Species distribution models and

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OUTLINE. Distribution models. → presence-only, presence-background, presence-absence. Case studies. Fuzzy logic. Model evaluation. Take-home messages ...
Is there safety in numbers? Can we be confident with our species distribution models? A. Márcia Barbosa CIBIO/InBIO – UÉvora (Portugal)

OUTLINE Distribution models → presence-only, presence-background, presence-absence

Case studies

Fuzzy logic Model evaluation

Take-home messages

Distribution models species distribution data geology temperature

precipitation

environmental variables

topography

species’ predicted distribution

Presence-only methods

geographical envelopes

(kernels, hulls – range maps)

environmental envelopes (OA, BIOCLIM, DOMAIN)

Presence-background methods

compare distributions of the variables between the presences and the available area (ENFA, GARP, MAXENT)

Presence-absence methods

GLM, GAM, CART, BRT, RF, ANN, MARS

- good transparency/complexity balance - several success stories:

Baetic midwife toad (Alytes dickhilleni) Herp conference 2004

Baetic midwife toad (Alytes dickhilleni) Herp conference 2004 Herp conference 2012

- 38 new presences (+28%) - mean favourability 0.87

- proportion of new records in favourable (>0.5 or >0.8) sites higher than chance - correct classification and kappa increased

Monk parakeet (Myiopsitta monachus)

Distribution in 2003

Predicted favourability

Muñoz & Real (2006) Diversity and Distributions 12: 656-665

Monk parakeet (Myiopsitta monachus)

Distribution in 2003

Predicted favourability

Muñoz & Real (2006) Diversity and Distributions 12: 656-665

Monk parakeet (Myiopsitta monachus)

2003: intermediate fav.

high favourability presence data 2003

2015: presence

Monk parakeet (Myiopsitta monachus)

2003: intermediate fav.

high favourability presence data 2003

2015: presence

Iberian painted frogs (Discoglossus sp.)

D. galganoi D. jeanneae Real et al. (2005) Canadian Journal of Zoology 83: 536-545

Real et al. (2005) Canadian Journal of Zoology 83: 536-545

Iberian lynx (Lynx pardinus)

Real et al. (2009) Diversity and Distributions 15: 390-400

Iberian lynx (Lynx pardinus) correlated with abundance

Favourability for lynx+rabbit

max

min Caribú Hongo

Real et al. (2009) Diversity and Distributions 15: 390-400

Iberian desman (Galemys pyrenaicus)

Barbosa et al. (2010) Ecological Modelling 220:747-754

conservation? Barbosa et al. (2010) Ecological Modelling 220:747-754

0.00 0.06 0.13 0.19 0.25 0.31 0.38 0.44 0.50 0.56 0.63 0.69 0.75 0.81 0.88 0.94 1.00

0 1

Barbosa et al. (2010) Conservation Biology 24: 1378-1387

validation? Barbosa et al. (2010) Conservation Biology 24: 1378-1387

1

Validation: Favourability at sampled vs. random points (1000 samples / region) Favourability at presences vs. absences

Proportion presences in favourable / unfavourable vs. random areas Favourability at SICs vs. remaining territory

Barbosa et al. (2010) Conservation Biology 24: 1378-1387

0

Eurasian otter (Lutra lutra)

Barbosa et al. (2010) Biological Conservation 114:377-387

Eurasian otter (Lutra lutra)

Barbosa et al. (2010) Biological Conservation 114:377-387

Barbosa et al. (2010) Conservation Biology 24: 1378-1387

Iberian ribbed newt (Pleurodeles waltl)

Iberian ribbed newt (Pleurodeles waltl)

Last Interglacial (LIG)

Last Glacial Maximum (LGM)

Present

models identify historical reservoirs for genetic diversity

Intersection of favourability in LIG-LGM

PCA axis 1 of 4 genetic diversity variables

Species distribution models and fuzzy logic

Microtus cabrerae M. guentheri

M. thomasi

Species distribution models and fuzzy logic

M. guentheri

M. thomasi

Species distribution models and fuzzy logic

incorporates

uncertainty

Barbosa (2015) Methods in Ecology and Evolution, in press

Species distribution models and fuzzy logic

⇨ combining models ⇨ biogeographic regions ⇨ beta diversity (loss, gain, turnover)

Barbosa (2015) Methods in Ecology and Evolution, in press

70% 20%

Model evaluation

70% 20%

- discrimination / classification - calibration / reliability

Things to keep in mind…

"[model] values reflect the probability that each cell is occupied by the focal species, based on the covariate values associated with the cell. These

probabilities

can

be

viewed

as

expectations

associated with a stochastic process and do not represent realizations of this process.“ (Karanth et al. 2009, J. Appl. Ecol.)

Things to keep in mind… "Under the most rigorously controlled conditions of pressure, temperature, volume, humidity, and other

variables, an organism will do as it damn well pleases.“ (Murphy’s Law)

"Essentially, all models are wrong, but some are useful."

(George E. P. Box)

Is there safety in numbers?

Can we be confident with our species distribution models?

rubbish in, rubbish out!

that’s it, thanks!

fuzzy sets and fuzzy logic

Barbosa (2015) Methods in Ecology and Evolution, in press

fuzzy sets and fuzzy logic

Barbosa (2015) Methods in Ecology and Evolution, in press