Ecological Niche Modeling: Ecological Niche Modeling: A tool set to assess distributional A tool set to assess distributional

patterns in biodiversity and patterns in biodiversity and pathogenspathogens

based onbased on

Jane Costajcosta@ioc.fiocruz.br

Instituto Oswaldo Cruz, FiocruzRio de Janeiro, Brasil

Townsend Peterson town@ku.edu

University of Kansas, Lawrence, Kansas, USA Emerging Infectious Diseases,

12, December, 2006

What is ecologic niche What is ecologic niche modelingmodeling

The idea is that known occurrences of species The idea is that known occurrences of species across landscapes can be related to digital raster across landscapes can be related to digital raster GIS coverages summarizing environmental GIS coverages summarizing environmental variation across those landscapes to develop a variation across those landscapes to develop a quantitative picture of the ecologic distribution of quantitative picture of the ecologic distribution of the species.the species.

ENM characterizes the distribution of the species ENM characterizes the distribution of the species in a space defined by environmental parameters, in a space defined by environmental parameters, which are precisely those that govern the species' which are precisely those that govern the species' geographic distribution under Grinnell's definition geographic distribution under Grinnell's definition of ecological niches. of ecological niches.

The set of environmental The set of environmental conditions, resources, conditions, resources, interactions, etc., in which a interactions, etc., in which a species is able to maintain species is able to maintain populations without populations without immigrationimmigration

Ecological Niche ConceptEcological Niche Concept

project

Hypothetical example of a species' known occurrences (circles) and inferences from that information          

GarpGarp

GARP is a genetic algorithm that creates ecological niche models for species. The models describe environmental conditions under which the species should be able to maintain populations. For input, GARP uses a set of point localities where the species is known to occur and a set of geographic layers representing the environmental parameters that might limit the

species' capabilities to survive.

Geographic Space Ecological Space

occurrence points on native distribution

ecological niche modeling

Projection back onto geography

Native range prediction

Invaded range prediction

Essence of Ecological Niche Essence of Ecological Niche ModelingModeling

temperature

Model of niche in ecological dimensions

pre

cip

itatio

nNote that ENM applications such as GARP can show

excellent predictive ability for quite small samples

The applications of ENM

Here is outlined what the Here is outlined what the technique has to offer to the field.technique has to offer to the field.

The applications of ENM 1-Understanding Ecology of Diseases1-Understanding Ecology of Diseases

In many cases, the details of ecologic parameters In many cases, the details of ecologic parameters associated with occurrences of diseases or of species associated with occurrences of diseases or of species participating in disease transmission (e.g., vectors, participating in disease transmission (e.g., vectors, hosts, pathogens) may be unclear because of small hosts, pathogens) may be unclear because of small sample sizes, biased reporting, or simply lack of sample sizes, biased reporting, or simply lack of detailed geographic or ecologic analysis. detailed geographic or ecologic analysis.

ENM encompasses a suite of tools that relate known ENM encompasses a suite of tools that relate known occurrences of these species or phenomena to raster occurrences of these species or phenomena to raster geographic information system layers that geographic information system layers that summarize variation in several environmental summarize variation in several environmental dimensions.dimensions.

The applications of ENM 1-Understanding Ecology of Diseases1-Understanding Ecology of Diseases

The result is an objective, quantitative picture The result is an objective, quantitative picture of how what is known about a species or of how what is known about a species or phenomenon relates to environmental phenomenon relates to environmental variation across a landscape. variation across a landscape.

Studies using these approaches include an Studies using these approaches include an examination of ecologic differences among examination of ecologic differences among different Chagas disease vectors in Brazil and different Chagas disease vectors in Brazil and a characterization of ecologic features of a characterization of ecologic features of outbreaks of hemorrhagic fever caused by outbreaks of hemorrhagic fever caused by Ebola and Marburg virusesEbola and Marburg viruses

Am. J. Trop. Med. Hygiene 67:516-520The The Triatoma brasiliensisTriatoma brasiliensis

species complexspecies complex

Ecological similarity matrix among populations based on the ability of the model for one population to predict the distribution of another

br me ma juPredicted br 0.98 0.00 0.44 0.51

me 0.38 0.85 0.10 0.21ma 0.87 0.00 1.00 0.87ju 0.87 0.00 0.76 0.93

Predictor

The applications of ENM 2- Characterizing Distributional Areas2- Characterizing Distributional Areas

ENM is used to investigate landscapes for areas ENM is used to investigate landscapes for areas

that meet the ecologic requirements of the that meet the ecologic requirements of the

speciesspecies

The result is an interpolation between known The result is an interpolation between known

sampling locations informed by observed sampling locations informed by observed

associations between the species and associations between the species and

environmental characteristics.environmental characteristics.

The applications of ENM 2- Characterizing Distributional Areas2- Characterizing Distributional Areas

ENM produces statistically robust predictions ENM produces statistically robust predictions of geographic distributions of species or of geographic distributions of species or phenomena (even in unsampled areas), phenomena (even in unsampled areas), greatly exceeding expectations under greatly exceeding expectations under random (null) models. Numerous examples of random (null) models. Numerous examples of applications of this functionality to disease applications of this functionality to disease systems have been publishedsystems have been published. .

The applications of ENM 3- Identifying Areas of Potential Invasion in 3- Identifying Areas of Potential Invasion in other Regionsother Regions

ENMs characterize general environmental regimes ENMs characterize general environmental regimes

under which species or phenomena may occur. under which species or phenomena may occur.

To the extent that the model is appropriately and To the extent that the model is appropriately and

correctly calibrated, it may be used to seek areas of correctly calibrated, it may be used to seek areas of

potential distribution. potential distribution.

Thus, ENMs can be used to identify areas that fit the Thus, ENMs can be used to identify areas that fit the

ecologic bill for a species, even if the species is not ecologic bill for a species, even if the species is not

present there.present there.

The applications of ENM 3- Identifying Areas of Potential Invasion 3- Identifying Areas of Potential Invasion in other Regionsin other Regions

This approach has seen extensive

experimentation and testing in the

biodiversity realm, but applications to

disease transmission have as yet been have as yet been

few. few.

The applications of ENM 4- Anticipating Risk Areas with Changing 4- Anticipating Risk Areas with Changing ClimatesClimates

A logical extension of using ENMs to identify potential A logical extension of using ENMs to identify potential

distributional areas is to address the question of likely distributional areas is to address the question of likely

geographic shifts in distributional areas of species or geographic shifts in distributional areas of species or

phenomena under scenarios of climate change or changing land phenomena under scenarios of climate change or changing land

use.use.

This approach has seen considerable attention in the This approach has seen considerable attention in the

biodiversity realm, with both tests and validations, and with biodiversity realm, with both tests and validations, and with

broad applications across faunas and floras. In the disease broad applications across faunas and floras. In the disease

world, applications have been few, although 1 study used likely world, applications have been few, although 1 study used likely

climate change–mediated range shifts to hypothesize the climate change–mediated range shifts to hypothesize the

identity of identity of LutzomyiaLutzomyia vectors of recent leishmaniasis outbreaks vectors of recent leishmaniasis outbreaks

in southern Brazil.in southern Brazil.

The applications of ENM 5- Identifying Unknown Vectors or Hosts5- Identifying Unknown Vectors or Hosts

ENM approaches can be applied to various parts of disease transmission ENM approaches can be applied to various parts of disease transmission

cycles (e.g., overall case distribution, reservoir host distribution, vector cycles (e.g., overall case distribution, reservoir host distribution, vector

distribution) to identify unknown elements in systems. distribution) to identify unknown elements in systems.

The geography of overall case distributions can provide an indication of The geography of overall case distributions can provide an indication of

which clades are potential reservoirs and which are not. A first which clades are potential reservoirs and which are not. A first

application was an attempt to identify mammalian hosts of the application was an attempt to identify mammalian hosts of the Triatoma Triatoma

protractaprotracta group of Chagas disease vectors in Mexico, which succeeded group of Chagas disease vectors in Mexico, which succeeded

in anticipating the mammal hosts of 5 of 5 species for which a test was in anticipating the mammal hosts of 5 of 5 species for which a test was

possible. possible.

Further exploration of this possible application of ENM methods has Further exploration of this possible application of ENM methods has

focused on the mysterious long-term reservoir of the filoviruses (Ebola focused on the mysterious long-term reservoir of the filoviruses (Ebola

and Marburg viruses) by comparing African mammal distributions with and Marburg viruses) by comparing African mammal distributions with

those of filovirus-caused disease outbreaks.those of filovirus-caused disease outbreaks.

DiscussionDiscussion1-Current Challenges in ENM1-Current Challenges in ENM

ENM, although it has old roots, is nonetheless a relatively ENM, although it has old roots, is nonetheless a relatively new tool in distributional ecology and biogeography. As such new tool in distributional ecology and biogeography. As such numerous challenges remain in terms of refining approaches numerous challenges remain in terms of refining approaches toward a more powerful and synthetic methodology. toward a more powerful and synthetic methodology.

To improve the of ability to interpolate accurately versus To improve the of ability to interpolate accurately versus ability to extrapolate effectively remains a challenge for the ability to extrapolate effectively remains a challenge for the ENM methods. ENM methods.

A second frontier that includes yet-to-be-resolved details for A second frontier that includes yet-to-be-resolved details for ENM is that of testing and evaluating model results. Currently ENM is that of testing and evaluating model results. Currently accepted approaches center on the ability to predict accepted approaches center on the ability to predict independent test occurrence data in the smallest area independent test occurrence data in the smallest area predicted. However, efficient predictions can be poor predicted. However, efficient predictions can be poor descriptors of a species' geographic range descriptors of a species' geographic range

DiscussionDiscussion2-Current Challenges in 2-Current Challenges in Applications of ENM to Disease Applications of ENM to Disease SystemsSystems The first, and perhaps most important, is The first, and perhaps most important, is

understanding the role of scale in space and time. understanding the role of scale in space and time.

Preliminary explorations suggest that proper matching Preliminary explorations suggest that proper matching

of temporal and spatial scales in analyses may offer of temporal and spatial scales in analyses may offer

particular opportunities for precise and accurate particular opportunities for precise and accurate

prediction of the behavior of disease phenomenaprediction of the behavior of disease phenomena

Similarly, proper choice of environmental datasets Similarly, proper choice of environmental datasets

requires further exploration. requires further exploration.

DiscussionDiscussion2-Current Challenges in 2-Current Challenges in Applications of ENM to Disease Applications of ENM to Disease SystemsSystems Climate data provide longer temporal applicability, but Climate data provide longer temporal applicability, but

remotely sensed data that summarize aspects of remotely sensed data that summarize aspects of

surface reflectance can provide finer spatial surface reflectance can provide finer spatial

resolution, and may measure aspects of ecologic resolution, and may measure aspects of ecologic

landscapes that climate parameters alone may not landscapes that climate parameters alone may not

capture capture

Finally, because disease transmission systems often Finally, because disease transmission systems often

represent complex interactions among multiple represent complex interactions among multiple

species (e.g., vectors, hosts, pathogens), options exist species (e.g., vectors, hosts, pathogens), options exist

for how they should be analyzed and modeled. for how they should be analyzed and modeled.

ConclusionsConclusions

ENM can solve several problems of spatial resolution of ENM can solve several problems of spatial resolution of

summaries of geographic risk for disease. summaries of geographic risk for disease.

ENM is in the early stages of being explored for its potential ENM is in the early stages of being explored for its potential

for illuminating unknown phenomena in the world of disease for illuminating unknown phenomena in the world of disease

transmission. transmission.

The extensive explorations of ENM in the biodiversity field, The extensive explorations of ENM in the biodiversity field,

however, serve as a benchmark of quality and acceptance however, serve as a benchmark of quality and acceptance

for the technique for the technique

THANKS!THANKS!