Arthropods in Agricultural Landscapes: Challenging and...
Transcript of Arthropods in Agricultural Landscapes: Challenging and...
Nancy Schellhorn Principal Research Scientist Team Leader – Spatial Ecology
Arthropods in Agricultural Landscapes: Challenging and Supporting Food and Fibre
Production
AGRICULTURE FLAGSHIP
Presentation title | Presenter name | Page 2
‘Despite a clear increase in pesticide use, crop losses have
not significantly decreased during the last 40 years’ (Oerke, 2006, J Ag Sc)
Yield loss (pre-farm gate) and efficacy of control
(Oerke, 2006, J Ag Sc)
Loss
Pre-harvest 18%
+ post-harvest = 37%
Increasing Food Demand Global challenges (Keating et al 2014):
1. Reducing food & fibre loss
2. Minimizing degradation of Ag landscapes
3. Reducing farm inputs
Entomology
Key message:
Entomology will be part of the solution
• Connect scales
• Demonstrate impact
Global challenge of food & fibre demand
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Arthropods are habitat linkers!
Spatial & Temporal Scale
Population processes occur at various spatial scales
Stiling & Strong 1982, Heads & Lawton 1983; Weins & Milne 1989; Thies & Tscharntke 1999; Bommarco & Banks 2003, Schellhorn
& Andow 2005; Tscharntke et al 2005; Schellhorn et al 2015 (in press)
Temporal scales
Farming Practices
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Measures of Impact
Must be linked to the management objective
Examples are many and varied but must provide
evidence of:
• lesser problem
• greater savings
• science-based solution is being adopted
• Movement • Landscapes • Farming practices
Research approach:
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OUTLINE: 2 examples - global challenges
1. Link between perennial habitat and crop pest control
Minimizing degradation of Ag landscapes
2. Moth behaviour across cotton / grain landscapes
Reducing Farm Inputs
Reducing food & fibre loss
Thies & Tscharntke 1999, Science
Seminal paper – Landscape Structure and Biological Control in Agro-ecosystems
Meligethes aeneau
Ichneumonidae
Tersilochus heterocerus,
Phradis interstitialis,
P. morionellus
What’s the global state of play 15+?
> 65 Studies rl/sp landscape metrics (% area) and
abundance and diversity of NE
Trend is positive – higher abundance and diversity of NE
with higher % non-crop habitat;
Most studies = NE abundance and diversity – a few
measure pest suppression;
Demonstrates a clear link b/t spatial scales -- field, farm &
landscape.
The ‘problems’ and gaps
Schellhorn et al Insect Sci 2015
Strong focus on NE abundance / diversity & landscape
Need more examples of pests abundance & landscape
Most Northern Hemisphere;
Need examples from Asia;
Need more examples that integrate farming practice with
landscape ecology studies
Several have demonstrated pest suppression = pest control
Need evidence of > control or > savings; evidence of impact (Chaplin-Kramer et al 2012; Jonnson et al 2012; Rusch et al 2013; Costamanga et al 2014 (in press))
1. Link between perennial habitat and crop pest control
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Bethugra, NSW
Ex. 1. The problem: Focus has been on pest and natural enemies in crops – not other habitats in landscape.
Key Predators & Pests, juveniles & adults
Ex. 1. The solution: Where are pests and predators found?
Method
Landscape
Year/ Month
Sites
Region
Crop Near
Sites
Native Veg.
Weeds
Sites
Monthly Samples
F M A M J J A S O N D J F M A M J J A S O N D
2009 2010
Crop Far Sites
Ex. 1. The solution: Where are pests and predators found?
Pest = Region (40%) (F2,282=209.92 P<.001 ) and habitat type (31%) (F2,282=163.01 P<.001 )
Predators = 50% habitat type alone (F2,282=159.46 P<.001)
Bianchi et al, 2013; Ag & For Ent ; Parry et al, 2015, Basic & App Ecol (in press)
In native remnants, on which plant species are they found?
Pests = 47% of the variance was explained by plant type alone (F2,154=72.59 P<.001)
Predators = region (35%) (F2,152=67.03 P<.001 ) and plant type (4%) (F2,152=8.12 P<.001 )
Bianchi et al, 2013; Ag & For Ent; Parry et al, 2015, Basic & App Ecol (in press)
The solution: When are they there?
Photo credit: Keith Power, Beatsheet
Adult Nymph
Rutherglen Bug Nysius vinitor
NSW: January-March 2010
NSW: April-June 2010
NSW: July-September 2010
canola
canola
canola
canola
canola
canola
NSW: October-December 2010
canola
canola
canola
canola
canola
canola
More weeds in pastures = more Rutherglen bug juveniles
0
25
50
75
100
0.5 2.5 3 5 7.5 15 35 50weed percentage
Mea
n de
nsity
of r
gbJ
per
m2
Me
an
Ruth
erg
len b
ug juvenile
s p
er
m2
The Solution: When are they there?
0.0
100.0
200.0
300.0
400.0
500.0
Tota
l per
mo
nth
m2
NV Adult
NV Juv
0
100
200
300
400
500
600
Jan
Feb
Mar
ch
Ap
ril
May
Jun
e
July
Au
g
Sep
t
Oct
No
v
De
c
Jan
Feb
Mar
ch
Ap
ril
May
Jun
e
July
Au
g
Sep
t
Oct
2010 2011
Tota
l per
mo
nth
m2
Canola Adult
Canola Juv
Wheat Adult
Wheat Juv
Photos credit: www.goodbugs.org.au
Pe
st d
en
sity
Time
Theory predicts: Early arrival of predators results in better pest suppression
Settle et al. 1996; Ives & Settle 1997; Landis & van der Werf 1997; Bianchi & van der Werf 2003; Bianchi et al 2009
Sentinel plants (1000’s+)
The solution: Is pest suppression greater in fields near remnant veg patches compared to far?
Melon
Whitefly
nymphs
Cotton
Helicoverpa
eggs
Darling
Downs
Landscapes
simple
complex
X 19
Lockyer
Valley, QLD
Period
Para
sitoid
s p
er
pla
nt
0.0
0.1
0.2
0.3
0.4
0.5
2007
2008
Early Mid Late
Remnant Near Far
0.0
0.2
0.4
0.6
0.8
North
South
Pa
rasito
ids p
er
pla
nt
Cotton
Bianchi et al. Ag, Ecosys, & Env (in review)
Darling
Downs
Spatially and temporally erradict
Significant factors - % NV @ 1km; TRT; Period; Year
0 5 10 15
0
50
100
150
surv
ivin
g a
phid
s
% Lucerne at 2 km
Land-use predictors – Aphid predation
aphids = 70.29 - 5.29 (% Lucerne 1.5K); Adj. r2 = 0.37
Melon
% Lucerne 1.5K Costamanga et al. Ecol Appl (in press)
Adj r2 = 0.41
Sources of Insect Predators
Costamanga et al. Ecol Appl (in press)
Snap shot - Data Summary
Think beyond the crop -- Landscape context matters!
Native vegetation clearly provides
habitat for beneficials, especially
important out-of-cropping season
Weeds harbour pests, and some
beneficials;
Perennial pasture and lucerne plays key
role in landscape for beneficials AND for
pests if pasture is degraded and weedy!
Presentation title | Presenter name 35 |
Parry et al., Basic
and Applied Ecology
(in press)
Relative benefit of a plant species
Predator Dominated
Pest Dominated
Presentation title | Presenter name
Lower for Longer
Pest suppression
Schellhorn et al. Insect Science 2015 – Special Feature
Pest control
Presentation title | Presenter name | Page 38
Summary Ex. 1:
Link Conservation Biology
& Agricultural Production
? IMPACT
2. Helicoverpa spp moth behaviour across a landscape
Helicoverpa armigera
[GLOBAL PEST]
and H. punctigera
159 host spp,
~85 HA & HP
~34 HA (monocots) (Zalucki et al 1986)
Bt Cotton (Bollgard®)
Helicoverpa armigera & H. punctigera
0
2
4
6
8
10
12
95/9
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96/9
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97/9
8
98/9
9
99/0
0
00/0
1
01/
02
02/
03
03/
04
04/
05
05/
06
06/
07
ac
tiv
e in
gre
die
nt
(kg
pe
r h
a)
Conventional INGARD Bollgard II
IMPACTS of GM based IPM - Insecticide Reductions
Ist generation Bt cotton
44% reduction
2nd Generation Bt Cotton
85% reduction
Data courtesy of GP Fitt
The Problem: Reduction in insecticide use depends on: 1. continued effective Resistance Mngt Plan;
and 2. biological control for other Helicoverpa
susceptible crops across landscape.
Queensland, Australia
Darling Downs,
Field data collection
Cotton
Sorghum
Maize
Pigeon Pea
Bt Cotton Other host crops
(e.g. Sorghum)
SINK
Grasses
SOURCE
Ha only
Helicoverpa armigera
and H. punctigera
Mandated Refuge
(Pigeon Pea)
SOURCE
Ha & Hp
159 host spp,
~85 HA & HP
~34 HA (monocots) (Zalucki et al 1986)
Mandated Refuge (% of Bollgard Cotton)
• 5% Pigeon Pea
• 10% Unsprayed conventional cotton
• Within 2km of Bollgard Cotton
Planting Windows
Pupae Busting
$170-230 Ha
(seed, H2O,
cultivation, herbicide)
$2000-2800 Ha
(gross margin of cotton)
(pers. comm. Dr Paul Grundy)
hypotheses E
gg
de
nsity
0 100
% Bt Cotton (km)
H. Armigera only
% Sorghum (km)
Eg
g d
en
sity
0 100
100 %
of crop
1 2 6 5 4 3 8 7
fields
Selecting Fields for Sampling & Characterizing Land Use / Land Cover
sorghum
Bt cotton (sink)
Corn
Pigeon pea
26 different land use categories were
mapped for each landscape using Arc GIS
185 ha
30 ha
8 Bt cotton fields
3 landscapes
5 years
Parasitized
H. armigera
H. punctigera
Helicoverpa spp
Infertile
Unviable
Parasitized unknown
Counted
Collected
a sub-set
Fates determined:
(23,819)
(15,934)
Eggs tell us about
female
moth behaviour
Eggs in Bt cotton tell
us about colonization
Summary of spatio-temporal data
•Three landscapes
•Five Years
N C P N C P N C P N C P N C P
09/10 10/11 11/12 12/13 13/14
Drought Big Flood Regular Flood Drought
Bt cotton sorghum pigeon pea
Egg data: Approx 36,000 eggs counted;
20,000 eggs collected - fates for Helicoverpa
spp. and egg parasitoids.
Moth data -Sweep Net data: Caught and
disturbed Helicoverpa spp (♀ moths caught
12/13 and 13/14 dissected )
Moth data - Pheromone trap data: ♂
Helicoverpa both spp. approx 6000 traps deployed
75000 male moths caught
Pupae dig data: Helicoverpa spp. associated
parasitoids (5th instar = trigger) 15 sites
Moth data - UV Solar light trap data: 64 traps rpt
3 x for one landscape. Total 192 traps
♀Helicoverpa moths spp. dissected (approx 90
moths)
PAMPAS
% L
and
Co
ver
0
20
40
60
80
100
0
20
40
60
80
100
CECIL PLAINS
NANDI
0
20
40
60
80
100
Matrix Source Bt cotton
Pampas
2009-10
DROUGHT
2010-11
FLOOD
2011-12
NORMAL
2012-13
FLOOD
2013-14
DROUGHT
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
Cecil Plains
Dec 6Jan 6
Jan19Feb 9
Feb 23
Mar 16
0
20
40
60
80
100
Dec 6
Flood
Feb 8
Feb 22
Mar 8
Mar 22
Apr 6
0
20
40
60
80
100
Dec 6
Dec20
Jan 3
Jan 17
Jan 31
Feb 14
Feb 280
20
40
60
80
100
Dec 5
Dec 19Jan 2
Jan 15
Flood
Feb 2
Feb 20Mar 6
0
20
40
60
80
100
Dec 2
Dec 16
Dec 30
Jan 13
Jan 27
Feb 10
Feb 240
20
40
60
80
100
Helicoverpa armigera Helicoverpa punctigera
% H
elic
overp
a e
ggs c
olle
cte
d f
rom
Bt cotton
Date - Trip
*
* No eggs found
Nandi
Helicoverpa armigera
and H. punctigera
Random Forest Models What is it?
Machine learning technique
Average of a large series of de-correlated regression trees
Random Forest Models What are the features?
• Distribution free; • Large # of predictors of diff. types (continuous and
categorical); • Handles missing data; • Handles collinearity – (i.e. landscape compositional data;
nested spatial scales) • Non-parametric modelling approach
4 spatial scales, 26 land uses, +temporal variables = 100+
Top Predictors
from RF model –
Pruned Regression Tree
LARGEST
# predicted Ha eggs
(165) Yr 2;
>4.5 Hp eggs;
Sorghum @ 2km
< 15%
SMALLEST
# predicted Ha
eggs (3-6);
Cotdev pre-flwr
n=258
years (n=682)
LARGEST
# predicted Ha eggs
YR1,4,5; Lndscp B,C
Little fallow;
Top Predictors
from RF model –
Pruned Regression Tree
TEMPORAL variables
Flowering Cotton,
Moon Phase,
Year,
& est. # Ha eggs
n=682
LARGEST
# predicted Hp eggs;
Waxing moon @ >80%,
or Cot flrw & lots of Ha
eggs;
Random Forests Parametric models
Exploratory tool
- 100+ predictors
- Eg. 4 spatial scales, 3 temporal scales, egg
densities of each Helicoverpa spp, and 26
landuses;
confidence; shift question
-predictors affecting spp proportions & predict
abundance
What predicts H. armigera egg abundance in cotton?
GLMM – fixed effects [Yr,Trip, unique field, season:lndscp random effects]
H. armigera
Eg
g d
en
sity
0 100
% Bt Cotton (km) % Bt Cotton @ 2 km
Landscape
N
CP
P E
gg d
ensity in c
ott
on
0 100
(adjusted R2 = 52.1 )
• 20 x more eggs (1km radius) in
sorghum ‘surrounded’ by cotton, than
sorghum in a ‘sea’ of sorghum
•More moths too.
H. armigera eggs and moths in sorghum?
Early descriptive results
What predicts H. punctigera egg abundance in cotton?
GLMM – fixed effects [Yr,Trip, unique field, season:lndscp random effects]
H. punctigera
Eg
g d
en
sity
0 100
% Bt Cotton (km)
Cotton
development
% of Full Moon % fallow (bare soil) @ 1km
No.
of
eggs in c
ott
on
(adjusted R2 = 47.9)
Summary of Results:
Spatial drivers dominate for H. armigera
Temporal drivers dominate for H. punctigera
Local landscape
Host preference (sorghum over cotton)
80% waxing to full moon
Cotton Development (squares to open flowers)
No crops at 1km
‘Influence of the moon is likely to be
complex’ Zalucki 91; Zalucki et al 94; Morton et al. 81;
Walker 89; Scholz & Parker 2003;
Understanding of key drivers of global pests
Presentation title | Presenter name | Page 63
Summary Ex. ‘2’:
? IMPACT
(Resistance Management
Plan)
Future opportunities to reduce farm inputs:
N
Bommarco et al, Oecologia (in review) Lundin et al 2013, Proc Royal Soc B
YIELD
Termite and Ant Tunnels – No-till marginal grain systems
Evans et al, 2012, Nature Communications
H2O infiltration; N uptake;
YIELD 37%
Ecological intensification
• Movement • Landscapes • Farming practices
Conclusion:
Impact • Capture of pest control services from
landscape mosaic = protect natural farm assets • Potential to refine resistance management
plan = reduce food fibre loss; reduce pesticide use
Presentation title | Presenter name | Page 66
15-20-fold
increase in the
amount of
pesticides used
worldwide in
grain
(Oerke 2006)
Presentation title | Presenter name | Page 67
AG Landscape Ex.
MANY THANKS TO:
Growers;
Funders; and
Collaborators
Svet Micic, Tony
Dore, Art Diggle
Presentation title | Presenter name | Page 68
Bt Cotton Ex.
MANY THANKS TO:
Growers;
Funders; Tracey Parker;
and many casuals
Thank you CSIRO Dr Nancy Schellhorn
T +61 7 3833 5710
e [email protected] w http://www.csiro.au/people/Nancy.Schellhorn
AGRICULTURE FLAGSHIP
Egg Fates
H. armigera
H. punctigera
Unable to distinguish
Unviable
Parasitized (spp)
Unable to distinguish
Presentation title | Presenter name | Page 72
Doubling of Grain Production since 1960’s (Oerke, 2006, J Ag Sc)
6.9-fold increase in N fertilizion
1.7-fold increase in amount of irrigated cropland
1.1-fold increase in land in cultivation
0.5-0.6 increase in genetic improvements by crop breeders (McLaren 2000)
15-20-fold increase in the amount of pesticides used worldwide
(No significant decrease in crops losses; has enabled farmers to
increase crop productivity without losing an economically non-acceptable
portion of their crop to pests)