegetable crops” and “corn” are being

compared to the “irrigated crops” in the historical map)

-0,01 -0,005 0 0,005 0,01

Irrigated crops

Extensie crops

Montado

Montado with crops

Pine and Eucaliptus Plantations

Urban Areas

Riparian galleries

Extensive olive

Irrigated Olive

Extensive Vine

Irrigated Vine

Irrigated Cereal

Irrigated Forage

Vegetable crops

Corn

Pollinator abundance index

LU

LC

26

The comparison of the pollinator abundance index per LULC between the two maps shows a decrease

of the index in the historical map for most of the LULC that occur in the two maps, like was seen in

figure 3.2.2.5. This comparison shows that the major decrease occurred in vineyards, being that most of

the LULCs present a small variation of the pollination abundance index.

The LULC that are present in both the historical and the current LULC maps were compared using the

Mann Whitney U test (table 3.2.2.2).

Table 3.2.2.2 - Mann Whitney U to compare the habitat quality score for LULC that occur in both historical and current

LULC type. N = Number of polygons of the LULC in the historical map + number of polygons of the LULC in the current

map.

Montado Irrigated

crops

Extensive

crops

Riparian

galleries

Pine and

Eucalyptus

Plantations

N 40+31 17+18 66+149 34+21 13+6

Mean for the historical

map 0.0059 0.0005 0.0005 0.0031 0.0078

Mean for the current

map 0.0045 0.0004 0.0004 0.0024 0.0058

U 7.000 0.000 0.000 0.000 0.000

p-value 0.000 0.000 0.000 0.000 0.000

Analyzing the results from the Mann Whitney U test it is confirmed that all the LULC analyzed show

significant differences between the historical and the current LULC maps.

3.2.3 Carbon storage

The bibliography search resulted in the characterization of the carbon stock of each compartment and

LULC type, which are summarized in table 3.2.3.1 as average values whenever multiple values were

found.

27

Table 3.2.3.1 - Average value for Carbon pools (Mg/ha) (Complete table – Appendix C)

LULC Above the ground pool Below the ground

pool Soil pool

Dead

organic

matter

Montado 25.786 2.940 60.250 2.000

Montado with crops 10.255 2.940 57.000 2.000

Extensive crops 0.310 0.310 38.000 0.000

Irrigated crops 0.310 0.310 34.833 0.000

Extensive olive 7.850 1.150 33.996 0.000

Pine and Eucalyptus

Plantations 109.395 1.460 68.400 2.000

Extensive Vine 2.81 1.790 42.080 0.000

Riparian galleries 184.067 52.680 25.619 43.910

Irrigated Olive 9.730 1.360 27.016 0.000

Corn 14.275 2.020 31.750 0.000

Irrigated Forage 0.530 0.940 49.000 0.000

Vegetable crops 0.310 0.310 25.450 0.000

Irrigated Vine 2.590 9.980 48.700 0.000

Irrigated Cereal 4.060 0.580 6.5067 0.000

After the collection of these data the model was run and the following maps were obtained (figure

3.2.3.1):

28

Figure 3.2.3.1 - InVest results for Carbon storage service (Mg/pixel). A pixel is 900m2. A- historical LULC map; B- current

LULC map.

The historical LULC map showed a total carbon storage of 654 250 Mg and the current LULC map

showed a total of 589 696 Mg of carbon stored. The overall difference was 64 554 Mg which corresponds

to a variation of -9.9% from the past. To compare the two maps obtained the variation of total carbon

for each LULC was calculated and compared (figure 3.2.3.2)

A

B

29

Figure 3.2.3.2 – Percentage of variation in the carbon storage between the historical LULC map and the current LULC map.

The increase in carbon storage index is shown in a scale of green, while the decrease in the carbon storage is shown on a scale

of red.

The comparison map shows an increase on the amount of carbon stored for most of the area, but some

LULC shoed a high reduction. The results were also compared per LULC (figure 3.2.3.3).

Figure 3.2.3.3 - Difference of total carbon stored (Gg) per LULC. The areas that did not exist in historical map but occur in the

current one are being compared to their equivalents – the “irrigated olive” is being compared to the “extensive olive” in the

historical map, the “irrigated vine” is being compared with the “extensive vine” in the historical map and the irrigated crops

(“irrigated cereal”, “irrigated forage”, “vegetable crops” and “corn” are being compared to the “irrigated crops” in the historical

map)

As can be seen in figure 3.2.3.3, there was a negative variation in the total carbon stored in the area.

Consider that not all LULC are represented in figure 3.2.3.3, being that some less representative LULC

-300 -200 -100 0 100 200 300

Corn

Vegetable crops

Irrigated Forage

Irrigated Cereal

Irrigated Vine

Extensive Vine

Irrigated Olive

Extensive olive

Riparian galleries

Urban Areas

Pine and Eucaliptus Plantations

Montado with crops

Montado

Extensie crops

Irrigated crops

Carbon (Gg)

LU

LC

30

were excluded. Since extensive crops were the predominant LULC before irrigation and in the current

map were replace by irrigated crops, this is the LULC with the major decrease in total carbon stored.

Irrigated areas sum a total increase of 100.4 Gg of carbon variation, while extensive crops show a

decrease of 248.6 Gg of carbon. Values per area for each LULC are summarized in figure 3.2.3.4.

Figure 3.2.3.4 - Carbon per area for each LULC in the Monte Novo irrigation site. Based on the results from the InVest carbon

storage model

3.2.4 Nutrient Cycling

For this model the Tea Bag index was used. The results obtain are presented in figure 3.2.4.1.

Figure 3.2.4.1 - Tea bag index results. Variation of the average percentage weigh of the tea bags per LULC during the

sampling period.

There were no major differences between the LULC of the disturbance gradient regarding the

decomposition rate of the green tea.

The Kruskall Wallis H test results for comparison between LULC are summarized on table 3.2.4.1. The

test did not show significant differences between LULC, except for the data from the eighth week. The

data from the eight week was compared between LULCs using the Mann Whitney U test to evaluate

which LULC was different.

0

50

100

150

200

250

300

350C

arb

on

(M

g/h

a)

LULC

0

20

40

60

80

100

0 2 4 6 8 10 12

Time (Weeks)

Riparian galleries Montado Forage

Extensive crops Irrigated crops

31

Table 3.2.4.1 - Kruskal Wallis results for the comparison of the decomposition rate from the TBI protocol for different LULC

in the Monte Novo irrigation site.

LULC Riparian

Galleries Montado Forage

Extensive

Crops

Irrigated

Crops

Riparian

Galleries 1.000 0.137 1.000 0.047

Montado 1.000 1.000 1.000

Forage 1.000 1.000

Extensive

Crops 0.446

Irrigated

Crops

Total N= 15 H= 10.8333 p=0.0285

None of the comparisons showed significant differences, except the comparison between riparian

galleries and the irrigated crops that appeared to be only marginally significant (p= 0.047). For this,

these results will not be considered in the discussion.

3.3 Ecosystem services overall comparison and potential trade offs

After all the models were completed, the results per LULC were compared considering the changes that

occurred from the historical to the present map, among the different ecosystem services. In general, the

ecosystem service that decreased the most LULC was the crop pollination, which was observed for all

LULC types except irrigated cereals and vegetables crops. Habitat quality variations were positive for

some LUCL while negative for others, while carbon sequestration did not change in several LULC.

The distribution of the LULC capacity to deliver the studied ecosystem services can be observed on

figure 3.3.1 and 3.3.2. Here it is shown that irrigated LULC are among the ones with lower capacity to

deliver these ecosystem services, while the Pine and Eucalyptus plantations and the Montado are the

two with higher capacity.

Figure 3.3.1 - Summary of the ecosystem service delivery distribution in the Monte Novo irrigation site. The map represents

the sum of the provision of each ecosystem service studied for the different LULC in the area. The capacity of delivery ranges

from red (lowest delivery capacity) to green (highest delivery capacity).

32

Figure 3.3.2 – Ecosystem service relative importance per LULC in the Monte Novo irrigation site for the current map. Each

ecosystem service was scaled from 0 to 100 and then calculated for each LULC, therefore LULC with higher values for the

ecosystem services than the others are more important in the provision of that ecosystem service.

0

50

100

Irrigated crops

0

50

100

Extensive crops

0

50

100

Montado

0

50

100

Montado with

crops

0

50

100

Pine and

eucalyptus

plantations

0

50

100

Urban areas

0

50

100

Riparian Galleries

0

50

100

Extensive Olive

0

50

100

Irrigated olive

0

50

100

Extensive Vine

0

50

100

Irrigated Vine

0

50

100

Irrigated Cereal

0

50

100

Irrigated forage

0

50

100

Vegetable crops

0

50

100

Corn

33

4 Discussion

The present work allowed us to quantify and map how agriculture intensification by irrigation altered

ecosystem services delivery in a dryland area. It also allowed us to suggest ways to optimize those

ecosystem services in the future, taking in consideration the way ecosystems and their biodiversity

provide ecosystem services and how agriculture threatens them.

The main difference between the two LULC maps (historical and current) were the areas covered by

extensive agriculture and irrigated agriculture, being that the irrigated areas increased from only 4.75%

in the historical map to 56.32% in the current map (an additional 7050 ha of irrigated land in the study

area). These alterations were expected, since one of the main goals of the construction of the Alqueva

dam was the irrigation of the area.

4.1 Modeling Ecosystem Services

4.1.1 Habitat quality

The conversion of land from natural complex systems to simplified agricultural ecosystems and the

increasing exploitation of resources are major causes of the high rate of farmland biodiversity loss

(Kleijn et al. 2009). But proposing biodiversity-friendly land use policies on a large scale requires

forecasting future land use patterns and these impacts as accurately as possible (Chiron et al. 2013).

The results of the diversity indexes show a decrease in value according to a disturbance gradient

(Montado > Riparian galleries > Extensive agriculture > Irrigated olive > Irrigated vine > Irrigated corn).

This variation was expected since it was already studied that low intensity farming system are important

to nature conservation and protection while more intensive systems can cause major environmental

problems in agricultural and surrounding non-agricultural ecosystems, since they result in an increased

pressure on biodiversity (Reidsma et al. 2006).

The obtained results from the InVest Habitat Quality model show a decrease in the habitat quality index

for most of the area. The extensive agriculture areas maintain a higher score than the irrigated areas, but

in the current LULC map the irrigated area occupy a larger portion of the landscape which leads to

global decrease in habitat quality. These results can be explained by the known effect of agriculture

intensification on birds. In fact, the bird carrying capacity of conventionally farmed areas (using

fertilizers and herbicides) is only 37-51% of the carrying capacity of farmed land where the use of

fertilizers and pesticides is minimized. (Mclaughlin & Mineau 1995a). Agriculture has many other

impacts on birds, such as: 1) change in agricultural habitats as a result of agricultural development and

intensification: this includes the specialization of agriculture (monocultures), and the loss of mixed

farming through conversion of arable to pasture and vice versa; intensification of arable production,

with increased use of pesticide and fertilizers, with impacts on agricultural habitats and food chains, and

changes in cropping patterns; loss of unfarmed feature such as hedgerows, woodlands and ponds, loss

of traditional agricultural habitats such as hay meadows and orchards through conversion to more

intensive systems; 2) Impacts of agriculture on other nearby habitats like pollution (e.g. eutrophication

of watercourses or spreading of pesticides to nearby habitats) (Heath & Rayment 2003). A study from

Benton et al. (2002) also show a lower bird occurrence in areas with irrigated crops, and where the use

of pesticides is more likely. The use of pesticides practice leads to a decrease in the number of

arthropods, and therefore, some bird groups will have restrictions in their diet in these areas. Recent

studies suggested that reduced food supplies may reduce survival (Benton et al. 2002), suggesting that

the use of pesticides may be linked to the reduced number of bird species found, which supports our

results, since a lower habitat quality score was found for LULC that are more likely to have pesticides

being used in.

34

Another study by Chiron et al. (2013) verified how a change in crop cover composition or area could

affect bird species. Crop composition would mostly affect specialist species that depend on a particular

crop type, yet it is assumed that changes to the global arable area would affect all crop covers and species

equally, meaning that all species present can be affected by changes in the LULC composition. The

same study also verified that, globally, the relative abundance of farmland bird species was positively

correlated with cereals and negatively correlated with grain and maize covers. This corroborates the

results obtain in the present study, since extensive areas show a higher habitat score that the irrigated

ones (like corn maize plots).

The results also show a higher habitat quality score in areas like riparian galleries. Riparian vegetation

that is structurally complex and floristically diverse appears to be important for many bird species, as

these areas provide food, nesting places and cover from predators (Luther et al. 2008). These natural

communities may promote the connectivity between other habitat patches, like the Montado

(Almeida et al. 2015).

The present habitat quality service model is based only in three biodiversity threats: presence of urban

areas, food availability and presence of pesticides, while other threats could be considered. However,

the selected threats are representative of others not accounted directly on the model, which is positive

since the same threat is not being counted more than one time. For example, the presence of urban areas

also accounts for the introduction of exotic species, which is promoted in these areas

(Chace & Walsh 2004).

It should also be considered the limitations of the InVest model. In the Invest Habitat Quality model all

the threats on the landscape are additive, although there is evidence that, in some cases, the collective

impact of multiple threats is much greater than the sum of individual threat levels would suggest. This

may have led to an overestimation of the habitat quality score in the Monte Novo irrigation site. Also,

because the chosen landscape is nested within a larger landscape, it is important to recognize that a

landscape has an artificial boundary where the habitat threats immediately outside of the study boundary

have been clipped. Consequently, threat intensity will always be less on the edges of the studied

landscape, which may justify that the some of the areas with the highest habitat quality score are situated

in the edges of the maps resulting from the InVest habitat quality model (Tallis et al. 2011). It should

be considered that are no evidences that the surrounding LULC have changed, since the areas are not

irrigated, for that even though the values obtain for the Habitat Quality service might be slightly under

or overestimated, the comparison between the historical and current LULC maps should be correct.

4.1.2 Crop pollination

The diversity indexes obtained for pollinators show a higher diversity on the irrigated areas rather than

the Montado and Riparian galleries, being that the higher number of pollinator specimens was collected

on the corn fields. This may be related with the used method, since the exposure of the plates in the area

may be related with the number of animals that they attracted. Since the irrigated areas are a more open

space than the Montado, Riparian galleries and extensive agriculture, the plates were more attractive in

the irrigated areas, making this a possible reason for the higher number of specimens collected in the

irrigated areas.

The specimens collected in the Monte Novo irrigation site did allow the identification of the genera

present. In animals living in complex landscapes, population size, in terms of offspring production, is

affected by the characteristics of the habitat where its nest is located (Williams & Kremen 2007).

Accordingly, the quality of the matrix, surrounding the remains of natural habitats, has a strong effect

on pollinator movements (Baños-Picón et al. 2013). A study by Baños-Picón et al. (2013) found that

crop type was the factor that correlated most with the total number of brood cells and emergent progeny,

35

while diversity measures at bee community level were closely related with farming intensity and there

was no direct effect of crop type.

Our results show a decrease in the pollinator abundance index for most of the area, being that irrigated

plots show a higher decrease than the other LULC. This might be related with soil disturbance that affect

the nesting places for most of the genera found in the area. It should also be taken in account that the

model returns the pollinator abundance index that can be found in each grid cell of the map, and not the

abundance of pollinators that each grid cell can support. This justifies why some LULCs where, for

example, flower resources are not available, have a high pollinator abundance index. This only means

that LULC is within flight distance for the pollinators that find food in the surrounding areas. This is the

case of Pine and Eucalyptus plantations, that although do not provide flower resources for pollinators,

might provide some nesting areas. Also this LULC is found in small patches on the landscape, which

makes it of easy access for pollinators coming from the surrounding LULCs like the Montado.

The results showed that it is possible to intercalate different LULC, particularly the ones that offer

nesting sites or flower resources (e. g. Montado) with the ones that do not offer these requirements (e.

g. irrigated crops), provided that they are within flying distance from each other. This shows that the

presence of natural habitats can ensure the crop pollination service.

In future studies, the sampling method should be revised (e.g. using nets instead of pan traps) and more

pollinator groups could be analyzed. A longer sampling period (e.g. different seasons) could also be

helpful. It would also be interesting to study the effect of other pollinators, like butterflies and birds.

Since the ecological data regarding each genus found in the area was only based on bibliographic

research, the actual pollinator preferences in the Monte Novo irrigation site might not be accurately

shown in the InVest results. To enhance these results, further work in the area should be carried, in order

to access in which LULC each genus can be found and estimate the actual pollination potential in each

LULC. Also, some limitations inherent to the model should be considered. The model does not include

the dynamics of bee populations over time, and therefore cannot evaluate if these populations are

sustainable given the current landscape. The model also does not account for fine-scale features in the

landscape that are likely to influence pollinators, that in the case of the present study may lead to an

underestimation of the pollinator abundance index in the Monte Novo irrigation site since structures like

hedgerows that can be used as nesting and foraging places for pollinators are not account in the InVest

crop pollination model because of their small size in the landscape (Tallis et al. 2011).

4.1.3 Carbon storage

The results show that in the current situation the area stores less carbon than previously. Additionally,

the types of LULC that store that carbon vary between the two times. In the historical LULC map, the

majority of the carbon is stored in the Riparian areas and in the extensive crops areas. This is probably

due to the large area that the extensive crop agriculture occupied in the historical LULC map. In the

current LULC map, the carbon continues to be mainly stored in the riparian areas, since these areas

suffer no alteration in the maps, so the carbon pools will be equal. Riparian areas may store large

quantities of carbon due to their relatively high rates of productivity and/or the saturated conditions that

can favor the storage of belowground carbon (Giese et al. 2003). It should be taken in consideration that

these areas were possibly more degraded than what was accounted by the model, due to the more

intensive agriculture performed nowadays around riparian areas. Agriculture can have tremendous

impact on the riparian zone. Streams are channelized to improve drainage, which can have the

unforeseen consequences of increasing sediment and chemical loading (Ryan et al. 2003). Therefore,

their capacity to store carbon could be different that the one we predicted using bibliographic values for

the Monte Novo irrigation site riparian galleries, and this should be verified in future works by field

36

sampling. Meanwhile, no differences were found for the nutrient cycling service between LULC (see

Section 4.2.4).

Also, in the current map, the carbon is mainly stored in areas of permanent irrigated crops, like the

irrigated olive groves and irrigated vine yards. This justifies the increase of carbon in these land uses,

since the amount of carbon stored above ground increased due to the permanent presence of trees.

The total carbon stored has decreased in the current LULC map. If the carbon variation map (figure

3.2.3.3) is analyzed carefully, it can be seen that some areas had increased their amount of carbon stored,

but that this increment does not make up for the loses that some of the land uses suffered. For example,

the alteration from extensive crops to permanent irrigated crops has slightly increased the amount of

carbon, but the change from extensive agriculture to non-permanent irrigated crops like corn or

vegetable crops has led to a great decrease in the carbon stored in those land uses.

Condron et al. (2014) reject the hypothesis that increased primary production resulting from irrigation

of a dry-land pastoral farming system results in increased SOC (soil organic carbon), contradicting the

predictions of Soussana et al. (2004) model, which predict that net annual increases in SOC would be

directly related to pasture production. The present study shows a decrease in the overall amount of

carbon stored in the area, which also contradicts the predictions of Soussana et al. (2004). However,

both studies only focused on the SOC (part of the soil carbon pool) while the present study comprises

four carbon pools (aboveground, belowground, soil and dead matter).

The construction of the carbon pool database presented some challenges, mainly because there is not

much information available for the study area. Many of the references used were from areas that could

relate to the study area, for example, data from Mediterranean climate areas. In future studies in the area,

it could be interesting to measure the amount of carbon in the site instead of only using bibliographic

data, and compare the results. This would lead to more precise results for the area, helping also to create

a more complete database for this type of projects in Mediterranean areas. Regarding the riparian areas,

it would be interesting to study similar riparian galleries in the area that have not been affected by the

land use changes that the galleries studied were and compare the results, like the work done by

Giese et al. (2003). There are also some limitations for this model, that are related with its simplification.

The model only accounts for the estimation of carbon stored in each pool for each LULC, being that it

does not account for possible variations of carbon storage within the same LULC, that can be impacted

by drivers like climate change (Tallis et al. 2011).

4.1.4 Nutrient Cycling

As observed in the results, no major differences were found for the decomposition rate for the different

land uses on the disturbance gradient. This may be due to the changes in land cover being not old enough

to cause a major alteration in soil properties (the site had been irrigated for 7 years by the time the soil

was collected). Another hypothesis is that the LULC changes that occurred were not intense enough to

cause alterations in the decomposition rates.

Arroita et al. (2013) preformed a similar study in Spain to evaluate the decomposition capacity of the

soils. As they expected, the irrigated soils promoted breakdown in soils, as water availability is strongly

limiting in semi-arid regions. Organic matter breakdown depends on soil moisture, temperature and

nutrient contents, and tends to be promoted in agricultural soils, up to the point of reducing their contents

on organic matter (Arroita et al. 2013). However, in the present study, no differences on decomposition

rates were found on the disturbance gradient analyzed. This was due to the fact that we were interested

in the changes that might have been caused by LULC changes over the long-term rather than the effect

of weather or irrigation. For that we place all soil samples collect on the field under the same conditions

of humidity and temperature in the greenhouse. This way we measure the potential decomposition rate,

37

and remove the potential effect of weather and irrigation during the experiment. The few studies that

have used plant litter to test decomposition show that the combination of temperature and moisture can

explain 50-70% of the variation in decomposition (Keuskamp et al. 2013). And, in the study by

Arroita et al. (2013) the samples were subject to the field conditions, where the amount received in each

sample site is not equal. They found that the most important factor accelerating breakdown of organic

matter seems to be water availability (Arroita et al. 2013), especially in semi-arid regions, as moisture

promotes microbial activity, thus enhancing decomposition (Arroita et al. 2013), which might explain

why no significant differences were found when a similar experiment was conducted in a greenhouse

where all the samples were subjected to the same watering conditions.

It would be important to perform the experiment with more replicates per land cover, so that we would

have more security in the results. Regarding the TBI protocol, since the experiment was performed using

the green tea, and not the rooibos one, the results are coherent with the expected ones, since the green

tea decomposes quickly at first and then tends to steady the decomposition rate. The protocol was used

to test fast decomposition and with the same weather conditions, which did not account for possible

variations between LULC (e. g. Riparian galleries are more likely to have higher levels of humidity).

While this method cannot substitute the thoroughness and precision of conventional litter bag methods,

TBI considerably reduces the effort necessary to fingerprint local decomposition

(Keuskamp et al. 2013).

4.2 Limitations

Besides some limitations already discussed, data sampling in the field might have been influenced by

the climate observed during the year when the work was done. The month of May (when the fieldwork

was performed) and the ones that preceded were considered not normal regarding temperature and

precipitation, being that some were warmer than the normal and others were cooler, and all of them had

higher precipitation levels than is usual (IPMAa 2016). This should be taken into consideration, because

it most certainly affects the data collected during the field work, for example the presence of flying

insects, like bees, might have been affected by the high precipitation, and the lack of insects might have

impacted the presence of insectivorous birds during the sampling period (IPMA 2016b; IPMA 2016c).

This issue justifies how important it would be to perform the field work for each of the ecosystem

services in more than a year. This would allow to have an average data for multiple climatic conditions,

in which the differences in the environmental data for a specific year would not have much influence,

allowing to focus only on the differences between the LULC. However, all the land uses in the area

where under the same temperature and precipitation conditions, so the differences between them are

likely to hold in years with different climate.

Also regarding the field work, the differences in the size of the sampling patches may influence the

results. Even though the sampling was performed in the largest patches for each land use analyzed the

size of the patches was not uniform. Besides, the fragmentation effect within the patches was not

considered for the field work, being that the biodiversity in some plots might have been overestimated,

since larger patches of habitat support more species than small patches (Bartlett et al. 2016). In the

future, this limitation can be overcome with a more detailed LULC map being constructed before further

field sampling for the models. Also, fragmentation effect was not taken into consideration. In fact,

smaller patches could hold less biodiversity than larger ones due to edge effects and carrying capacity

(Wood 2000). Here, all LULC of the same type were considered to potentially provide the same amount

of ecosystem service (per area). To do this we would need to stratify the field sampling to the patch size,

in order to evaluate the influence of patch size, which was not performed in this work.

38

Patch size might also be really important for bird territory. Even though this variable was not considered

in the InVest habitat quality model, bird territory size should be considered when designing LULC

patches, for that some patches might not be large enough to ensure they have capacity to sustain some

bird species.

4.3 Ecosystem services overall comparison and potential trade offs

The final results show that the LULC where all the ecosystem services studied are the highest are the

Riparian galleries, while the ones where the ecosystem services are the lowest are the urban and irrigated

LULC.

Kennedy et al. (2016) found that a landscape level planning for mitigation of agricultural impact can

bring more benefits than a farm-level planning. In their work they show that the protection and/or

restoration of part of the natural habitats imposes relatively small costs to a commercial producer but

generates substantial conservation benefits by supporting greater biodiversity, storing additional tons of

carbon, and marginally improving surface water quality. Since in the Monte Novo irrigation site the

LULC with higher capacity of providing all the ecosystem services under analyses are the most natural

ones, it would also be important to preserve these areas in order to maintain the ecosystem services flow

in the area or create new patches of important LULC like the Montado or Riparian Galleries which can

potentially supply important ecosystem services to nearby agriculture LULC types.

4.4 How to optimize ecosystem services

Landscapes can have several configurations. Likewise, there are some configurations that can be

promoted to enhance of both agricultural productivity and biodiversity conservation. Land sharing and

land sparing are two of these configurations. Land sharing involves integrating biodiversity conservation

and agriculture on the same land, using wildlife-friendly farming methods, while land sparing consists

of separating land for conservation from land for crops, with high-yield farming facilitating the

protection of remaining natural habitats from agricultural expansion (Phalan et al. 2011). The Monte

Novo irrigation site has a land sparing configuration, therefore, natural areas such as the Montado,

riparian galleries and less intensive agricultural sites can be used to promote ecosystem services in the

area. Creating natural corridors that promote connectivity can help biodiversity, promoting that way the

flow of ecosystem services. That said, a land sparing strategy can be optimized to enhance both

agriculture and ecosystem services flow. In future work, projections using different configuration

scenarios could be run to evaluate how agriculture and ecosystem services can be further optimized.

Enhancing functional biodiversity in agroecosystems is a key ecological strategy to bring sustainability

to production (Altieri 1999). Management tools for other agricultural practices, such as intercropping

and rotation to reduce pesticide use, assessment of the severity of pest species competition prior to

pesticide use, fertilizer application linked to no-till methods or nutrient budgets, and re-seeding

improved pastures with native vegetation, may successfully benefit agriculture and preserve the quality

of habitat for wildlife (Mclaughlin & Mineau 1995a). A more diverse copping pattern may provide multi

brooded ground-breeding birds with more suitable nesting sites throughout the breeding season

(Wilson et al. 1997).

The “rest-rotation” grazing system has been found useful for ameliorating some of the adverse impacts

of season-long grazing on wild birds primarily because certain areas if pasture are left undisturbed at

least part of the time (Mclaughlin & Mineau 1995a).

Hedgerows and similar habitat elements improve the structural connectivity within landscapes

(Ascensão et al. 2012). Hedgerows and field margins are known to be important refuge habitats for

many small-sized vertebrate and invertebrate species, including ants, butterflies, bees, beetles, birds and

39

small mammals (Ascensão et al. 2012). These structures could be place around the crop fields in the

study area in order to promote crop pollination.

Loss of pollinators can be mitigated through a number of interventions including on-farm management

and protection of semi-natural habitats in the wider landscape (Potts et al. 2015). Some measures include

allowing the natural occurrence of wild plants within crop fields ensures that resources for flower

visitors are restored to some extent, without the loss of arable land. Such practice not only helps sustain

pollinator diversity but also benefits production in areas which are isolated from natural habitat

(Carvalheiro et al. 2011). Regarding agricultural fields, measures like support diversified farming

systems, promoting no-till agriculture, promote Integrated Pest Management (IPM), supporting organic

farming systems and restoring natural habitats can help improve pollinator abundance and richness

(IPBES 2016). For these, in the study area, the implementation of Montado or permanent crop patches

surrounding the annual crops could be a helpful measure.

Preserving the quantity and quality of soils is one of the main objectives of current efforts to make

agriculture more “sustainable” (Mclaughlin & Mineau 1995a). The soil carbon stock capacity depends

not only on abiotic factors such as the mineralogical composition and climate, but also on soil use and

management (Zinn et al. 2007). Soil erosion by water and land use change has a significant impact on

the large pool of soil organic carbon (Van Oost et al. 2005). Because of this many authors have proposed

soil management techniques for improving soil properties and diminishing atmospheric CO2

concentrations such as restrictions on agriculture (Corral-Fernández et al. 2013).

Tillage alters many aspects of the soil’s physical environment including soil water, aeration,

compaction, porosity and temperature (Mclaughlin & Mineau 1995a). It also renders soil susceptible to

wind and water erosion which can affect the level of organic matter and nitrogen in the top layer of soils

(Mclaughlin & Mineau 1995a). Conservation agriculture, which results in less erosion, more water

infiltration, reduced runoff and reduced fuel costs, can help promoting soil quality

(Mclaughlin & Mineau 1995a). Conservation agriculture is characterized by FAO as an agriculture that

maintains a permanent or semi-permanent organic soil cover (e. g. a growing crop or dead mulch), which

function is to protect the soil physically from the sun, rain and wind and to feed soil biota. The soil

micro-organisms and soil fauna take over the tillage function and soil nutrient balancing. Mechanical

tillage disturbs this process. Therefore, zero or minimum tillage and direct seeding are important

elements of conservation agriculture. A varied crop rotation is also important to avoid diseased and pest

problems (Hobbs et al. 2008).

In Europe, the EU Common Agricultural Policy is a key driving force for agricultural change and

environmental aspects of farmland management, promoting, for example, agro-environmental schemes

that conserve high nature value farmland sites (e. g. the Montado in Portugal) (Hoogeveen et al. 2004).

In the Monte Novo irrigation site, one way to promote some of these measures would be to use the

riparian galleries and the Montado areas to connect and surround the most intensive agriculture areas,

allowing the conservation and protection of biodiversity and optimizing a land sparing strategy. Also

promoting the existence of hedgerows and the occurrence of wild flowers within crop fields can optimize

pollination. Besides, reducing the use of pesticides by using an Integrated Pest Management strategy

can improve the abundance of pollinators as well. Some measures from conservation agriculture, like

using cover crops, can help increase soil organic carbon storage, promoting at the same time soil fertility

and less erosion.

40

5 Final Remarks

This study showed that the transition from extensive to intensive agriculture through irrigation has

promoted alterations in the ecosystem services. More importantly habitat quality has decreased in most

of the study area. A decrease the pollinator abundance in the area was also found. These alterations were

expected for both ecosystem services since agricultural expansion and intensification are major drives

for biodiversity and habitat loss, and thus also of decrease in ecosystem services.

Carbon storage suffered alterations due to irrigation, probably due to the presence of a higher number

of permanent crops like olive groves and vineyards in the current scenario. Still the amount of carbon

stored decreased from the historical LULC map to the current one.

No differences were found for the disturbance gradient for the nutrient cycling service, this may be due

to the short time that has passed since the irrigation by the Alqueva dam started in the area.

Considering all tested ecosystem services, the Riparian galleries showed to provide the most ecosystem

services, being that the Montado was also considered of great importance in the area. The promotion of

corridors or patches of these LULC could be very helpful to promote ecosystem services in the Monte

Novo irrigation site. This is especially important to provide critical ecosystem services such as

pollination to nearby agriculture areas, which have limited supply of those services.

The construction of a spatially explicit model using software like InVest is a great tool to analyze and

evaluate the changes in an area that suffered land use changes like the irrigated area studies in this

project. The trade-off analysis allows to evaluate the gains and losses that occurred due to the LULC

changes. Therefore, measures to optimize the positive aspects can be taken.

Projects like the present one are of great importance to understand the impacts of agricultural expansion

in drylands and allow to conceive which are the most important alterations that should be considered

when taking measures to promote a more sustainable agriculture.

Future work regarding other ecosystem services, like water quality, and with improved methods for the

ecosystem services already analyzed should be conducted in order to obtain a better global

understanding of the ecosystems services capacity in the area. Also, the study could be conducted in

other types of climate in order to evaluate if the ecosystem services are affected in the same way under

different climate conditions.

41

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Appendix A. Pollinators database

Genera Flight distance (m) Flight season Nesting preferences

Andrena 35727.28 April to June Ground

Shrubs

Dasypoda 77802.61 September to May Ground

Eucera 81053.87 May to July Ground

Halictus 121906.90 Mars to October Ground

Heriades 8963.11 June to September Wood

Lasioglossum 31730.34 Mars to September Ground

Wood

Panurgus 31730.34 June to August Ground

48

Appendix B. Bird Species identified in the Monte Novo irrigation site

Species Feeding preferences nest preferences

Acrocephalus scirpaceus invertebrates shrub

Aegithalos caudatus invertebrates trees

Alectoris rufa herbivorous herbs

Apus apus invertebrates cavity

carduelis cannabina invertebrates shrub

Carduelis carduelis seeds shrub

Carduelis chloris seeds trees

Cettia cetti invertebrates shrub

Ciconia ciconia invertebrates cavity

Cisticola juncidis seeds herbs

Columba livia seeds cavity

Corvus corone omnivorous trees

Coturnix coturnix invertebrates herbs

Cuculus canorus omnivorous foster nest

Cyanistes caeruleus omnivorous cavity

Cyanopica cyanus invertebrates trees

Delichon urbicum seeds cavity

Emberiza calandra seeds herbs

Fringilla coelebs seeds trees

Galerida cristata omnivorous ground

Garrulus glandarius omnivorous trees

Hippolais polyglotta invertebrates shrub

Hirundo daurica invertebrates shrub

Hirundo rustica invertebrates cavity

Lanius meridionalis invertebrates shrub

Lanius senator invertebrates trees

Lophophanes cristatus invertebrates cavity

Lulula arborea omnivorous shrub

Luscinia megarhynchos omnivorous shrub

Merops apiaster invertebrates cavity

Milvus migrans carnivorous trees

Parus major invertebrates cavity

Passer domesticus invertebrates cavity

Phylloscopus ibericus invertebrates shrub

Pica pica omnivorous trees

Saxicola torquatus invertebrates shrub

Serinus serinus seeds trees

Sitta europaea invertebrates cavity

Streptopelia decaocto seeds trees

Sturnus unicolor omnivorous cavity

Sylvia melanocephala omnivorous shrub

Sylvia undata omnivorous shrub

Turdus merula omnivorous trees

Upupa epops invertebrates cavity

49

Appendix C. Carbon Pools database

LULC Above the ground pool

Montado

40 Houghton & Hackler 2000 United States Chaparral

25.786

20.04 APA 2015 Portugal

34.72 (Correia et al. 2014)

33.7 (Correia et al. 2014) Central

Portugal

Q. suber

woodland

0.47 IPCC

Montado

with crops

20.04 APA 2015 Portugal 10.255

0.47 IPCC

Extensive

crops 0.31 APA 2015 Portugal 0.31

Irrigated

crops 0.31 APA 2015 Portugal 0.31

Extensive

Olive 7.85 APA 2015 Portugal 7.85

Pine

plantations

200 Houghton & Hackler 2000 United States Chaparral 109.395

18.79 APA 2015 Portugal

Eucalyptus

plantations 20.04 APA 2015 Portugal 20.04

Extensive

vine

3.34 APA 2015 Portugal 2.81

2.28 (Brunori et al. 2016) Italy vineyard

Riparian

galleries

196.5 (Giese et al. 2003) Savannah,

USA

Riparian

galleries

184.0667 155.7 (Rheinhardt et al. 2012) North

Carolina

Riparian

zone

200 (Naiman & Decamps 1997) Riparian

zones

Irrigated

olive 9.73 (Proietti et al. 2014) Italy

Olive

grove 9.73

Corn 16.85 (Liebman et al. 2013) USA corn

14.275 11.7 (Pordesimo et al. 2004) corn stove

Irrigated

forage 0.53 APA 2015 Portugal 0.53

Vegetable

crops 0.31 APA 2015 Portugal 0.31

Irrigated vine 2.59 (Juhos & Tõkei 2012) Hungry vineyard 2.59

Irrigated

cereal 4.06 (Peregrina et al. 2014) Spain barley 4.06

50

LULC Below the ground Pool

Montado 2.94 APA 2015 Portugal 2.94

Montado

with crops 2.94 APA 2015 Portugal 2.94

Extensive

crops 0.31 APA 2015 Portugal 0.31

Irrigated

crops 0.31 APA 2015 Portugal 0.31

Extensive

Olive 1.15 APA 2015 Portugal 1.15

Pine

plantations 1.46 APA 2015 Portugal 1.46

Eucalyptus

plantations 4.2 APA 2015 Portugal 4.2

Extensive

vine

2.87 APA 2015 Portugal 1.79

0.71 (Brunori et al. 2016) Italy vineyard

Riparian

galleries

4360 (Giese et al. 2003) Savannah,

USA

Riparian

galleries 2230.5

101 (Naiman & Decamps 1997) Riparian

zones

Irrigated

olive 1.36 (Proietti et al. 2014) Italy

Olive

grove 1.36

Corn 2.02 (Liebman et al. 2013) USA corn 2.02

Irrigated

forage 0.94 APA 2015 Portugal 0.94

Vegetable

crops 0.31 APA 2015 Portugal 0.31

Irrigated vine 9.98 (Juhos & Tõkei 2012) Hungry vineyard 9.98

Irrigated

cereal 0.58 (Peregrina et al. 2014) Spain barley 0.58

LULC Soil Pool

Montado

80 Houghton & Hackler 2000 United States Chaparral

60.25

54 APA 2011 Portugal

50.8 (Lozano-García & Parras-Alcántara 2013) Spain dehesa

62.2 (Correia et al. 2014) Central

Portugal

35 (Gómez-Rey et al. 2013) Évora

Q suber

and Q

rotundifoli

a open

woodland

79.5 (D’Acqui et al. 2015) Italy Oak

Montado

with crops

60 Houghton & Hackler 2000 United States Chaparral 57

54 APA 2011 Portugal

51

Extensive

crops 38 APA 2011 Portugal 38

Irrigated

crops

54 APA 2011 Portugal 36.4

18.8 (D’Acqui et al. 2015) Italy Cropland

Extensive

Olive

55 APA 2011 Portugal

38.87

39.9 (Lozano-García & Parras-Alcántara 2013) Spain olive

grove

35.5 (D’Acqui et al. 2015) Italy Olive

25.08 (Castro et al. 2008) Spain Olive

grove

Pine

plantations

160 Houghton & Hackler 2000 United States Chaparral

68.4

38 APA 2011 Portugal

30.1 (Almagro et al. 2013) SE Spain

Open

Aleppo

pine

woodland

45.5 (D’Acqui et al. 2015) Italy Pine

Eucalyptus

plantations 68 APA 2011 Portugal 68

Extensive

vine

40 APA 2011 Portugal 42.08

44.16 (Brunori et al. 2016) Italy vineyard

Riparian

galleries

12.03

8 (Giese et al. 2003)

Savannah,

USA

Riparian

galleries 25.619

39.2 (Rheinhardt et al. 2012) North

Carolina

Riparian

zone

Irrigated

olive

13.5 (Almagro et al. 2013) SE Spain Olive

grove

30.145

42.1 (Chiti et al. 2012) Italy Olive

grove

39.9 Rodríguez-Murillo 2001 Spain Olive

grove

25.08 (Castro et al. 2008) Spain Olive

grove

Corn 26.9 (Gregorich et al. 2001) Ontario Monocult

ure maize 26.9

Irrigated

forage 49 APA 2011 Portugal Pastagens 49

Vegetable

crops 36.4 (D’Acqui et al. 2015) Italy Cropland 36.4

Irrigated vine

77.8 (Chiti et al. 2012) Italy Vineyard

48.7 28.9 Rodríguez-Murillo 2001 Spain Vineyard

39.4 Martin et al. 2011 France Vineyard

52

Irrigated

cereal

1.52 (Peregrina et al. 2014) Spain barley 2.51

3.5 (Plaza-Bonilla et al. 2014) Spain barley

LULC Dead Matter

Montado 2 APA 2011 Portugal 2

Montado

with crops 2 APA 2011 Portugal 2

Extensive

crops 0 APA 2011 Portugal 0

Irrigated

crops 0 APA 2011 Portugal 0

Extensive

Olive 0 APA 2011 Portugal 0

Pine

plantations 2 APA 2011 Portugal 2

Eucalyptus

plantations 1 APA 2011 Portugal 1

Extensive

vine 0 APA 2011 Portugal 0

Riparian

galleries

2420 (Giese et al. 2003) Savannah,

USA

Riparian

galleries 1252.7

85.4 (Rheinhardt et al. 2012) North

Carolina

Riparian

zone

Irrigated

olive 0 APA 2011 Portugal 0

Corn 0 APA 2011 Portugal 0

Irrigated

forage 0 APA 2011 Portugal 0

Vegetable

crops 0 APA 2011 Portugal 0

Irrigated vine 0 APA 2011 Portugal 0

Irrigated

cereal 0 APA 2011 Portugal 0