Use and limitations of ecological niche prediction based ... · PDF fileUse and limitations of...

Use and limitations of ecological

niche prediction based on species

distribution modeling

Silvana Amaral

Symposium in Modelling of Terrestrial Systems and Evolution UFOP, May 10-13, 2011

Ecological Questions:

Assessing spp invasion and

proliferation

Suggesting unsurveyed sites of high

potential of occurrence for rare

species

Management plans for species

recovery and mapping suitable sites

for species reintroduction

Conservation planning and reserve

selection

Impact of CC, LUCC and other

environmental changes on species

distributions

Quantifying the environmental

niche of species

Testing biogeographical, ecological

and evolutionary hypotheses

Modelling species assemblages

(biodiversity, composition) from

individual spp predictions

Building bio- or ecogeographic

regions

Improving the calculation of

ecological distance between

patches in landscape meta-

population dynamic and gene flow

models

cervo-do pantanal

(Blastocerus dichotomus)

Caramujo gigante africano

(Achatina fulica)

What are SDMs and how do they work?

Environmental niche modelling, ecological niche modelling, fundamental niche modelling, or niche modelling

SDMs – Species distribution models are empirical models relating field observations to

environmental predictor variables, based on statistically or theoretically derived

response surfaces (Guisan & Zimmermann 2000).

Dados das espécies: presença, presença-ausência, observações de abundância a partir de

amostragem de campo aleatória ou estratificada, ou oportunistas – coleções

Preditores ambientais – efeitos diretos ou indiretos: Fatores limitantes (reguladores): controlam eco-fisiologia (temp, água, solo)

Distúrbios: perturbações (naturais ou antropogênicas) no ambiente

Recursos: todos componentes assimiláveis (energia, nutrientes, água)

Padrões espaciais diferenciados conforme a escala, hierarquicamente:

Distribuição gradual –grande extensão e resolução grosseira– controle por reguladores climáticos

Distribuição agrupada – pequena área e resolução fina – controle por distribuição agrupada de recursos (variação micro-topográfica ou fragmentação de habitat)

–

The process of Species Distribution Modeling

Geographical coordinates

Occurrence Poitns

precipitation

topography

temperature

Environemntal Variables

Predictive Distribution

Specie Distribution Model

Alg

ori

thm

Source: modified from Siqueira (2005)

Ecological Niche Modelling

The Niche concept

Abiotic niche

Biotic interactionsAccessibility

Area presenting appropriate combinations of abiotic and biotic conditions (= potentialdistribution)

Actual geographic distribution(abiotic and biotic conditions fulfilled,accessible to dispersers)

COMPONENTS:


The niche concept

Grinnell : a spatial unit

Elton: sp function in the trophic web

Hutchinson:

the ways in which tolerances and requirements interact to define

the conditions and resources needed by an individual or a

species in order to practice its way of life.

J.Grinnell1877 - 1939

C. Elton 1990-1991

G.E. Hutchinson1944-1958

Hutchinson (1957)

Diana Valeriano, Depto Ecologia - USP


Niche: defined as the sum of all the environmental factors

acting on the organism, is a region of a hyper-n-

dimensional space ... "(1944).G.E. Hutchinson1944-1958

Ecological Space

Temperature

Mo

istu

re

Considering all variables Xn, (physical and biological ) the FUNDAMENTAL niche of any sp will define its ecological properties.

The fundamental niche is a mere abstract formalization from the ecological niche (1957).


Usually, conditions where an organims (sp or population)

can persist (survive and reproducing) are wider than the

conditions in which the organism lives.

This reduction is due to biotic interactions !


Hutchinsonian niche

Fundamental Niche – every factors in the hiper-volumen of n-dimension, considering the

absence of other spp

Realized Niche – part of fundamental niche considering inter-specific interactions.


Temperature

Mo

istu

re

Realized Niche

Fundamental Niche

Fitness (for organisms) or

Growth population rate (populations)

Out of this limit = 0

- “Realized niches do not intersect” (1957) – excluding competition is part of the concept

- Niche is a property of the occupant and not of the environment

- Niche has a temporal dimension, Niche changes, evolutes !

- Niche can be quantified: environmental variables are continuous axis; niche internal structure depends on the sp performance that can be measured by the population fitness


Simplification:

Based on the observed distribution (occurrences) SDMs quantifies the

realized niche of Hutchinson

Realized Niche is replaced by Potential Niche

Potential Niche: defined as part of fundamental niche available for the sp and restricted by realized environment (Ackerly, 2003)

The study site does NOT contain all the possible variables combination to explore the sp gradient

Abiotic Niche

Biotic InteractionsAccess

Initial Situation

Abiotic Niche


Specie invasion

Modelling can be useful …



Simplification:

Based on the observed distribution (occurrences) SDMs quantifies the

realized niche of Hutchinson

Realized Niche is replaced by Potential Niche

Potential Niche: defined as part of fundamental niche available for the sp and restricted by realized environment (Ackerly, 2003)

The study site does NOT contain all the possible variables combination to explore the sp gradient

Abiotic Niche


Initial Situation

Modelling can be useful …

Abiotic Niche


Climate ChangesDiana Valeriano, Depto Ecologia - USP


Sp occurrence – geographical coordinates of individual records

Niche points – environmental variable values for the occurrence points

Niche model – occurrence probability function for the specie

Potential Distribution mapping: applying the niche model over a geographical region to obtain a geo-refereced map with the potential niche

Niche modelling Environmental variables (other area/time)

Model Projection

Potential Distribution Mapping

Algorithm

Elisangela S. C. Rodrigues (2010)


SDMs – Species distribution models are empirical models relating field

observations to environmental predictor variables, based on

statistically or theoretically derived response surfaces.

SDMs – Ideally 6 steps:

1. Formulation

2. Data preparation

3. Model Fitness

4. Model Evaluation

5. Spatial Predictions

6. Model usefulness

(Guisan & Zimmermann, 2000)

1. Formulation

Assumption - the Equilibrium postulate

Environmental data and species refers to a time and space

sampling

Models are snapshots of spp x environment relations

Postulate: modelling spp are in pseudo-equilíbrium with their

environment

BUT:

Is the environment in equilibrium?

How long would take to be back in equilibrium after disturbed?

Invasive spp are not in equilibrium, they must be modeled from the native distribution

1. Formulation

Equilibrium

Important for general scale model

OK for persistent spp

Advantage: less physiology and behavior knowledge

Disadvantage: human influence, disturbs and successionalprocess are not included (individual behaviour, dispersion, migration, plasticity, adapatation, etc)

Static Models - consider equilibrium or pseudo-

equilibrium

Non-equilibrium: more realistic, BUT model should be dynamic and stochastic

Alternative – Dynamic simulation modelling Difficulty: knowledge about sp and environment relation, datasets

Modelling for what?What is the scientific question???

2. Data Preparation




Species occurrence data:

Presence, presence-absence, abundance; from field sampling or opportunistic - collections

Environmental predictors – direct or indirect effects

Limitation Factors (regulators): controlling eco-physiology (temp, H2O, soil)

Disturbances: disturbances (natural or anthropogenic) in the environment

Resources : any assimilable component (energy, nutrients, H2O)

Different spatial patterns according to the scale, hierarchically:

2. Data Preparation

Sampling and Data

Spatial Scale

Explicative variable (physiology) for preditive modelling

Sampling Desingn – GRADIENT Gradsect – (Gradient-Oriented Transect (Gradsect) Sampling)

Random-Stratified – random/sistematic sampling in homogeneus enviroment

Gradsect – for sp richness betterthan just sistematic or random

Data collected based on observation=> should samplie fixed sub-set fixo/ environmental estratum

Auto-correlation analysis to identifymimunum distance between samples

Specie occurence

Point Data

Latitude and longitude (cartography)

Spp identification (taxonomy)

Collection / project specialist inventories

Restrict area (usually small)

Sampling design – not planned for modelling

Geographical position can be imprecise

Data Availability– politics ("my data")

Interesting initiative -Biota-FAPESP and MCT

Data from Biological Collections and Herbarium

Specie occurence

Data from Scientific Herbaria

Classification System

Family, gender and specie

Appropriate storage

Suitable environmental conditions

Maria Cândida H. Mamede - IBt

Excicata

Herborization

Sp distribution – time and space

Flora from conserved disturbed areas

Taxonomic and phylogenetic studies

Precise sp identification (curator)

Associated collections

Specie occurence

Lable – informations – Geo-reference

Type


Specie occurence

Lable – informations – Geo-reference


Specie occurence

Database –

Data Cleaning

Taxonomic

Collections

Geographic UTM

GPS

DMS, ....

precision

http://splink.cria.org.br/tools?criaLANG=pt

Data &Tools

2. Data Preparation




Species occurrence data:

Presence, presence-absence, abundance; from field sampling or opportunistic - collections

Environmental predictors – direct or indirect effects

Limitation Factors (regulators): controlling eco-physiology (temp, H2O, soil)

Disturbances: disturbances (natural or anthropogenic) in the environment

Resources : any assimilable component (energy, nutrients, H2O)

Different spatial patterns according to the scale, hierarchically:

Gradual Distribution – large extend, corse resolution climatic regulators

Disperse Distribution – small area and fine resolution – controled by cluster resource distribution

(micro-topographic variation or habitat fragmentation)

SDM – limiting factors x sccale

–

Environmental Information

Field work, systematic mapping, remote sensing and GIS model resultant

DEM – important because of correlation with other variables. Precision. However low Predictive power

Topographic gradient can be useful to verify the correspondence between digital attributes and field data

TASK: select the adequate dataset to parameterize the model.

??? How selecting the predictive variables???

Based on the specialist knowledge of the group: minimum environmental requirements

Statistical procedures to select the predictive variables

stepwise for LS, GLMs and CCA

Jackknife, etc.

Data Preparation

Environmental variables

Usually selected – regional scales (!)

Climatic

Topographic

Soil

RS...

To make SDMs feasible...

AMBDATA

Variáveis Ambientais para Modelagem de Distribuição de Espécies

Departamento de Processamento de Imagens – DPI/INPE

Grupo de Modelagem para Estudos da Biodiversidade

Environmental variables commonly used for MDE

BRAZIL and Brazilian Legal Amazon (bbox)

Data for download with references/metadata

Available at www.dpi.inpe.br/Ambdata

To make SDMs feasible...

AMBDATA

Variáveis Ambientais para Modelagem de Distribuição de Espécies

Departamento de Processamento de Imagens – DPI/INPE

Grupo de Modelagem para Estudos da Biodiversidade

Variáveis ambientais normalmente usadas para MDE

Recorte BRASIL e Amazônia Legal

Dados para download com referências/metadados

Disponível em www.dpi.inpe.br/Ambdata

Statistical Model Formulation

Select an adequate algorithm to predict a certain responsevariable and estimate the model coefficients

Statistical models – most are specific to the probability distribution. Test: response variable statistical distribution

Generalized Regression

Relate a response variable to a single (simple) or a combination of (multiple) environmental variables (predictors)

Predictors - environmental var or derivate orthogonal components (avoid multi-collinearity) from multivariate analysis (PCs).

Classical regression (LR) - response variable has normal distributionand variance that does not change with the average (homoscedasticity)

3. Model Fitness

Generalized Regressions

GLMs – more flexible – response var. with other distributions and non-constant variance function

Linear Transformation

Predictions with values in a defined interval

Distributions: Gaussian, Poisson, Binomial or Gamma, and identity functions logarithmic, logistic e inverse

GAMs – Alternative regression: non-parametric functions to smooth the preditor Moving-averages – regression weighted by local or density functions weighted locally

Generalized Aditive Models – smoothes each predictor and calculates addictively the response variable; Multidimensional

Regression models can incorporate

ecological processes as dispersion or connectivity

Model Fitness

Classification Techniques

Classification trees (qualitative) and regression (quantitative) based on rules, or maximum likelihood.

It attributes a class for the response variable (binomial or multinomial) for every environmental predictors combination (nominal or continuous).

Model Fitness

Built from simple rules inter-relations from previous knowledge – literature, lab, observation, etc.

Environmental/climatic envelops – actual distribution x climatic

variables comparison envelop (hypercube) that describes the

environmental influence over specie variation

Climate Change scenarios simulation

Model Fitness

Elith & Leathwick, 2009

Environmental envelops

BIOCLIM – minimum retangular envelop in a multi-dimentional climatic space

HABITAT – more restrict space: convex hull envelops.

Model Fitness

DOMAIN – based on similarity metrics point-by-point (multivariate distance measures)

Environmental Distance (OM):

environmental dissimilarity distance;

Euclidian, Mahalanobis,Gower, etc.

Ordination techniques – spp or communities

Canonic Correspondence Analysis

Gradient direct analysis, principal ordination are linear combination of environmental data

Suppose Gaussian distribution for spp; lower and upper occurrence threshold and optimum along the gradient.

Very robust method. Adequate when absence data is abundant

Model Fitness

Bayesian approach

Combine a priori probability of a sp (community) observation with the probabilities related to every environmental predictor

Model Fitness

Conditional probability can be the rative frequency of a sp in a discrete class of a nominal preditor

P a priori can be based on literature

For vegetation mapping: P a posteriori is estimate for every vegetation unity

the unity with hishest P is associated to every candidate location

Neural Networks

Promissing tool : a few references for predicting sp space distribution based on biophysical variables

More powerefunn than miultivariate regression to model non-linear relations

Problem – non-parametric process of classification (“black art”)

Other approaches

ENFA – Ecological Niche-factor analysis –difers from CCA by considering one sp each time. Only presence data (animals).

MONOMAX –fits a monotonic max likelihood function iteractively

Discriminat function analysis

GIS Models – environmental overlay, variation metrics, similarity and rules to combine probabilites

Model Fitness

GARP

3

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Aldair Santa Catarina (2006)

GARP - Genetic Algorithm for Rule-set Production

Genetic Algorithm to predict potential specie distribution from

environmental and biological data;

Automatic and um-supervised method;

Robust: tests several solutions and models (rules);

Maximize the significance and accuracy of prediction rules.

Maxent Potential probability distribution for the entire area

Distr Prob ?


Sp occurrence points


Potential sp distribution estimate

Algorithm

Maxent

Data (no meaning ) = 25 - can be stored in the computer

Information (meaning associated) temperature = 25

(information can be represented by data)

Mean February max temperature = 25

二月份的平均最高氣溫 = 25

(this data represetation does not bring us any information)

Knowledge without knowing what is MEAN, the information does not

make any sense! (it depends on personal experience)

Entropy is a measure of disorder or predictability of a system. It

is a measure of uncertainty of an event

Unexpected observations have more information than expected observations !


Maxent

ENTROPY: is a measure of uncertainty of information

about the occurrence of an event

But if I have no previous idea, and an event occurs,

the entropy is greater because it brings me more information

It is s related to the probability of an event: the higher the probability

of an event, the lower the entropy.

If P is high the result is expected No information associated to the event.

If P is low It will be a asurprise! the event will bring information!

Maximum entropy: P is uniform.

If the dice has two sides #2, the entropy will be smaller because P(#2) will be

higher.

Uncertainty → surprise → information (or the entropy for an event, uncertainty)


Maxent

Entropy: uncertainty in an event, related to amount of information that

is transmitted when it occurs (“metric”)

Maximum entropy principle: from a set of possible probability

distribution, it must choose the Pdistr with present maximum entropy

i.e., that is most spread out, or closest to uniform, subject to a set of

constraints.

For that sp occurrence data and environmental layers a set of possible probability distribution

????

Choose/find the Pdist that provides more possible information – or the maximum entropy !

Probability Distribution


Maxent

Constraints=> represent the evidence, i.e. known facts about

input dataset. In our case: the environmental layers = features

X => geographic region

x1, x2,...,xn => observed/registered points

f1, f2,...,fn => features (layer values or a function of them )

Task: Having set points and layers, find the probability

distribution for this dataset:

Restrictons:

Features , evidences

(criteria about the layer values)

Probabilities Sum has to be = 1.

xn

x1

x2

x3

x4

Several possibleprobability distribuition


Maxent Maxent : estimate a target probability distribution by finding the probability distribution of

maximum entropy (i.e., that is most spread out, or closest to uniform), subject to a set

of constraints that represent our incomplete information about the target distribution.

There are several probability distribution that satisyes all the restrictions!!

(Then, the model is consistent and among then, it has to choose the one with contains maximum entropy!)

Find the weight for every features in a way that the result is the Max entropy

x1

x2

x3

x4

xn

The model express the adequacy of each cell as a function from environmental variables.

High maxent values for a cell indicates that it contains favorable condition for that specie! Prediction of favorable conditions!!!

From the model, projects to the geographical area, using environmental variables data set

http://www.cs.princeton.edu/~schapire/maxent/


SDM Modeling tools

R - statistical models (RL, GLM, GAM,…)

http://www.r-project.org/

openModeller (Bioclim, climate space model, envelope score,

environmental distance (metric), GARP, SVM, maxent)

http://openmodeller.sourceforge.net/

DesktopGarp

http://www.nhm.ku.edu/desktopgarp/

Maxent





http://openmodeller.sourceforge.net/

http://www.nhm.ku.edu/desktopgarp/


5. Predictions/ Model Results

Predict the potential sp distribution

Potential distribution maps OR cartographic representation of: Occurrence Probability ( GLMs logistics)

Likely Abundance (GLM ordinal)

Predicted Occurrence – non-probabilistic metric (CCA)

Likely Entity (from hierarchical analysis)

Occurrence Probability

6. Model evaluation

Validation –measure the adequacy between the predictcted model and

field observation (~accuracy for RS)

BUT Validation = formulation of the theory model

EVALUATION is better : not to verify if T or F, but for hypothesis testing and biological patterns prediction

Evaluation – as a measure of adequacy , dependent of the project

objectives and the application domain of the modeling

Usually :

Two independent datasets: training (to calibrate) and testing (evaluation)

Confusion matrix

Jack-knife, Cross Validation (CV) and/or bootstrap

6. Model evaluation Evaluation based on independent dataset

To calibrate and evaluate

split-sample – large dataset (Inadequate for small datasets)

Do not mix sampling and observational datasets

Confusion Matrix

Commission error are not errors from the model

Omission errors are serious!

Minimum predicted area:

predict smallest possible area of

sp potential occurrence

+ -

+ a b

- c dPredicted

Real

Omission Errors

Commission Errors

OmissionCommission

Real Geographical DistributionPredicted Geographical

Distribution

Iwashita (2007)

6. Model evaluation

JackKnife – Calculates confident intervals

Computed taking one observation off each time

Cross-validation – verifies the hypothesis if the result can be replicated

or it was just random

Part of dataset to calibrate the model and the other to test the error

Simple , double or Multi – repeats the pair several any times, selecting sub-samples

Bootstrap – addresses the deviation of the estimate performing

multiple re-sampling with replacement within the calibration dataset.

Obtaining an estimate without deviations.

Bias - difference between parameter estimate and the actual population value.

If the difference between the value obtained and corrected for deviation is very high, the adequacy of the model should be questioned

Model evaluation

Receiver operator characteristic (ROC-plot)

sensibility x especificity plot

Sensitivity is the probability

of a pixel x be correctly

classified as occurrence

Specificity is the probability of a

pixel be correctly

classified as absence

The closer to 1 is the area under

the curve - AUC, the better is

the model !

(BUT take care about the predicted area)

Research Perspectives

Environemtal Layers limitation

Accuracy and resolution from env data Remote sensing as additional data source. Precise

information about moisture, soil wetness, vegetation indices, land use class, etc.

Biotic Interactions Limitations

Competition – challenging.

Integrated systems of simultaneous regressions, GLMs?

Modeling system has to be close to equilibrium

From spp community modeling


Cause-effect Limitations

From Static models to space-temporal models??

Integration between eco-physiologist and succession dynamic modelers !

Historical Factors

Include biogeographical and evolutive factor into static SDMs. Place history

Sp absence in adequate environment – past geological or climatic events; physical barriers…

Organism history

Try to integrate evolution studies (phylogeny), population genetics, spp genetic integrity

Sampling design

Re-sampling strategies for modeling purposes, including environmental gradient


Space explicitly uncertainties evaluation

Include the uncertainties in the geographical space Important for model credibility and applicability;

Mapping of the uncertainties

Special auto-correlation

Auto-correlation and spatial variance of occurrence and environmental data

Include dispersion patterns in the modelling

Cellular automata

deal with neighborhood relations (spatial correlation) and dynamic

environments

Cells, their and transitions states –to model sp distribution of plants in climate change, simulation of migration of plants along corridors in segmented landscapes

SDMs – Remark

1. Formulation

2. Data Preparation

3. Model Fitness

4. Evaluation

5. Spatial predictions

6. Model usefullness

IMPORTANT: keep in mind the limitation and

assumptions in every step during the modelling

process

Normalized Difference Vegetation Index (NDVI) improving species distribution models: an example with the neotropical genus Coccocypselum (Rubiaceae)

Silvana Amaral

Cristina Bestetti Costa

Camilo Daleles Rennó

Divisão de Processamento de Imagens – DPI /INPE

XII Simpósio Brasileiro de Sensoriamento Remoto Florianópolis, April/2007

Context

The availability of observational data of species, and the scope

and resolution of spatially explicit environmental data, are

increasing,

Capabilities of the computational and analytical tools

Remote sensing data can contribute to the modeling process by improving the environmental data set and the niche characterization.

AVHRR/NOAA imagery, in combination with other variables, proved to have sufficient resolution to model the range of bird species.

METEOSAT temporal series was tested to improve climate data for wild life distribution models

Osborne et al, 2001

Context

Multi-temporal Normalized Difference Vegetation Index (NDVI) can be

used to model trends in species richness

The use of a vegetation index contributes providing information about

the canopy closure,

the phenological status

the water content variation within the different physiognomies.

We hypothesize that species distribution models that uses vegetation

index (NDVI) as an additional environmental variable would improve

the representation of a species spatial distribution.

Objective

This work analyses the contribution of

remote sensing data, specifically the NDVI,

for species distribution models, based on the

taxonomic revision of the neotropical genus

Coccocypselum P. Br.

The genus Coccocypselum

C. lanceolatum C. lanceolatum

C. hasslerianum C. erythrocephalum

Photo: C.B. Costa

The genus Coccocypselum

Genus Coccocypselum Rubiaceae family:

One of the most important families in the tropics.

Prostrate and creeping herbs with a spongy blue berries fruits.

18 species - small herbaceous genus, widely distributed in the Neotropics, south Mexico to northern Argentina

15 species occur in Brazil, 5 species selected.

Assumption: the observed species pattern is in a relative equilibrium with the environment

5:2:18

14:10:18

3:0:18

5:0:18

5:2:18

14:10:18

3:0:18

5:0:18

Geographic range and biodiversity

Source: Costa 2005

Coccocypsellum

Species

Distribution

C. anomalum Brazil – Atlantic coast

C. aureum Neotropics

C. bahiensis Brazil – Atlantic coast

C. capitatum Brazil – Atlantic coast

C. condalia Neotropics

C. cordifolium Neotropics

C. erythrocephamlum Brazil – Atlantic coast

C. geophiloides Equator, Colombia, Brazil, Bolívia

C. glabrifolium Brazil – Atlantic coast

C. guianense Central America, north of AS

C. hasslerianum Brazil, Bolivia, Paraguay,

Argentina

C. hirsutum Colombia, Venezuela, Guiana,

Peru, Brazil e Bolivia

C. hispidulum Central America, Colombia,

Venezuela, Trinidad, Peru

C. lanceolatum Neotropics

C. lymansmithii Brazil – Atlantic coast

C. pedunculare BA, MG

C. pulchellum PR, RS, SC

C. repens Central America and Antilles

Data Sources

Presence data:

taxonomic revision of Coccocypselum(Costa 2005)

Information associated with specimen vouchers in natural history museums.

Pseudo-absent data criteria:

Visited sites - species not recorded;

Visited sites, but other excluding species collected.

Not visited sites, but floristic studies did not record the species.

Sites that did not contain Coccocypselum species and the habitat is unsuitable.

Evaluation training set: 20% of the records

Training points Evaluation points Studied species

Presence Presence Absence

C. capitatum (Graham) C.B. Costa & Mamede 65 15 15

C. cordifolium Nees & Mart. 72 16 16

C. erythrocephalum Cham. & Schltdl. 33 8 8

C. lymansmithii Standl. 22 5 5

C. pulchellum Cham. 52 12 12

Data Sources / Tools

TerraView: TerraLib–OM Plugin

Species occurrences


Results of species distribution

modeling

Environmental Variables

Selection based on specialist knowledge

general distribution of Coccocypselum genus is related to the conditions of humidity and altitudinal gradients

Nature Variables Resolution

(Degree) Source Date

Maximum temperature

Minimum temperature

Average temperature

Precipitation

Clima

Bioclimatic variables

0.25

Weather stations

WordClim Project

Average monthly climate data from 1950-2000 series

Elevation

Slope Relieve

Aspect

0.0089

SRTM

NASA

2000 imagery

Maximum NDVI

Minimum NDVI

Average NDVI

Vegetation RS

Standard deviation NDVI

1.0

AVHRR-17

NASA/CPTEC

Fortnightly mosaic 2005

Environmental Variables

AVHRR/NOAA17 -

NDVI

Derived measure of photosynthetic activity

Mosaic fortnightly series for 2005

Mosaic Images –cloud detection procedure – nodata values pixels

Images of NDVI Minimum, Maximum, Average and Standard Deviation

3

11

10

13

4

10

6

7

Fitness

11000000000000000001

10111010111000100101

10101000101001110110

11001110101011101101

00001000101000000100

11001010101010100101

00011000101010000010

11001000101000100100

População inicial

8

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3

2

1

Ind.

3

11

10

13

4

10

6

7

Fitness

11000000000000000001

10111010111000100101

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00001000101000000100

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00011000101010000010

11001000101000100100

População inicial

8

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1

Ind.

11001010101010100101

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10101000101001110110

11001010101010100101

Cromossomos pais

3

5

6

5

1

7

6

3

Indivíduo

11001010101010100101

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10101000101011101101

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10101000101001100101

11001010101010110110

Cromossomos filhos

27

81

37

50

Sorteio

11001010101010100101

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10101000101001110110

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Cromossomos pais

3

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Indivíduo

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Cromossomos filhos

27

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37

50

Sorteio

11001010101010100101

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Cromossomos filhos

11001110101010100101

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Indivíduo

11001010101010100101

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10101000101001100101

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Cromossomos filhos

11001110101010100101

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11001110101001110110

11001100101000100100

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2

1

Indivíduo

3

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Fitness

11000000000000000001

10111010111000100101

10101000101001110110

11001110101011101101

00001000101000000100

11001010101010100101

00011000101010000010

11001000101000100100

População inicial

11001110101010100101

11001110101011101101

10101000101011101111

11001110101001110110

11001100101000100100

11111010111000100101

10101000101001110110

11101010101010000101


8

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1

Ind.

11

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12

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10

10

Fitness

3

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10

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7

Fitness

11000000000000000001

10111010111000100101

10101000101001110110

11001110101011101101

00001000101000000100

11001010101010100101

00011000101010000010

11001000101000100100

População inicial

11001110101010100101

11001110101011101101

10101000101011101111

11001110101001110110

11001100101000100100

11111010111000100101

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Ind.

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Fitness

Species Distribution Modeling

To evaluate the importance of NDVI:

climatologic and topographic variables

climatologic and topographic variables +NDVI

Genetic Algorithm for Rule-Set Production (GARP) Best

sub-set, in the OpenModeller (OM).

Gerar uma população

Estimar a população

Parar

Seleção

Cruzamento

Mutação

Estimação da nova população

Não

Sim

Fim da busca?

Source: Santa Catarina, 2006

Species Distribution Modeling

GARP Best-subset

random approach, 10 models per species.

result is a summary with the potential distribution (%)

Statistical Test

Comparison: models with and without NDVI data

Confusion Matrix: presence and absence evaluation points

Are the models different from what would be expected from a random classification?? Kappa statistic

Different number of evaluation points=> non-parametric statistic to compare samples: the Mann-Whitney test (U)

Evaluation of sample size effect

Compare models – smaller U statistic, better models.

Coccocypselum Distributions

Coccocypselum species - wet forest, along rivers and wet places.

The NDVI data incorporated to models can predict a relation between the

species in open or more forested physiognomy.

C. cordifolium modeled with climatic and topography data. C. cordifolium modeled with climatic, topography and NDVI data.


Predicted distribution map with NDVI reveals a more restricted pattern for

all species of Coccocypselum studied.

C. capitatum and C. cordifolium present a wider distributional pattern. The

others species have a more restricted range and the models seem to be more

accurate.

C. erythrocephalum - climatic and topography data. C. erythrocephalum - climatic, topography and NDVI data.


Coccocypselum capitatum

C. capitatum - climatic and topography data. C. capitatum - climatic, topography and NDVI data.


Coccocypselum lymansmithii

C. lymansmithii - climatic and topography data. C. lymansmithii - climatic, topography and NDVI data.


Coccocypselum pulchellum

C. pulchellum - climatic and topography data. C. pulchellum - climatic, topography and NDVI data.


The usefulness of the environmental niche modeling when applied

to biogeographical and conservation approaches has been

contested (Araújo and Guisan 2006).

The species distribution models shows a result consistent to the

distributional observation found in the taxonomic study of the

genus Coccocypselum (Costa 2005).

As expected, the SDM showed a wider distributional pattern.

SDM can be improved by restricting the predicted ranges using

expert drawn range maps and biogeographical regions information.

Statistical Analysis

Kappa analysis

values higher than 0.5 => good fit between the evaluation points and the modeled species distribution.

The models were better than random.

Models with NDVI were NOT superior to those fitted with climatic and topographical predictors only.

Mann-Whitney test

Only C. pulchellum, C. capitatum and C. cordifolium models differentiated statistically between the presence and absence occurrence data.

C. lymansmithii and C. erythrocephalum the distribution model was not conclusive.

Limited distribution of theses species and the low number of samples points (<10).

Kappa Mann-Whitney (U Statistic)

Specie

no NDVI NDVI no NDVI NDVI

Critical value

(α =5%) N

C. lymansmithii 0.8 0.8 7.5 7.5 2 5

C. erythrocephalum 0.5 0.5 21.5 22.5 12 8

C. pulchellum 0.83 0.83 16* 15* 37 12

C. capitatum 0.6 0.53 55* 48.5* 64 15

C. cordifolium 0.75 0.56 46.5* 36* 75 16

* Significative at 5%

Contribution

The species distribution models generate by GARP indicates a potential

presence of the species studied closely related to the known distribution of

the species studied.

The models for wide distribution species (C. capitatum and C. cordifolium),

were more consistent to the real distribution then the restrict ones

(C. lymansmithii, C. erythrocephalum and C. pulchellum).

It presents a very robust statistical analysis of the models, since absent data

were generated exclusively for this purpose. Approach is not frequent in the

species distribution modeling analysis by the difficulty of having absence data.

Contribution

Comparing the species distribution models generated including NDVI data,

they presented better results than the distribution models generated without

NDVI data.

Despite the values were very similar, this results suggested an improvement

when using NDVI as environmental variables in the modeling process.

This study illustrated the potential of incorporating NDVI data into large-

scale models of plant species distribution.

The same approach should be applied over other species with higher sample

size for more accurate analysis.

SEM

coord.

64,72%

COM coord.

válidos

27,69%

(2637

ou

77,79%)

COM

coord.

35,28%

(3360)

erros

7,59%

(723 ou

21,33%)

Pam species occurrence database for SDMs

São Paulo – 763 reg.

Amazonas –

427 reg.

Acre – 298 reg.Bahia – 298 reg.

Mato Grosso – 129 reg.

Goiás – 106 reg.

Pará – 99 reg.

Maranhão – 94 reg.


Amazonas –

427 reg.



Goiás – 106 reg.

Pará – 99 reg.

Maranhão – 94 reg.• TOTAL 9524 records;• 3360 with geographic coordinates• 2637 records after initial corrections (coordinates in the sea, replication, taxonomy )

• Richer genders : Bactris (38spp.), Geonoma (37), Syagrus(25), Attalea (22) and Astrocaryum (13).

Arasato et al., 2007


Registros /spp

No Brasil


Amazonas –

427 reg.



Goiás – 106 reg.

Pará – 99 reg.



Amazonas –

427 reg.



Goiás – 106 reg.

Pará – 99 reg.


Pam species occurrence database for SDMs


WORLDCLIM.ORG (10arc-min or +18,5km)

Climate: temperatures, minimum and maximum, and precipitation for January and July;

Bioclimatic: bio1, bio4, bio13

SRTM (Shuttle Radar Topography Mission)

Altitude, slope and aspect: ~ 1 km

HAND: 30 arc-sec ou +1km ; limiar de 100

Densidade de drenagem (Kernel): r.e. 10 km; raio de influência de 43000 km²

HAND: 30 arc-sec or +1 km; 100 limiar for drainage Density (Kernel): r.e. 10 km; radius of influence of 43000 km²

Algorithms

Maxent: http://www.cs.princeton.edu/~schapire/maxent

GARP best subset: http://openmodeller.sourceforge.net/

Drainage Density and SRTM - HAND do SRTM for Palm species distribution modelling


Euterpe edulis Mart.

A

GARP

B

Maxent

A

no DDren

B

DDren

Resultados

GARP – very generalised

Distribution of Euterpe edulis Mart., proved really dependent

on the seasonality of temperature (bio4) and the availability of

water (prec and drainage density), this order, as indicated by

literature

Data from SRTM, drainage density, and HAND were not

sufficient to override other relieve variables


A

no HAND

B

HAND

A

GARP

B

Maxent

Results

The best model was the resulting from the

use of variable altitude, slope, aspect,

drainage density and hand to define the

limits of distribution of Euterpe Edulis


Remarks

SDM has many limitations...

A tool to understand the variables/processes that defines sp

distribution

Ex. Can be useful for conservation

Input Database precision & trustworthy sp occurrence data

Remote Sensing and geoinformation basic for modeling

Georeferecing

Environmental variables – precision and temporal cover

Our Biodiversity group...

Silvana Amaral

Dalton Valeriano

Cristina B. Costa

Luciana S. Arasato

Diana Valeriano

Camilo Rennó

Marco Antonio Ribeiro Jr.

Arimatéa de C. Ximenes

Study Group

Biodiversity

Based on environmental variability

Self Organizing Mapping (SOM) for ecoregions definition/mapping

Arimatea C. Ximenes

Biodiversity

Based on environmental variability

Spatial dependence modeling – sp abundance – E.edulis

DEM / Hand

Occurence data (palmeiras)

Luciana S. Arasato

Biodiversity

DEM / Hand

Spatial dependence modeling – Arecaceae

Luciana S. Arasato

HAND x Abundance + spatial correlation

Atlantic Rain Forest – Arecaceae (palm trees)

Biodiversity

Occurrence data

Pontos de altitude

Curvas de nível

Luciana S. Arasato

Based on Phylogenetic Richness

Amazon Forest – trees from RadamBrasil invetory

Biodiversity

Cristina B. Costa

Based on Phylogenetic Richness

Amazon Forest – trees from RadamBrasil invetory Conservation issues

Biodiversity

Cristina B. Costa

Based on life forms – BOX model

Contact Amazon Forest / Cerrado

FLORESTA AMAZÔNICA CERRADO

Arborescentes perenifólias Arborescentes de tronco suculento

Arbustos em roseta mesófilos (palmeiras anãs) Arbustos em roseta mesófilos (palmeiras anãs)

Arbustos em roseta xerófilos (bromélias terrestres) Arbustos em roseta xerófilos (bromélias terrestres)

Arbustos espinhentos caducifólios Arbustos espinhentos caducifólios

Arbustos latifoliados perenifólios tropicais Arbustos latifoliados perenifólios tropicais

Árvores baixas latifoliadas caducifólias Árvores anãs latifoliadas perenifólias tropicais

Árvores caducifólias mesófilas Árvores baixas latifoliadas caducifólias

Árvores caducifólias xerófilas Árvores caducifólias mesófilas

Árvores de floresta pluvial tropical Árvores palmiformes (palmeiras)

Árvores palmiformes (palmeiras) Árvores microfilas perenifólias tropicais

Árvores microfilas perenifólias tropicais Árvores pequenas de floresta montana

Árvores pequenas latifoliadas perenifólias tropicais Árvores pequenas latifoliadas perenifólias tropicais

Árvores perenifólias de região temperada quente Arvoretas palmiformes (palmeiras baixas)

Arvoretas palmiformes (palmeiras baixas) Epífitas de folha estreita

Epífitas de folha estreita Epífitas latifoliadas caducifólias

Ervas latifoliadas perenifólias tropicais Ervas latifoliadas caducifólias

Ervas latifoliadas suculentas Ervas latifoliadas perenifólias tropicais

Gramíneas altas (típicas) Ervas latifoliadas suculentas

Gramíneas altas (tipo cana) Gramíneas altas (típicas)

Gramíneas arborescentes Gramíneas altas (tipo cana)

Gramíneas pequenas (em tufo) Gramíneas arborescentes

Gramíneas pequenas (tipo gramado) Gramíneas pequenas (em tufo)

Hemiepífitas Gramíneas pequenas (em touceira espessa)

Lianas latifoliadas perenifólias tropicais Gramíneas pequenas (tipo gramado)

Palmeiras trepadeiras Lianas latifoliadas perenifólias tropicais

Pteridófitas perenifólias Trepadeiras latifoliadas caducifólias

Trepadeiras latifoliadas perenifólias

Biodiversity

André Jardim

Based on Niche theory – (SDMs)

Amazon Forest – Lizards

Biodiversity

Marco Antonio Ribeiro Júnior, MPEG

Based on Niche theory – (SDMs)

Amazon Forest – Lizards

Biodiversity

OG

RO

SW WSE

EGui

WGui

Similarity between

areas

Environmental

factors and

distribution patterns

PhylogenyScenarios

Hypothesis about evolution context for the biogeography scenarios associating:

Marco Antonio Ribeiro Júnior, MPEG

Based on Niche theory – (ARECACEAE SDM)

http://www.dpi.inpe.br/Ambdata/index.php (Rubiaceae, Croton, Poaceae,...)

Biodiversity

http://www.dpi.inpe.br/Ambdata/index.php

Based on Individual / community modelling

NE Brazilian coast - Mangrove forest -spp succession

Ombrophylous Mixed Forest in Southeast Brazil - Tree Species –TROLL (?)

Biodiversity

Ombrophyllous Mixed Forest

Definition- co-ocorrence of Gymnospermae (connifers)

and Angiospermae

Original distribution in Brazil

Adapted from Hueck (1953)

Community Structure evolution

Diana Damasceno B. Valeriano

Araucaria Forest BiogeographyPast (~200.000 km2) x Present (~2%)

Large emergent trees – defines the forest structure – long life span –recruitment failure in the absence of large disturbances


Biogeography

Mixed Araucaria Forests –

exclusive of Southern

Hemisphere

Two species in South

America:

• Araucaria araucana (Chile)

• Araucaria angustifolia (Brazil)

Life History & Structure

Monodominance of long-lived

pioneers

(Large emergent trees – defines the

forest structure – long life span –

recruitment failure in the absence of

large disturbances)

Araucaria Forest


Mixed Forests with Monodominance of Connifers:

1. Biogeographical Model of Forest Expansion (Klein 1960) – Connifers expansion in open areas – failure to recrute inside the forest

2. Gap Model – autogenic succession (Jarenkow & Batista 1987) – gap from a fallen connifer – allows regeneration

3. Temporal Plot Replacement Model (Lozenge) (Ogden & Stewart 1995) - intermittent recruitment dependent of severe disturbances

Dynamic Models for Mixed Forests


Study AreaCampos do Jordão State Park, SP


Forests and High Altitude Fields Mosaic(Campos do Jordão State Park - Ikonos Satellite Image – 2005)


Methods:

1. Permanent plot (0.5ha)

2. Two inventories – 20 years apart (1988 – 2008)

3. Biometric and floristic data of all trees with dbh ≥ 1.6cm

4. All trees had their position recorded (x,y coordinates- 0.1m precision) and received an identification tag

Main Goal: to evaluate the forest dynamics

1.Mature forest or under ongoing succession?

2.Which model better describes the forest dynamics?

Approachs:

1.Structural and Floristic dynamics

2.Dominant Population Dynamics

3.Horizontal Structural Dynamics (exploratory)

Forest Dynamic - 20 years apart


Height stratification

1988 (t1) - 2008 (t2)Dominant tree populations (cover value)(14 species - )

emergents

canopy

understory


Diameter stratification1988 (t1) - 2008 (t2)Dominant tree populations (14 species)

Emergents canopy understory(exclusive)


Tree Locations - Surface Density MapsKernel Spatial Interpolation

Understorey 2008

Understory1988 Dominant1988

Dominant 2008

N88_understory x N88_dominant

r = -0,73

N08_understory x N08_dominant

r = 0,06


Mortality and Recruitment Surface DensityMaps

Kernel Spatial Interpolation

Chablis areas

= 1988 mapped logs;

= 2008 mapped logs

(darker’s over lighter’s).

A. angustifolia and P. lambertii - dap ≥ 50cm (stars)


Modelling Forest Dynamics

The question:

What is the stage of Campos do Jordão Mixed Forest: Dynamic

equilibrium /mature forest? under ongoing succession?

GOAL

To predict the effect of forest dynamics on tree biomass,

structure and species composition

Use of spatially explicit forest models - TROLL???

Steps:

1. Group species into functional types

2. Discriminate height groups

3. Parameterization of growth, mortality, recruitment rates

4. Characterize tree population structure (dbh) and successionalpattern


Thank you!

No hard questions, please!!

[email protected]

(Take a look at our “Referatas” - www.dpi.inpe.br/referata)

“All models are wrongbut some are useful!”

(Box, 1979).

References

Guisan, A. ; Thuiller, W. 2005, Predicting species distribution: offering

more than simple habitat. Ecology Letters, 8:993-1009.

Guisan, A. ; Zimmermann. 2000, Predictive habitat distribution models

in ecology. Ecological Modelling, 135:147-186.

Ambdata (http://www.dpi.inpe.br/Ambdata/index.php)

Referatas (http://www.dpi.inpe.br/referata/)

IWASHITA, F. Sensibilidade de modelos de distribuição de espécies a erros de

posicionamento de dados de coleta. 2007. 103 p. (INPE-15174-TDI/1291). Dissertação

(Mestrado em Sensoriamento Remoto) - Instituto Nacional de Pesquisas Espaciais, São José

dos Campos, 2007. Disponível em: <http://urlib.net/sid.inpe.br/mtc-

m17@80/2007/06.13.12.04>. Acesso em: 06 abr. 2011.



http://www.dpi.inpe.br/referata/

http://www.dpi.inpe.br/referata/

http://urlib.net/sid.inpe.br/mtc-m17@80/2007/06.13.12.04







References

XIMENES, A. C. ; AMARAL, S. ; VALERIANO, D. M. . O conceito de ecorregião e os métodos utilizados para o seu

mapeamento. Geografia (Rio Claro. Impresso), v. 35, p. 219-226, 2010.

FOOK, K. D. ; AMARAL, S. ; MONTEIRO, Antônio Miguel Vieira ; CAMARA, Gilberto ; XIMENES, A. C. ; ARASATO, L. S. .

Making species distribution models available on the web for reuse in biodiversity experiments: euterpe edulis species case

study. Sociedade & natureza (UFU. Online), v. 21, p. 39-49, 2009.7.

FOOK, K. D. ; AMARAL, S. ; Vieira Monteiro, Antônio Miguel ; CÂMARA, Gilberto ; Casanova, Marco Antônio ; Amaral,

Silvana . Geoweb Services for Sharing Modelling Results in Biodiversity Networks. Transactions in GIS, v. 13, p. 379-399,

2009.

XIMENES, A. C. ; AMARAL, S. . Mapeamento das Ecorregiões do Distrito Florestal Sustentável da BR-163 na Amazônia

Brasileira com uso de redes neurais. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15. (SBSR), 2011,

Curitiba, PR. SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15. (SBSR). São José dos Campos : INPE, 2011. p.

3094-3102.

ARASATO, L. S. ; AMARAL, S. ; RENNÓ, C. D. . Detecting individual palm trees (Arecaceae family) in the Amazon

rainforest using high resolution image classification. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15.

(SBSR), 2011, Curitiba, PR. SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO. São José dos Campos : INPE, 2011.

p. 7628-7635.

ARASATO, L. S. ; AMARAL, S. ; XIMENES, A. C. . Densidade de drenagem e HAND (Height Above the Nearest Drainage)

do SRTM para modelagem de distribuição de espécie de palmeiras no Brasil. In: SIMPÓSIO BRASILEIRO DE

SENSORIAMENTO REMOTO, 14, 2009, Natal. SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 14., 2009.

XIMENES, A. C. ; AMARAL, S. ; ARCOVERDE, G. F. B. ; MONTEIRO, Antônio Miguel Vieira . Redes neurais para a seleção

de variáveis ambientais no processo de modelagem de distribuição de espécies na região Norte do Brasil. In: XIV

SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 2009, Natal. XIV SIMPÓSIO BRASILEIRO DE

SENSORIAMENTO REMOTO. São José dos Campos : INPE, 2009.

FOOK, K. D. ; AMARAL, S. ; MONTEIRO, Antônio Miguel Vieira ; CAMARA, Gilberto . Sharing executable models through

an Open Architecture based on Geospatial Web Services: a Case Study in Biodiversity Modelling. In: GEOINFO, 2008,

Rio de Janeiro. Geoinfo, 2008.

http://lattes.cnpq.br/7327236294706424



























References AMARAL, S. ; COSTA, C. B. ; RENNÓ, C. D. . Normalized Difference Vegetation Index (NDVI) improving species

distribution models: an example with the neotropical genus Coccocypselum (Rubiaceae). In: XIII Simpósio Brasileiro de

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