Theoretical Principles & Practical applications Ecological Niche Modelling Theoretical Principles & Practical applications

Lecture Structure Why model species ranges? What is a niche? – fundamental and realized Correlative range modelling – background and assumptions Distribution datasets Variables and their selection Models and their selection Model calibration and evaluation

Why model species ranges? We need to know where species occur and why they occur where they do: We want to predict where a particular species occurs. We want to know more about organism-environment relationships.

Used in response to Increase the rates of habitat, and face species loss; Complete (spatial and temporal) distribution info for a large number of taxa; Contrast the existing distribution data mostly collected in an ad hoc fashion. Given the rate of species loss, it is unlikely that we will get the distribution data that we need in time if we rely on conventional survey techniques. Atlases are an invaluable data source and cover very few taxa but they are very important for model development and calibration.

What we know about species distribution? Despite many decades of investigation our knowledge is still incomplete and at broad scale!!! USGS Forest Service Atlas Florae Europeae - Jalas & Suominen (1965-2013) EUFORGEN Network (2009)

Distribution models have been used to predict species richness centres of endemism the occurrence of particular species assemblages the occurrence of individual species the location of unknown populations the location of suitable breeding habitat breeding success species abundance genetic variability of species help target field surveys aid in the design of reserves inform wildlife management outside protected areas guide mediatory actions in human–wildlife conflicts monitor declining species predict range expansions of recovering species estimate the likelihood of species’ long-term persistence in areas considered for protection identify locations suitable for introduction identify locations suitable for reintroductions identify sites vulnerable to local extinction identify sites vulnerable to species invasion explore the potential consequences of climate change

Modelling literature at a glance 2,215 scientific products 1,964 published papers Source: www.scopus.com

THEORETICAL PRINCIPLES: FUNDAMENTAL vs REALIZED NICHE (G. Evelyn Hutchinson, 1957) Definition: n-dimensional hypervolume described by N environmental and resource constraints within which a species can maintain a viable* population. In other terms, it is the combination of conditions and resources required by an individual species defines the area in which it is able to live. Definition: the set of conditions actually used by given species/population after interactions with other species (predation and especially competition) have been taken into account. * Viable = Survive + Grow + Reproduce

Principles: Range edges WHAT DETERMINES THE EDGE OF GEOGRAPHIC RANGES? Populations do not just cease to exist at the edge of their geographic distributions, but rather taper off gradually. The edges of geographic ranges are thus defined by a change in the local population dynamics, where net gains in population are reduced to levels lower than the net losses. There are changes in local population dynamics at the edge of a distribution, and more net losses than net gains THESE POPULATION LEVEL CHANGES ARE DUE TO: Changes in abiotic factors (physical barriers, climate factors, absence of essential resources) and biotic factors (impact of competitors, predators or parasites) Genetic mechanisms that prevent species from becoming more widespread. Abiotic/biotic factors are only limiting because a species has not evolved the morphological + physiological + ecological means to overcome them.

SPECIES RANGES ON THE BOOKS Biomes of the World

Fagus sylvatica Quercus cerris Corylus avellana Castanea sativa Quercus cerris

Specifics: HOW does NICHE-BASED MODELLIng works?

Niche-based modelling: assumptions Environmental factors drive species distribution Species are in equilibrium with their environment Limiting variables – are they really limiting? Evidence for species dying/not reproducing due to climate Collinearity of variables Static vs dynamic approaches: data snapshot or time series response?

Cautionary note on modelling in general Risk of all models: the results of a model can never be better than the data used to construct and run it. If the basic input data is incorrect, this error can be multiplied many times over. Need to understand assumptions, explicit and implicit. Models are an abstraction of reality, the accuracy and assumptions depend on the scale of the model, and so although the model will improve our understanding of the processes involved, it does not necessarily mean that the outputs are 100% truth.

Direct Indirect Specifics: variable selection Definition Example Variables with biological relationship with study species Variables that correlate with study species because of correlation with series of intermediate direct factors rather than direct relationship Definition Climate, nesting sites, soil nutrients (plants), interacting species, site isolation Elevation, soil, topography, geology, soil nutrients (animals) Example Model structure easily interpreted in biological meaningful terms. Direct biological relationship should generalize better to new areas, and be more effective for climate change modeling than indirect predictors. Provides more info for conservation management Data sets widely available in GIS Low cost, ease of collection Can be effective predictors, i.e. elevation in mountainous areas Encompasses a range of correlated variables so should: result in parsimonious models if variable selection applied, recording fewer variables Strength Variables require greater effort to record Data sets may need to be estimated for large spatial extents (using indirect variables reducing overall accuracy Correlation with direct variables tend to be location specific Limited interpretation – biological meaning inferred, resulting in increased uncertainty Weakness

Variables and their selection Species only select their habitats in the broadest sense (Heglund 2002), and distribution patterns are the cumulative result of a large number of fine scale decisions made to maximize resource acquisition. The more accurately these fine-scale resources can be approximated and access quantified, the better the model should perform if all models were equal. Predictions at broad scales can use broader environmental variables, often associated with the fundamental niche. Finer scale predictions need to concern themselves more with those variables that determine the realized niche. Source: Pearson & Dawson 2003

criticalities Environmental variables are often direct variables, such as precipitation or temperature. Soil conditions include pH, texture, organic carbon and fertility, but these measures are often difficult to obtain on an appropriate scale, since there can be considerable variation even within an area. In general, it is a good idea to avoid indirect measures of a variable, which is obviously a challenge since much of a country is not monitored, and many such measures are not easily taken. Features such as slope and altitude allow some degree of projection into the future, since the lapse rate (extent to which temperature changes with increasing altitude) may closely parallel changing climatic conditions. Solar radiation and wind, which are essential for plant growth responses and dispersal are both particularly challenging to obtain accurate measures of.

Environmental Variables What data are available and where? Local scale (National/Regional) Global scale (World/Continent) Species Museum/Herbarium data Survey Atlas Forest Inventories Field data Presence / Absence data Taxonomic updates of older museum Museum/Herbarium data Survey Atlas Expert Atlas Presence / Absence data Taxonomic updates of older museum Environmental Variables Hydrological Bulletin Field data National/Regional projects Geoportals WebGIS repositories Hydrological Bulletin Global Climatic repositories International projects Geoportals WebGIS repositories

Examples: species Local scale (National/Regional) Forest Inventory of Sicily (Italy) http://sif.regione.sicilia.it/ National Forest Inventory of France http://inventaire-forestier.ign.fr/ National Forest Inventory of Spain http://www.magrama.gob.es/ Species information system of Spain http://www.anthos.es/ National Forest Inventory of Portugal http://www.icnf.pt/portal/florestas/ifn National Flora Database of Croatia http://hirc.botanic.hr/fcd/

Examples: species Global scale (World/Continent) Forest Map of Europe http://www.efi.int/portal/936 Forest Species distribution in Europe http://forest.jrc.ec.europa.eu/ Ecosystems map of America http://rmgsc.cr.usgs.gov/ecosystems/dataviewer.shtml Global species distribution http://www.gbif.org/ European forest genetic resources http://www.euforgen.org

Examples: environmental variables Local scale (National/Regional) Digital Elevation Model of Italy http://tinitaly.pi.ingv.it/ National Geoportal of Italy http://www.pcn.minambiente.it/GN/ US Topographic maps http://viewer.nationalmap.gov/launch/ National Geoportal of Spain http://sig.magrama.es/geoportal/ National Climatic Registry of Spain http://www.aemet.es/es/idi/clima/registros_climaticos Andalusian Climatic Database http://www.juntadeandalucia.es/medioambiente/

Examples: environmental variables Global scale (World/Continent) Global Digital Elevation Model http://gdem.ersdac.jspacesystems.or.jp/ European Soil Map http://eusoils.jrc.ec.europa.eu/ World Environmental Data Explorer http://geodata.grid.unep.ch/ World Soil Grid Database http://soilgrids1km.isric.org/index.html World Climate Data http://www.worldclim.org/ European Climate Dataset http://eca.knmi.nl Global Bioclimatic Data http://www.climond.org

How do we choose a model type? BioClimatic envelope (Bioclim) Ordination (ENFA) Environmental Distance (DOMAIN, CSM) Ordinary Regression (Arc-SDM) Generalised additive models (GAM) Generalised linear models (GLM) Classification and regression trees (CART, RF) Genetic Algorithm (GARP) Artificial neural networks (SPECIES) Boosted regression Trees (BRT) Support Vector Machine (SVM) Bayesian/Maximum Entropy (MaxEnt) Profile Techniques Regression-based Techniques Machine Learning Techniques

Software available? http://openmodeller.sourceforge.net/ http://mmweb.animal.net.cn/index.jsp eHabitat Platform http://dopa.jrc.ec.europa.eu/ https://www.cs.princeton.edu/~schapire/maxent/ http://www.nhm.ku.edu/desktopgarp/

Present potential distribution What we can do with niche modelling? Past reconstructions Time Present potential distribution Future projections

Study of past to… Study of present to… Study of future to… Species ecological behaviour Inner plasticity and buffering attitude Response to climatic variables Range stability and oscillations Putative glacial refugia Demographic history In situ conservation strategies Study of past to… Suitable surfaces for immediate reforestations Amount of sites suitable for reforestation Human effect on the current landscape Ecological corridors Present vulnerability Study of present to… Response of plants to changes Bioclimatic niche reassessment Extinction risk Range shift and dynamics Future conservation strategies Areas to be selected as refugia Study of future to…

Practical application: quercus suber L.

Current distribution Vessella F., López-Tirado J., Simeone M.C., Schirone B., Hidalgo P.J. (2015)

Past reconstruction (120 kyr – present)

Future projections and climate change

Practical application: potential Forests in andalusia Present

2011-2040

2041-2070

2071-2100