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

Presentation title | Presenter name | Page 6

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

Presentation title | Presenter name | Page 8

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:

Presentation title | Presenter name | Page 11

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

Presentation title | Presenter name

|

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

6

96/9

7

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 nancy.schellhorn@csiro.au 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)