Download D3.3 Annex 1 review pest risk mapping best practice

ENHANCEMENTS OF PEST RISK ANALYSIS TECHNIQUES
Milestone 3.5 Best practice for mapping endangered areas identified
Author(s): Richard Baker, Christelle Robinet, David Makowski,
Alain Roques, Sylvie Augustin, Sarah Brunel, Philippe Reynaud,
Maxime Dupin, Darren Kriticos, Vojtech Jarosik,
Sue Worner
Partner(s): Fera, INRA, EPPO, LNPV, CRCNPB, IBOT, Bio-Protection
Submission date: November 2008
EU Framework 7 Research Project
Enhancements of Pest Risk
Analysis Techniques
(Grant Agreement No. 212459)
PRATIQUE
No. 212459
Deliverable number:
Date: DD/MM/YYYY
_____________________________________________________________________
PROJECT OVERVIEW: PRATIQUE is an EC-funded 7th Framework research project
designed to address the major challenges for pest risk analysis (PRA) in Europe. It has three
principal objectives: (i) to assemble the datasets required to construct PRAs valid for the
whole of the EU, (ii) to conduct multi-disciplinary research that enhances the techniques used
in PRA and (iii) to provide a decision support scheme for PRA that is efficient and userfriendly. For further information please visit the project website or e-mail the project office
using the details provided below:
Email: [email protected]
Internet: www.pratiqueproject.eu
Authors of this report and contact details
Name: Richard Baker
Partner: Fera
E-mail: [email protected]
Name: Christelle Robinet
Partner: INRA
E-mail: [email protected]
Name: Christelle Robinet
Partner: INRA
E-mail: [email protected]
Name: Alain Roques
Partner: INRA
E-mail: [email protected]
Name: Sylvie Augustin
Partner: INRA
E-mail: [email protected]
Name: Sarah Brunel
Partner: EPPO
E-mail: [email protected]
Name: Philippe Reynaud
Partner: CIRAD (LNPV)
E-mail: [email protected]
Name: Maxime Dupin
Partner: CIRAD (LNPV)
E-mail: [email protected]
Name: Darren Kriticos
Partner: CRCNPB
E-mail: [email protected]
Name: Vojtĕch Jarošík
Partner: IBOT
E-mail: [email protected]
Name: Sue Worner
Partner: Bioprotection
E-mail: [email protected]
Page 2 of 30
Disclaimer:
This publication has been funded under the small collaborative project PRATIQUE, an EU 7th
Framework Programme for Research, Technological Development and Demonstration
addressing theme: [kbbe-2007-1-2-03: development of more efficient risk analysis techniques
for pests and pathogens of phytosanitary concern call: fp7- kbbe-2007-1]. Its content does not
represent the official position of the European Commission and is entirely under the
responsibility of the authors. The information in this document is provided as is and no
guarantee or warranty is given that the information is fit for any particular purpose. The user
thereof uses the information at its sole risk and liability.
PRATIQUE
No. 212459
Deliverable number:
Date: DD/MM/YYYY
_____________________________________________________________________
Milestone 3.5 Best practice for mapping endangered areas
identified (month 6) (Subtask 3.3.1)
CONTENTS
1.
Task objective
2.
Best practice on mapping endangered areas from existing PRA standards
and schemes
3.
Best practice on mapping endangered areas from the literature (including
other EU projects)
4.
Key issues to address
5.
Conclusions
Annex 1: Profile of risk mapping software packages
Annex 2: Mapping the potential distribution of insect species: what is the best
practice?
1.
Task Objective
Text from the Description of Work:
Best practice worldwide for mapping endangered areas under current and future
climates will be determined by a review of PRA schemes and the literature. Particular
importance will be given to evaluating the work undertaken by EU funded projects,
e.g. ALARM, that have assembled and mapped relevant datasets.
CSL, CIRAD, Bio-Protection, CRCNPB, INRA, JKI, IBOT and UPAD will undertake
this work.
2.
Best practice on mapping endangered areas from existing PRA standards
and schemes
2.1
Defining endangered areas in International Standards
The following text is the only guidance given on defining endangered areas in the
International Standards for Phytosanitary Measures (ISPMs):
ISPM5 (IPPC, 2006)
Endangered Area: An area where ecological factors favour the establishment
of a pest whose presence in the area will result in economically important loss
[FAO, 1995]
ISPM11 (IPPC, 2004)
2.2.4.1 Conclusion regarding endangered areas
The part of the PRA area where ecological factors favour the establishment of the
pest should be identified in order to define the endangered area. This may be the
whole of the PRA area or a part of the area.
Page 2 of 30
2.3.3.1 Endangered area
The part of the PRA area where presence of the pest will result in economically
important loss should be identified as appropriate. This is needed to define the
endangered area.
It is clearly describing a two step process to define the area:
(i)
where the organism can establish based on ecological factors and
(ii)
where the organism will cause economic damage
Like all ISPMs it does not describe or recommend the particular methods that should
be used. Even the term “mapping” is not included. However in paragraph 2.2.2, it
does state that: “Climatic modelling systems may be used to compare climatic data on
the known distribution of a pest with that in the PRA area.”
2.2
Defining endangered areas in the EPPO Risk Analysis Scheme
The following text is in the current EPPO PRA scheme (EPPO, 2007):
EPPO PRA SCHEME
Conclusion regarding endangered areas
1.35 Based on the answers to questions 1.16 to 1.34 identify the part of the PRA
area where presence of host plants or suitable habitats and ecological factors
favour the establishment and spread of the pest to define the endangered area.
Note: The PRA area may be the whole EPPO region or part of it. The
endangered area may be the whole of the PRA area, or part or parts of the area (i.e.
the whole EPPO region or whole or part of several countries of the EPPO region). It
can be defined ecoclimatically, geographically, by crop or by production system (e.g.
protected cultivation such as glasshouses) or by types of ecosystems.
2.16 Referring back to the conclusion on endangered area (1.35), identify the parts
of the PRA area where the pest can establish and which are economically most at
risk.
This text mirrors that in ISPM11. It states that the endangered area can be defined by,
e.g.:
 Ecoclimatic zones
 Geographic area
 Crop distribution
 Production systems (e.g. protected cultivation)
 Ecosystem
However, like ISPM11, it does not describe or recommend any particular method that
should be used.
Pragmatically, because no clear methods are available for defining the endangered
areas where economically important loss will occur and there may not be agreement,
EPPO has suggested (EPPO, 2007) that: “The EPPO PRA process should identify the
endangered area whenever possible, i.e. when there is a consensus that the presence
of the pest in the area may result in economically important loss. When there is no
consensus that the presence of the pest in the area may result in economically
important loss, the delimitation of the endangered area is not possible at the EPPO
level. In this case only the area of potential establishment can be determined.”
2.3
Defining endangered areas in other PRA Schemes
Australia: no specific guidance given
New Zealand: no specific guidance given
Canada: no specific guidance given beyond what is already given in the ISPMs
(Karen Castro, personal communication 22nd August, 2008)
USA: No specific guidance is provided in the PRA scheme but detailed risk maps
with detailed instructions are available particularly as output from NAPPFAST that
combines pest (degree day) and disease models (generic infection model) with host
distributions to map pest risk in the USA (Magarey et al., 2007).
Other schemes: no risk mapping guidance given
2.4.
Conclusions on best practice in defining endangered areas in International
Standards and PRA Schemes
Although ISPM11 states that the endangered area should be based on where (a)
ecological factors favour the establishment of the pest and (b) the presence of the pest
will result in economically important loss, existing PRA schemes provide little or no
guidance on how this should be done. This is primarily because risk mapping tends to
be confined to detailed PRAs, undertaken, for example, to combat a specific new
threat, to determine whether expensive/stringent phytosanitary measures are justified
or to respond to legal/trade challenges. As such, best practice in mapping endangered
areas can generally only be inferred by examining these detailed PRAs.
Examples:
Phytophthora ramorum (RAPRA EU project in prep; NAPPFAST USA; Canada)
Tilletia indica (Karnal Bunt Risks EU project; USA)
Diabrotica virgifera virgifera (DIABRACT EU project; UK)
Anoplophora glabripennis (Europe)
Eichornia crassipes (EPPO)
Features in common:
 Emphasis on identifying areas of climatic suitability
 Some mapping of host distribution
3.
Best practice in mapping endangered areas from the literature (including
other EU projects)
3.1
Mapping endangered areas based on ecological (including human) factors
Ecological and human factors that can be mapped include (EPPO PRA scheme
question in brackets):
 Abiotic
o Climate (1.19)
o Soils (1.20)
o Pollution (1.20)
o Topography (1.20)
o Aquatic factors (1.20)
 Biotic
o Hosts (1.17)
o Habitats (1.17)
o Alternate hosts, vectors, root symbionts, pollinators, seed dispersers etc
(1.18)
o Competitors (1.22)
o Natural enemies (1.23)
 Human
o Entry points (1.13)
o Cultivation/management practices (1.24)
o Commodity, conveyance, human movement (1.33)
3.1.1 Climate
Modelling and mapping the climatic factors suitable for establishment is generally the
most frequently attempted.
Magarey et al. (2007) list 18 methods (see Annex 1). These can be loosely divided
into:
 Climatic mapping based on Deterministic Models, e.g:
o Degree Day and phenology model mapping
o NAPPFAST (generic pest/pathogen models with interpolated weather
data from ZedX)
 Climatic mapping based on Inductive Techniques, e.g:
o Climate envelope models
o BioSim
o MaxEnt
o CLIMEX: Match Climates
 Climatic mapping based on Combined Techniques, e.g:
o CLIMEX: Compare locations
A detailed review of the different climatic risk mapping methods has been undertaken
by Christelle Robinet, Alain Roques and Sylvie Augustin for this project (see Annex
2).
Descriptions of best practice for applying and interpreting CLIMEX are provided in
the “EPPO Instructions for the Use and Interpretation of CLIMEX” (EPPO, 2007;
2008 in prep) and in the CLIMEX manual (Sutherst et al., 2007)
3.1.2 Hosts and habitats
In general, other factors, such as suitable hosts/habitats, soils etc are simply overlaid
on the map of suitable climate. If displayed in a GIS, masking techniques can be used
so that only the areas where suitable climate, soils, hosts/habitats etc are present are
displayed. The principal difficulties arise when the datasets are at different spatial and
temporal resolutions and collected at different time periods. Upscaling and
downscaling methods exist for resolving resolution issues. Only very rarely is there
sufficient information to map factors other than the distribution of suitable hosts and
habitats. Robinet et al (Annex 2, Part 4) summarise the issues in part 4 of their review.
3.2
Mapping endangered areas based on economic, environmental and social
impacts
The economic, environmental and social factors that can be mapped include (EPPO
PRA scheme question in brackets):
 Crop area (2.2)
 Crop value (2.2)
 Vulnerable species, habitats and ecosystems (2.7)
 Vulnerable people and communities (2.9)
3.2.1 Mapping the risk of plant invasions in Europe based on habitat invasibility
A specific approach to mapping the risk from invasions by alien plants was recently
undertaken (Chytrý et al. 2009) with funding from the EU ALARM project. This
approach focuses not on individual species but on the overal invasion load in
European habitats. Recent studies analysing plots used for sampling vegetation at the
scale of tens to hundreds of square metres have demonstrated that habitats differ
considerably in their invasibility. The differences in the level of invasion (expressed
as the proportion of alien to all species in the plot) between Central European habitats
are mainly caused by inherent habitat properties, and to a lesser extent by propagule
pressure and climatic differences between regions. Therefore, habitat type is a good
predictor of the level of plant invasion (Chytrý et al. 2008a). It has been also shown
that patterns of habitat invasion are consistent among European regions with
contrasting climates, biogeographical affinities, history and socio-economic
background (Chytrý et al. 2008b). These findings provided a solid background for
mapping the level of plant invasion, based on the projection of the habitat-specific
levels of invasion onto land-cover maps.
More than 50,000 vegetation plots were classified in EUNIS habitat categories
and used to quantify levels of invasion for each habitat. The spatially non-explicit
EUNIS based data were transformed into the spatially explicit CORINE land-cover
classes, based on estimated proportion of each of EUNIS habitat types in CORINE
land-cover classes, and the level of invasion was calculated for each class. Sampling
was done in three European regions, representing Mediterranean, temperate and
Atlantic climate and extrapolated to other regions on the basis of climatic similarities.
The resulting map reflects the risk from invasions by alien plants at the European
scale (Chytrý et al. 2009).
An approach linking large sets of spatially explicit data from vegetation survey
plots can produce robust information on macroecological patterns of plant invasions.
Spatially explicit information on habitat invasions can be used to identify the areas of
highest risk of invasion so as to support effective monitoring and management of alien
plants; combined with scenarios of future land-use change, it may also be used for
prediction of invasion risks in the future (Pyšek et al., 2008).
3.2.2 Mapping other impact factors
A preliminary review of the literature has not provided good examples. Some maps,
e.g. of D. virgifera virgifera in the USA (http://www.entm.purdue.edu/wcr/ ) and
Europe, are based on known impacts. Others are based on maps of highly suitable
ecological conditions (see above).
At the pest risk mapping workshop in Minneapolis, Richard Baker summarised the
challenge of mapping potential impacts as follows:
o Predicting establishment endangered areas based on climatic suitability and
host/habitat range is already very difficult
o Predicting spread very challenging even using diffusion models
o Modelling population dynamics in relationship to an economic injury level
even more difficult
o Can we assign some a priori vulnerability index for economic, environmental
social receptors?
o How do we take time and climate/landuse change into account?
The difference between the establishment and impacts endangered areas can be seen
in this table:
Topic
Establishment
Spread
Population density,
inoculum level
Key factors
Establishment
Endangered Area
Possible
Not necessarily
Sufficient to maintain
presence
Suitable climate, available
hosts/habitats
Impacts Endangered
Area
Very likely
Very likely
Above economic injury
level
Very suitable climate,
many hosts/habitats,
vulnerable receptors of
high value
Some possible ways forward include:
o Splitting establishment endangered area into grid cells and assessing spread,
population dynamics and impact vulnerabilities for each cell
o Assuming most species can be spread rapidly and long distances by man and
assigning risk by distance from ports, nurseries, habitation, existing outbreaks
as appropriate
o Using climatic suitability indices, e.g. growth/ecoclimatic indices, degree
days, generation time, generation number and generic infection index as a
surrogate for population/inoculum density.
o Determining the relative vulnerablity of receptors (by value, size, rarity,
control efficacy etc)
o Estimating the change in impacts over time (shape of the curve) rather than an
overall value
o Studying historic invasions
The most vulnerable crop types/areas are, for example, those with:
o Favoured host status
o Especially high value, e.g. seed potatoes
o Very high quality standards, e.g. dessert fruit
o Long replacement time, e.g. timber & top fruit trees
o Pest friendly management practices, e.g. no rotation for D. virgifera virgifera
o High vector densities
o Significant proportion of national production
o Significant proportion of the export market
o Heritage varieties
o Organic status and/or biological control systems
o No effective control methods available
The most vulnerable environmental receptors include:
o Keystone species
o Rare and endemic species
o Nature reserves and special areas of conservation under, e.g. the EC Habitats
Directive
o Islands and other isolated habitats
o High amenity value
o Important ecosystem services
4.
Key issues to address
Limitations of current methods
 Data requirements
 Representing current and future climate change and land use
 Handling, displaying and communicating uncertainty
No specific guidance on best practice in pest risk mapping exists. This is one of the
reasons for holding the first international Pest Risk Mapping Workshop in June 2007
(Magarey et al., 2007). This meeting identified the following ten critical issues in
building risk models and creating risk maps, ranked as follows:
1. Model assessment, validation and documentation
2. Map representation and visualization of uncertainty
3. Availability and accessibility of primary data
4. Best practice guide for modeling (including toolkit)*
5. Communication, interpretation and use of risk maps by decision-makers*
6. Impact mapping
7. International/online collaboration*
8. Climate change
9. Gap in how human and biological dimensions interact
10. Training in modelling practice*
The issues marked with an asterisk are primarily organisational issues. While
modelling and risk mapping are taken together, clearly, a best practice guide for
mapping endangered areas must cover both aspects. In PRATIQUE, issue 3 is being
dealt with by WP1.
5.
Conclusions on best practice in pest risk mapping from existing schemes
and the literature
There is no existing guide to best practice in mapping endangered areas. Currently,
best practice can only be inferred by analysis of the different methods used and
examples available. This issue was discussed at the international pest risk mapping
workshop in September 2008 and coordinated approach will be adopted with
contributions from the USA, Canada, Australia, New Zealand and Europe. The
European component will be provided by PRATIQUE.
It is clear that mapping and spatial analysis in general could play a much greater role
in all sections of pest risk analysis since almost all the questions asked have a spatial
component and rely on datasets with a spatial reference. Maps communicate risk in a
much more direct and understandable manner than any risk rating method. Its
therefore vital that best practice is described and followed by all.
In some areas, further work needs to be undertaken to determine best practice. These
include:
The most appropriate methods for mapping endangered areas for species with poorly
known distributions and/or climatic responses.
Guidance on the choice of models in particular situations
Mapping pathogens with complex life cycles
Taking climate change into account
Mapping economic, environmental and social impacts (impact endangered areas)
Communicating uncertainty in risk maps
References
Baker, R.H.A. 2002. Predicting the limits to the potential distribution of alien crop
pests. In: Invasive Arthropods in Agriculture. Problems and Solutions, Hallman,
G.J. & Schwalbe, C.P. (Eds). pp. 207-241. Science Publishers Inc. Enfield USA.
Baker, R.H.A., Brunel, S., MacLeod, A. & Kriticos, D. J. 2007. Instructions for the
use and interpretation of CLIMEX. Draft EPPO Document: 07-13301.
Chytrý M., Jarošík V., Pyšek P., Hájek O., Knollová I., Tichý L. & Danihelka J.
2008b. Separating habitat invasibility by alien plants from the actual level of
invasion. Ecology 89: 1541–1553.
Chytrý M., Maskell L., Pino J., Pyšek P., Vila M., Font X. & Smart S. 2008a. Habitat
invasions by alien plants: a quantitative comparison between Mediterranean,
subcontinental and oceanic regions of Europe. Journal of Applied Ecology 45:
448–458.
Chytrý M., Pyšek P., Wild J., Maskell L. C., Pino J. & Vilà M. 2009. European map
of alien plant invasions, based on the quantitative assessment across habitats.
Diversity and Distributions (in press).
EPPO. 2007. EPPO PRA process: definition of the "endangered area" and the "area of
potential establishment". EPPO Document: 07-13572.
Kriticos, D.J. & Randall, R.P. 2001. A comparison of systems to analyse potential
weed distributions. Groves, R. H.; Panetta, F. D., and Virtue, J. G., Eds. Weed Risk
Assessment. Melbourne, Australia: CSIRO Publishing. pp. 61-79.
Magarey, R.D., Kriticos, D.J., Fowler, G.A., Kalaris, T. M., Pitt. J., Baker, R.H.A. &
Koch, F. 2007. Report on the APHIS-PPQ-CPHST Workshop on Pest Risk
Mapping. June 5-7, 2007 in Fort Collins, Colorado, USA
http://www.nappfast.org/ASPRM%20web/ASPRM%20Overview2.doc
Magarey, R.D., Fowler, G.A., Borchert, D.M., Sutton, T.B., Colunga-Garcia, M. &
Simpson, J.A. NAPPFAST: An internet system for the weather-based mapping of
plant pathogens. Plant Disease, 91: 336-345.
Pyšek P., Chytrý M. & Jarošík V. 2008. Habitats and land-use as determinants of
plant invasions in the temperate zone of Europe. In: Perrings C., Mooney H. A. &
Williamson M. (eds.), Bioinvasions and globalization: Ecology, economics,
management and policy, Oxford University Press, Oxford (in press).
Sutherst, R.W., Maywald, G. F. & Kriticos, D.J. 2007. CLIMEX Version 3. User’s
Guide. CSIRO. Hearne Scientific Software Pty Ltd
Sutherst, R.W., Maywald, G. F. & Skarratt, D.B. 1995. Predicting insect distributions
in a changed climate In: R. Harrington and N. E. Stork (eds), Insects in a changing
environment, pp. 59-91.Academic Press, London.
Annex 1 Profile of risk mapping software packages
(from the APHIS-PPQ-CPHST Workshop on Pest Risk Mapping on June 5-7, 2007 in
Fort Collins, Colorado, USA)
Package name
Objective
Model style
Artificial Neural
Networks (ANN)
ANN can identify
relationships between
the presence and
absence of the insect
species and climatic
variables at
different sites,
To describe the climatic
envelope of a species
and to predict its
occurrence
ANNs are an
alternative
modeling
technique based
on machine
learning.
BioMOD
BIOMOD:
BIOdiversity Modeling
aims to maximize the
predictive accuracy of
current species
distributions and the
reliability of future
potential distributions
using different types of
statistical modeling
methods.
CLIMATE
To predict the
distribution of an
organism based upon
climate preferences –
mainly weed risk
assessment
CLIMATE ENVELOPE
To predict the potential
distribution of species
using point data from
herbaria or museums
BIOCLIM/ ANUCLIM
Computer
platform
Proces
s or
regress
ion
Orient
ed
Reference
Various
R
Gevrey and
Worner
(2006)
Climate patternmatching with
minimum
bounding
rectangle
(MBR)
PC and UNIX
R
Nix (1986),
Busby (1991)
Hutchinson et
al. (1996)
Biomod
computes, for
each species and
in the same
package, the
four most
widely used
modeling
techniques in
species
predictions,
namely
Generalized
Linear Models
(GLM),
Generalized
Additive Models
(GAM),
Classification
and Regression
Tree analysis
(CART) and
Artificial Neural
Networks
(ANN).
Climate patternmatching with
choice of several
match
techniques
including MBR
and point-topoint similarity
indices (Gower
1971)
Climate patternmatching using
MBR
Unknown
R
Thuiller
(2003)
Apple
Macintosh/PC
R
Pheloung
(1996)
Web (UNIX)
R
Boston &
Stockwell
(1994)
CLIMEX for Windows
(Compare locations)
To compare locations or
match climates
To predict the relative
climatic suitability for a
species at selected
locations
(Match climates)
To predict the relative
climatic similarity
between different
locations
DOMAIN
Conservation ecology,
assessing adequacy of
reserve design and
designing sampling
strategies
The ENFA’s principle
is to compare the
distributions of the
EGV between the
presence data set
(species distribution)
and the whole area
(global distribution).
ENFA (Environmental
Niche Factor Analysis)
FloraMap
GARP
GLIM/GAM
FloraMap is a
specialized computer
program (and associated
data) that was
developed to map the
predicted distribution,
or areas of possible
climatic adaptation,
of organisms in the
wild.
To predict the potential
distribution of species
using point data from
herbaria or museums
using climatic and nonclimatic data
To predict the
probability of
occurrence of species
on a fine scale based
upon statistical
regression models
Process-oriented
model
describing
species response
to climatic
variables, and
predicting
climatic
suitability.
Climate patternmatching
procedure
Windows 2000,
XP
P
Sutherst et al
2007
R
Sutherst et al
2007
Climate patternmatching using
a point-to-point
similarity index
Windows 95/NT
R
Carpenter et
al (1993);
CIFOR
(1996)
The EcologicalNiche Factor
Analysis
(ENFA)
computes
suitability
functions by
comparing the
species
distribution in
the
ecogeographical
variables (EGV)
space with that
of the whole set
of cells using a
multivariate
approach.
Principal
components
analysis of
monthly climate
data using
multivariate and
Fourier
transformation
techniques
Part of
Biomapper
software,
Windows (most
versions)
R
Hirzel et al.
2002
Windows
R
Jones and
Gladkov
(1999)
Generates
environmentdescription rules
using machinelearning
techniques
General
statistical
procedure for
fitting species
response
functions to
survey data
Web (UNIX)
R
Boston &
Stockwell
(1994)
Not applicable
R
Austin &
Meyers
(1996)
GRASP
HABITAT
MaxEnt
A regression modeling
is used to establish
relationships between a
response variable and a
set of spatial predictors
To tightly define the
environmental envelope
of a species or other
biotic entity and to
predict the
environments in which
it may be present
To predict species
distribution
NAPPFAST
A tool for phytosanitary
risk mapping.
Regression Tree Analysis
A general statistical
procedure to analyse the
environmental
correlates of species
distributions
STASH
To describe the present
and natural distribution
of northern Europe's
major tree species
Generalized
Regression
analysis and
Spatial
Prediction
Creates a
convex polytope
in n-dimensional
space
MS Windows
PC
R
Lehman et al.
2002
PC
R
Walker &
Cocks (1991)
Machine
learning
technique based
on the
distribution of
maximum
entropy
On-line
templates for
phenology,
infection and
empirical
models. Simple
climate
matching tool
General
statistical
procedure for
defining set
membership
based upon
environmental
correlates
Process-oriented
model
describing
species response
to climatic
variables, and
predicting
climatic
suitability
Java based
R
Phillips et al.
2006
Internet explorer
P or R
Magarey et el.
2007
Not applicable
R
UNIX; though
could be run on
any system
running
FORTRAN
P
Sykes et al.
(1996)
Annex 2: Mapping the potential distribution of insect species: what is the best
practice?
Christelle Robinet, Alain Roques & Sylvie Augustin
INRA, UR633 Zoologie Forestière, F-45166 Olivet, France
1- The use of distribution models
Understanding the impact of climate change on species distribution has become one
of the major challenges nowadays among ecologists. Field and lab experiments can
be conducted to determine the climatic range a particular species, but the ultimate
approach consists in developing models and determining bioclimatic envelopes or
climate response surfaces. Some studies about the effects of climate change on the
species distribution analyses the projection of this envelope in the future. However,
some questions should be addressed first: how reliable are these results? How can we
select the best model?
Some basic choices should be made to develop a model (Beaumont et al.
2007):
a) choice of the bioclimatic model (for instance CLIMEX, GARP, GAM,… see
Manel et al. 1999, Guisan & Zimmermann 2000, Kriticos & Randall 2001, Guisan &
Thuiller 2005, Elith et al. 2006, Lawler et al. 2006, Pearson et al. 2006);
b) choice of predictor variables (e.g. temperature, precipitation or non climatic
factors such as land cover, see Peterson & Cohoon 1999, Beaumont et al 2005,
Heikkinen et al 2006), and
c) choice of the climate scenario (including the emission scenario, the climate model
or even an idealized scenario, e.g.: +3°C) (Meehl et al. 2007).
Bioclimatic models are the primary tools for simulating the impact of climate
change on species distribution (Beaumont et al 2007). Uncertainty in the climate
predictions can result from: (1) uncertainty in the climate scenario (to overcome this
problem, we should test many scenarios); (2) variability in each climate scenario (we
should run each climate model multiple times). Generally only the first uncertainty is
considered but Beaumont et al (2007) has clearly proved that the second uncertainty
could be even greater and affect considerable the predicted distribution of nine
Australian butterfly species when using BIOCLIM model. Several climate scenarios
and several realizations of each scenario are required to determine the range of
projected distribution in the future.
Statistical techniques used to determine the climate envelope tend to select
arbitrary meteorological variables that are not necessarily associated with biological
processes. Temporal resolution of these variables might be also arbitrary, eg.
temperature of the coldest month (Zalucki & Furlong 2005).
The nature of occurrence data may also be important: presence-absence
models are more accurate than presence-only models (Elith et al. 2006, Beaumont et
al. 2007). In case of species interactions, simple presence-absence datasets are not
exhaustive enough, and occurrence data of each species under different environmental
conditions are required.
The best approach is generally to compare different models and test different
scenarios to obtain a confidence range of the potential distribution, but it is
unfortunately not often the case in most of studies and the choice for a certain model
is not always justified in papers, especially about insect species. Few studies really
assess the performance of the models developed and even fewer assess the difference
in performance of different models. Even in that case, there is generally a strong bias
because authors initially support a particular model, and thus analysis is not
completely objective. Across various taxa, there are some good comparisons (e.g.
Kriticos & Randall 2001, Elith et al. 2006), but across insect species or more
generally terrestrial invertebrate species they are not frequent.
Different indexes of model performance can be calculated for validation. First
we can summarize this performance in a confusion matrix (Table 1), with a the
number of true positive values, b false positives, c false negatives, and d true
negatives. Based on these values, we can then derive other performance measures
such as: correct classification rate, misclassification rate, sensitivity, specificity, odds
ratio, positive and negative predictive power, normalized mutual information statistic,
and Cohen’s kappa (Fielding & Bell 1997; Manel et al. 2001).
ACTUAL
+
PREDICTED
Table 1. Confusion matrix.
+
a
c
b
d
In addition to the choice of the index performance, there is also a choice about
the dataset. Although the same dataset can be used for calibration and validation of
the model (method called ‘resubstitution’), it is generally preferable to use
independent datasets (Sutherst & Maywald 1985). Since it is not common to have
several independent datasets (e.g. occurrence data in different regions), the data
should usually be partitioned into a calibration dataset and a validation dataset
(Fielding & Bell 1997, Araújo et al. 2005, Ulrichs & Hopper 2008). Also, one data
point can be discarded in the calibration dataset and the remaining dataset sequentially
used for the calibration dataset; this method is called a jackknife sampling or LeaveOne-Out.
Despite all these available methods to evaluate the success of a species
distribution mapping, this validation part is generally neglected and only few studies
on insect species actually provide a comparison among different models (e.g. Ward
2007) because papers focus more on the interpretation of the projection results.
Here we review insect case studies for which a climate response surface has
been developed in order to determine the best practice(s) for mapping the potential
distribution. Insect species are particularly interesting because distribution of most
poikilothermic animals is determined by climate (Andrewartha & Birch 1954, 1984).
Although we focussed on insect species, we sometimes refer to other studies in the
discussion that we believe bring a general point of view on this subject and thus could
be interesting also for mapping the potential range of insect species. In this case, we
clearly mention that the reference concerns other species. In a second part, we review
the models developed for mapping the distribution of the pine processionary moth.
Performance of bioclimate envelope models is generally criticized because of
three reasons: they ignore biotic interactions, evolutionary changes, and dispersal
abilities (Davis et al. 1998; Pearson & Dawson 2003). There is also an assumption
rarely verified: the species’ distribution is supposed to be relatively stable and in
equilibrium with its surrounding environment (Sutherst & Maywald 2005 is one
exception). In case this assumption is not verified, stochastic dynamic models should
be used instead of response surfaces (Guisan & Zimmermann 2000).
There is also a debate about whether spatial distribution models define a
species fundamental niche or realized niche (Kriticos et al. 2007). Guisan & Thuiller
(2005) state that most of the literature assumes, without proper evidence, that spatial
models represent the realized niche of the species, because their observed
distributions are already constrained by biotic interactions and limiting resources.
2- Review of case studies
Review description
We have reviewed 53 papers (grouped into 48 case studies) dealing with modelling
the potential distribution of insect species, published between 1985 and 2008. Table 2
is a synthetic summary of the papers reviewed. This is not an exhaustive list of papers
dealing with climate response surface. For instance, for the Climex model, you can
obtain a non-exhaustive but more complete list in the 2007 user’s guide (Sutherst et
al. 2007b). We focussed on the more recent studies when similar works have been
published previously by the same authors because we aim to examine the performance
of the current methods.
Most of insects studied were biological control agents. Others were mostly
invasive species (or potentially invasive). This review reports few bioclimatic model
developed on endangered species. There is only one case for which the species was
neither studied for biological control or invasion risk, but rather in terms of
conservation because of a low vagility (Stockman et al. 2006) in order to evaluate the
performance models for a species whose distribution was not completely known.
Various models have been used: ANN, BIOCLIM, CLIMEX, discriminant
function, DOMAIN, GAM, GARP, GLM, logistic regression, MAXENT (reviewed
by Kriticos and Randall 2001), but also some specific mechanistic models: diapause
model, ecophysiological model, life stage model and phenological model. Proportion
of classic statistical models represents 80% against 20% for specific models. Only
19% of the papers effectively compared (at least two) different models.
Surprisingly, most of the studies aim to determine the potential range
distribution of an invasive species but few of them (only 35%) try to determine or
include the effects of the climate change on the overall potential distribution.
Analysis and interpretation
Based on the papers reviewed (table 2), we aim to present a synthetic view on the
performance of the main models employed.
BIOCLIM is a correlative model often used to determine the effects of climate
change (Beaumont & Hughes 2002, Beaumont et al. 2007). Despite its usefulness, it
seems that other models such as DOMAIN and MAXENT perform better (Ward
2007).
Predictions of the CLIMEX model were quite successful (except for one case,
van Klinken et al. 2003), and the discrepancies could be explained by non climatic
factors (e.g. cattle resistance, Sutherst & Maywald 1985). Performance is usually
based on a visual inspection. See the next section for a deeper analysis of the
CLIMEX predictions.
GARP is a genetic algorithm for rule-set prediction, derived from ecological
niches of species that has dispersal capabilities, evaluating correlations between
distributional occurrences and environmental characteristics. Due to stochastic
elements in this algorithm, subsequent runs using the same data will produce slightly
different results. There are three problems: (1) a “black box” method: we cannot
explore the role of each predictor individually, (2) goodness-of-fit is seldom checked
with field samples, (3) problems with spatial resolution and selection of
environmental layers (Stockman et al. 2006). Other models such as BIOCLIM and
GLM seem to perform better.
The logistic regression performed a little better than the linear discriminant
analysis (Cumming 2000), and the discriminant analysis was as successful as the life
development stage model (Hunter & Lindgren 1995). The logistic regression was
particularly efficient with a predictive variable connected to the habitat suitability
(Hill et al. 1999, Warren et al. 2001).
Ecophysiological models, phenological models and life stage models are quite
successful and probably more robust than statistical models because they reflect the
underlying mechanism and they are not based on a correlation that may change with
climate warming, environmental change or more generally global change.
Nevertheless, rigorous comparisons between statistical and mechanistic models are
needed.
Sometimes, climate is not the main limiting factor and, in these cases,
predicting the species potential distribution is very difficult (eg. Samways et al. 1999).
Other factors can affect the species distribution such as a localized response to microclimate, host type and availability, presence of natural enemies.
3- The most frequent model
The CLIMEX model appears to be an effective tool and the most popular method for
predicting the potential distribution of poikilotherm species. This single model
represents approximately 50% of the reviewed papers on insect species. CLIMEX is
generally used to predict the suitability of a region for a species based on long-term
average climate, but it is also possible to determine the suitability of a site over the
years (e.g.: Zalucki & Furlong 2005). This approach is very useful to determine the
effects of extreme variation of climatic variables on the species abundance and
distribution. This computer program includes three separate modules: match climates,
compare years and compare locations. It combines both simulation modelling and
inference approach to determine the species’ response to climate. Growth and stress
indices (Table 3) are derived from weekly meteorological data, and an ecoclimatic
index EI is calculated ranging from 0 (if unsuitable area) to 100 (if optimal
conditions). Parameter values can be estimated directly by the model based upon
observations only, but they can also be estimated independently using physiological
data even if the CLIMEX model is particularly valuable when too little biological data
is available and the native distribution well known. This method is undoubtedly the
most frequent in mapping the range of insect species. Validation of this method is
however not performed in details in many cases since it is considered as one of the
most efficient models for insect species. Only visual inspection is often reported (e.g.
Sutherst & Maywald 2005; Poutsma et al. 2008), but an automated parameter fitting
procedure have been recently developed (Sutherst et al. 2007b).
In the papers reviewed here, some limitations of the CLIMEX model have
been reported: (1) the distribution of weather stations greatly affects the output of the
model; (2) natural weather variability and extreme climatic conditions may affect the
species distribution but only mean parameters’ value over 30 years are generally
considered; (3) the model ignores microclimates around rivers or irrigated areas; (4)
the model assumes that the species distribution is only determined by climate; and (5)
parameters are adjusted following an iteration procedure and results can be easily
manipulated (Sutherst & Maywald 1985, Worner 1988, Scott 1992, Davis 1998,
Baker et al. 2000, Poutsma et al. 2008).
Nevertheless these drawbacks could be easily minimized when compared to
other models because: (1) distribution of weather stations will affect any climate
response surface and, when available, additional weather data can be included in the
CLIMEX model; (2) understanding the effects of extreme climatic conditions has
become one of the major challenge in the future, whatever model is used; (3)
improvements have been made and, for instance, Sutherst et al. (2007a) considered
irrigation as a predictor variable; (4) CLIMEX projection is a first step of a more
detailed and realistic model: other layers such as resource distribution and other
processes such as competition should be considered. It is always necessary to identify
non climatic factors that could explain the species occurrence (Sutherst 2003). In fact,
discrepancies between observed and predicted distribution can help to identify these
limiting factors (Sutherst & Maywald 1985). Quite recently, Sutherst et al. (2007ab)
succeeded in including species traits directly in the CLIMEX model. They have
investigated the simultaneous effects of both climate warming and interaction among
species, and found that effects of species interaction could even exceed effects of
climate change. Menéndez et al. (2008) also gave evidence that, in the range
expansion, individuals could escape natural enemies and thus the limit of the
distribution could shift more rapidly than previously thought. However, climate alone
is usually a significant driver of the species distribution (Hodkinson 1999) and the
CLIMEX reliability seems more closely related to the data quality used in the model
than in the nature of the model itself (Sutherst & Maywald 1985). (5) Objective
approaches were not available to estimate the parameter values in the past,
furthermore small changes in parameter values do not change considerably the model
outputs (Worner 1988). In the last version of CLIMEX, an automating fitting
procedure has been implemented via a genetic algorithm (Sutherst et al. 2007b).
CLIMEX not only allows mapping the climatic suitability for a certain species,
but also allows broader applications: Peacock & Worner (2006) have determined
analogous climates of a certain location (Auckland, New-Zealand) in order to identify
potential sources of new invasive insect species.
4- Consideration of other factors
Dispersal
There are some attempts to consider in a simplistic way the dispersal ability in
addition of the response climate surface (e.g. unlimited dispersal, contiguous dispersal
or no dispersal, Peterson et al. 2002). Outside insect species, Araújo et al (2006)
found contrasting effects with or without dispersal: most amphibians and reptiles
could extend their distribution with climate change in case of unlimited dispersal
ability, but will probably loose range in case of no dispersal ability. Possibility of the
insect species to track the climate change also depends on the dispersal ability of the
larval host plant (Araújo & Luoto 2007). Considering even simple hypotheses can
help us to understand better whether the species would be able to track the climate
change or not. Based on climate envelopes, and various climate and dispersal
scenarios, Thomas et al. (2004) proposed three methods to calculate the proportion of
species committed to extinction as a function of estimated area lost. It seems that lifehistory traits could help us to determine whether a species is able to track the climate
change. For instance, Jiguet et al. (2007) found that natal dispersal but also annual
fecundity and the number of generations per year could inform about the birds’
sensitivity to a climate change.
Dispersal associated with the population dynamics also might be important. If
individuals are subject to Allee effects (a reduced population growth rate at low
densities), then the species may not be able to track the climate change if their
potential distribution is predicted to go through corridors and then enter a large
suitable area (Roques et al. 2008)
Interactions
Importance of interactions is perhaps one of the most discussed questions (see Davis
et al. 1998). For herbivore species, studying the interaction with its host tree or host
plant is crucial. They should stay in synchrony but climate change may alter
differently the phenology of each one and completely disrupt this relationship (van
Ash & Visser 2007). Araújo & Luoto (2007) also supported the idea that biotic
interactions could be important also at macroecological scales. Effects of species
interaction could even exceed effects of climate change (Sutherst et al. 2007ab).
However, Huntley et al. (2004) claimed that the performance of climate
envelope models did not depend on the taxonomic group nor to trophic levels.
Although these models are strongly based on a correlative approach (between the
species distribution and climatic factors) and consider individually each species with
no interaction, they are probably the best ones to study the effects of the climate
change. Authors recalled that most species interactions are generalist and not
specialist, and this is maybe the reason why these interactions have few effects on the
species distribution. As a result, it seems that biotic interactions drive certainly the
species range, but for a first assessment, assuming that climate is the main driving
factor seems quite reasonable.
Habitat suitability
Even though the distribution of many species is likely to expand in response to
climate warming, the species may not be able to track the climate change because of
other important factors such as habitat suitability. Hill et al (2001) used a spatially
explicit mechanistic model called MIGRATE to determine the impact of the
landscape structure on the species range expansion, and they have clearly
demonstrated that development of such models are important to understand in further
details the response of the species to climate warming in heterogeneous habitats.
Land transformation derived from the ‘Human Footprint’ can also be
considered in addition to common environmental factors generally used to determine
the potential distribution (Thuiller et al. 2006, for African mammals). Both climate
change and land transformation could affect the current species distribution, but also
the community composition (Thuiller et al. 2006, for African mammals), and thus
species interaction.
Climate variables and spatial scale
Climate response surfaces are important to determine a potential effect of a change in
climatic factors on the species distribution when such factors primarily govern the
species niche. Generally this is the case at large geographical scales where habitat
availability, local extinctions and colonisations, and adaptability have minor effects
(Berry et al. 2002). For instance, at the European scale, land cover is mostly
correlated with climate and it is interesting to include only the variables weakly
explained by the climate in the model to improve its performance (Thuiller et al.
2004). When the geographical scale considered is restricted, and the landscape quite
fragmented, simple logistic regression on land cover data can determine successfully
the species distribution using no weather data as predictor (Cowley et al. 2000). Thus
the spatial scale may also interfere with the results. The same argument is true for
other factors such as species interaction: for ants, climatic drivers appear at large
scale, whereas the species distribution at small scale is mainly driven by microhabitat
specialisation and competition (Hölldobler & Wilson 1990).
The global scale disturbances of El Niño Southern Oscillation (ENSO)
influence insect migration when these disturbances result in exceptional rainfall in
semi-arid regions and lead to large populations of migratory insects (Drake & Farrow
1988). Unfortunately to show any relationship between abundance and such
predictors, long series abundance data for the species are required (Zalucki & Furlong
2005).
Finally, it seems that more realistic dispersal abilities, species interaction and
population dynamics should be included in distribution models in the future (Guisan
& Thuiller 2005), and we should test whether they bring important supplementary
information and increase the model performance or not.
5- Conclusion on this review
In conclusion, many distribution models are available but few comparisons have been
really made. Generally, modellers are very confident in their model and only show
the advantages. Drawbacks are rarely discussed if ever mentioned. For insect species,
the most popular model is undoubtedly the CLIMEX model, all the more that
improvements have been made recently (Sutherst et al 2007b). CLIMEX was
effectively developed for insect species and its drawbacks are reduced with the recent
improvements. Ecophysiological models are usually better to determine the
underlying mechanism, but they should be calibrated with consistent data. In contrast
to statistical models, their goodness-of-fit is probably higher when environmental
factors take unusual values. Models based on mechanistic understanding should be
more robust than purely statistical models because correlations may vary with climate
change (Pearson & Dawson 2003). Although these mechanistic models are
particularly appealing for well known and well documented species, they often
require too many data to be a general approach (Guisan & Thuiller 2005).
The model performance seems to rely mainly on the data quality and the rigor
of the method employed for any model. Observing some discrepancies can help to
identify a missing factor, and then improve the model performance.
6- Case study: the pine processionary moth
The pine processionary moth is an insect species native to Mediterranean countries.
For a few decades, the species has expanded its distribution in higher latitudes and
higher elevation (Battisti et al 2005).
Model 1: exclusion map
Huchon and Démolin (1970) developed the first distribution model for the pine
processionary moth. Based on correlative observations not clearly determined, they
defined the presence threshold by a combination of the mean minimum temperature in
January (TNJ, °C) and the cumulative annual sunshine (S, hours):
TNJ ≥ 0°C and S ≥ 1800
0 ≥ TNJ ≥ -4°C and S ≥ 1800 +100*(-TNJ)
This means that, outside the optimum area, 100 h of sunshine could compensate 1°C
below 0, but it cannot compensate more than 4°C below 0. The weather variables
used for this model were averaged from 1946 to 1960.
This exclusion model predicted that a large north part of France was unfavourable
for the pine processionary moth, and the species occurrence has been in agreement
with this exclusion map for many years.
Model 2: GAM model
Since the pine processionary moth invaded the south of the Paris Basin in the 1990s, it
was necessary to update this historical model and introduce the climate warming.
Since the cumulative annual sunshine is not a variable commonly used in climate
scenarios, we replaced this variable by the annual mean solar radiation (Wh/m²) and
used a generalized additive model (GAM). Correct classification rate was 83% for
1970-1980, but absence is incorrectly predicted for the ongoing years, even when
considering climate warming (Robinet et al. 2007).
Model 3: ecophysiological model
To understand in-depth the impact of climate warming on the species survival, lab and
field experiments have been conducted and we found that temperature could affect the
larval feeding activity and thus the larvae survival. Indeed two conditions should be
satisfied for the feeding: temperature inside the nest during the day should reach 9°C,
and then during the following night, air temperature should be above 0°C (Battisti et
al. 2005). If both conditions area satisfied, then larvae can go out of the nest during
the night and feed on the needles. If not, larvae do not feed and, in some cases,
starvation occurs. The feeding activity could explain a large part of the colony
survival. Therefore, we modelled this mechanism and calculated the mean number of
feeding days and the longest period of starvation (Robinet et al. 2007). There was an
unfavourable area in the South of the Paris Basin in the 1992-1996, which vanished
during 2001-2004. This change in the potential feeding activity can explain the
spectacular expansion observed in this region since then.
Model 4: diffusion model
Since habitat distribution and dispersal ability was not included in the previous model,
this ecophysiological model was then simplified and coupled to a growth model and a
diffusion model (Robinet 2006; Robinet et al. 2008). A climate scenario (regionalized
scenario B2 – scenario ARPEGE-Climat from Météo-France) was also considered.
The model was validated at larger temporal and spatial scales. It predicted retraction
of the distribution during cold winters in the past and a continuous expansion since
the late 1990s. Based on this model and hypotheses (climate scenario, dispersal
ability of 3 km/year), the pine processionary moth could reach Paris by 2025. This
model describes only the natural range expansion of the population, but quite recently,
some isolated colonies have been discovered far from the current distribution,
probably inadvertently transported by humans (Robinet et al. 2008). Thus, longdistance dispersal models should now be developed.
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