Species distribution modelling

Species distribution modelling (SDM), also known as environmental (or ecological) niche modelling (ENM), habitat modelling, predictive habitat distribution modelling, and range mapping[1] uses computer algorithms to predict the distribution of a species across geographic space and time using environmental data. The environmental data are most often climate data (e.g. temperature, precipitation), but can include other variables such as soil type, water depth, and land cover. SDMs are used in several research areas in conservation biology, ecology and evolution. These models can be used to understand how environmental conditions influence the occurrence or abundance of a species, and for predictive purposes (ecological forecasting). Predictions from an SDM may be of a species’ future distribution under climate change, a species’ past distribution in order to assess evolutionary relationships, or the potential future distribution of an invasive species. Predictions of current and/or future habitat suitability can be useful for management applications (e.g. reintroduction or translocation of vulnerable species, reserve placement in anticipation of climate change).

There are two main types of SDMs. Correlative SDMs, also known as climate envelope models, bioclimatic models, or resource selection function models, model the observed distribution of a species as a function of environmental conditions.[1] Mechanistic SDMs, also known as process-based models or biophysical models, use independently derived information about a species' physiology to develop a model of the environmental conditions under which the species can exist.[2]

The extent to which such modelled data reflect real-world species distributions will depend on a number of factors, including the nature, complexity, and accuracy of the models used and the quality of the available environmental data layers; the availability of sufficient and reliable species distribution data as model input; and the influence of various factors such as barriers to dispersal, geologic history, or biotic interactions, that increase the difference between the realized niche and the fundamental niche. Environmental niche modelling may be considered a part of the discipline of biodiversity informatics.

Predicting habitats
Example of simple correlative species distribution modelling using rainfall, altitude, and current species observations to create a model of possible existence for a certain species.


A. F. W. Schimper used geographical and environmental factors to explain plant distributions in his 1898 Pflanzengeographie auf physiologischer Grundlage (Plant Geography Upon a Physiological Basis). Andrew Murray used to environment to explain the distribution of mammals in his 1866 The Geographical Distribution of Mammals. Robert Whittaker's work with plants and Robert MacArthur's work with birds strongly established the role the environment plays in species distributions.[1] Elgene O. Box constructed environmental envelope models to predict the range of tree species[3]. His computer simulations were among the earliest uses of species distribution modelling.[1]

The adoption of more sophisticated generalised linear models (GLMs) made it possible to create more sophisticated and realistic species distribution models. The expansion of remote sensing and the development of GIS-based environmental modelling increase the amount of environmental information available for model-building and made it easier to use.[1]

Correlative vs mechanistic models

Correlative SDMs

SDMs originated as correlative models. Correlative SDMs model the observed distribution of a species as a function of geographically referenced climatic predictor variables using multiple regression approaches. Given a set of geographically referred observed presences of a species and a set of climate maps, an algorithm find the most likely environmental ranges within which a species lives. Correlative SDMs assume that species are at equilibrium with their environment and that the relevant environmental variables have been adequately sampled. The models allow for interpolation between a limited number of species occurrences.

For these algorithms to be effective, it is required to gather observations not only of species presences, but also of absences, that is, where the species does not live. Records of species absences are typically not as common as records of presences, thus often "random background" or "pseudo-absence" data are used to fit these models. If there are incomplete records of species occurrences, pseudo-absences can introduce bias. Since correlative SDMs are models of a species’ observed distribution, they are models of the realized niche (the environments where a species is found), as opposed to the fundamental niche (the environments where a species can be found, or where the abiotic environment is appropriate for the survival). For a given species, the realized and fundamental niches might be the same, but if a species is geographically confined due to dispersal limitation or species interactions, the realized niche will be smaller than the fundamental niche.

Correlative SDMs are easier and faster to implement than mechanistic SDMs, and can make ready use of available data. Since they are correlative however, they do not provide much information about causal mechanisms and are not good for extrapolation. They will also be inaccurate if the observed species range is not at equilibrium (e.g. if a species has been recently introduced and is actively expanding its range).

Mechanistic SDMs

Mechanistic SDMs are more recently developed. In contrast to correlative models, mechanistic SDMs use physiological information about a species (taken from controlled field or laboratory studies) to determine the range of environmental conditions within which the species can persist.[2] These models aim to directly characterize the fundamental niche, and to project it onto the landscape. A simple model may simply identify threshold values outside of which a species can't survive. A more complex model may consist of several sub-models, e.g. micro-climate conditions given macro-climate conditions, body temperature given micro-climate conditions, fitness or other biological rates (e.g. survival, fecundity) given body temperature (thermal performance curves), resource or energy requirements, and population dynamics. Geographically referenced environmental data are used as model inputs. Because the species distribution predictions are independent of the species’ known range, these models are especially useful for species whose range is actively shifting and not at equilibrium, such as invasive species.

Mechanistic SDMs incorporate causal mechanisms and are better for extrapolation and non-equilibrium situations. However, they are more labor-intensive to create than correlational models and require the collection and validation of a lot of physiological data, which may not be readily available. The models require many assumptions and parameter estimates, and they can become very complicated.

Dispersal, biotic interactions, and evolutionary processes present challenges, as they aren’t usually incorporated into either correlative or mechanistic models.

Correlational and mechanistic models can be used in combination to gain additional insights. For example, a mechanistic model could be used to identify areas that are clearly outside the species’ fundamental niche, and these areas can be marked as absences or excluded from analysis. See [4] for a comparison between mechanistic and correlative models.

Niche modelling algorithms (correlative)

There are a variety of mathematical methods that can be used for fitting, selecting, and evaluating correlative SDMs. Algorithms include "profile" methods, which are simple statistical techniques that use e.g. environmental distance to known sites of occurrence such as BIOCLIM[5][6] and DOMAIN; "regression" methods (e.g. forms of generalized linear models); and "machine learning" methods such as maximum entropy (MAXENT). An incomplete list of algorithms that have been used for niche modelling includes:

Profile techniques

Regression-based techniques

Machine learning techniques

Furthermore, ensemble models can be created from several model outputs to create a model that captures components of each. Often the mean or median value across several models is used as an ensemble. Similarly, consensus models are models that fall closest to some measure of central tendency of all models—consensus models can be individual model runs or ensembles of several models.

Niche modelling software (correlative)

SPACES is an online Environmental niche modeling platform that allows users to design and run dozens of the most prominent algorithms in a high performance, multi-platform, browser-based environment.

MaxEnt is the most widely used method/software uses presence only data and performs well when there are few presence records available.

ModEco implements various algorithms.

DIVA-GIS has an easy to use (and good for educational use) implementation of BIOCLIM

The Biodiversity and Climate Change Virtual Laboratory (BCCVL) is a "one stop modelling shop" that simplifies the process of biodiversity and climate impact modelling. It connects the research community to Australia's national computational infrastructure by integrating a suite of tools in a coherent online environment. Users can access global climate and environmental datasets or upload their own data, perform data analysis across six different experiment types with a suite of 17 different algorithms, and easily visualise, interpret and evaluate the results of the models. Experiments types include: Species Distribution Model, Multispecies Distribution Model, Species Trait Model (currently under development), Climate Change Projection, Biodiverse Analysis and Ensemble Analysis. Example of BCCVL SDM outputs can be found here

Most niche modelling algorithms are available in the R packages 'dismo', 'biomod2' and 'mopa'..

Software developers may want to build on the openModeller project.

The Collaboratory for Adaptation to Climate Change adapt.nd.edu has implemented an online version of openModeller that allows users to design and run openModeller in a high-performance, browser-based environment to allow for multiple parallel experiments without the limitations of local processor power.

See also


  1. ^ a b c d e Elith, Jane; Leathwick, John R. (2009-02-06). "Species Distribution Models: Ecological Explanation and Prediction Across Space and Time". Annual Review of Ecology, Evolution, and Systematics. 40 (1): 677–697. doi:10.1146/annurev.ecolsys.110308.120159. ISSN 1543-592X.
  2. ^ a b Kearney, Michael; Porter, Warren (2009). "Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges". Ecology Letters. 12 (4): 334–350. doi:10.1111/j.1461-0248.2008.01277.x. ISSN 1461-0248.
  3. ^ Box, Elgene O. (1981-05-01). "Predicting physiognomic vegetation types with climate variables". Vegetatio. 45 (2): 127–139. doi:10.1007/BF00119222. ISSN 1573-5052.
  4. ^ Morin, X.; Thuiller (2009). "Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change". Ecology. 90 (5): 1301–13. doi:10.1890/08-0134.1. PMID 19537550.
  5. ^ Nix HA (1986). "BIOCLIM — a Bioclimatic Analysis and Prediction System". Research Report, CSIRO Division of Water and Land Resources. 1983–1985: 59–60.
  6. ^ Nix HA (1986). "A biogeographic analysis of Australian elapid snakes". In Longmore (ed.). Atlas of Elapid Snakes of Australia. Australian Flora and Fauna Series 7. Bureau of Flora and Fauna, Canberra. pp. 4–15.

Further reading

External links

  • Climate Envelope Modeling Working Group - Online gathering place for scientists, practitioners, managers, and developers to discuss, support, and develop climate Environmental Niche Modeling tools and platforms
  • BioVeL Ecological Niche Modeling (ENM) - online tool with workflows to generate ecological niche models
  • EUBrazilOpenBio SpeciesLab Virtual Research Environment - online working environment to support the production of ecological niche modeling by (i) simplifying access to occurrence points and environmental parameters and (ii) offering a powerful version of openModeller benefitting from a distributed computing infrastructure;
  • openModeller - open source niche modelling library
  • lifemapper - niche modelling project from Kansas University
  • Lifemapper 2.0 - video of presentation by Aimee Stewart, Kansas University, at O'Reilly Where 2.0 Conference 2008
  • AquaMaps - global predictive maps for marine species
  • Ecological Modelling - International Journal on Ecological Modelling and Systems Ecology
Dispersal vector

A dispersal vector is an agent of biological dispersal that moves a dispersal unit, or organism, away from its birth population to another location or population in which the individual will reproduce. These dispersal units can range from pollen to seeds to fungi to entire organisms.

There are two types of dispersal vector, those that are active and those that are passive. Active dispersal involves organisms that are capable of movement under their own energy. In passive dispersal, the organisms have evolved dispersal units, or propagules, that use the kinetic energy of the environment for movement. In plants, some dispersal units have tissue that assists with dispersal and are called diaspores. Some dispersal is self-driven (autochory), such as using gravity (barochory), and does not rely on external vectors. Other types of dispersal are due to external vectors, which can be biotic vectors, such as animals (zoochory), or abiotic vectors, such as the wind (anemochory) or water (hydrochory).In many cases, organisms will be dispersed by more than one vector before reaching its final destination. It is often a combination of two or more modes of dispersal that act together to maximize dispersal distance, such as wind blowing a seed into a nearby river, that will carry it farther down stream.

Himalayan quail

The Himalayan quail (Ophrysia superciliosa) or mountain quail is a medium-sized quail belonging to the pheasant family. It was last reported in 1876 and is feared extinct. This species was known from only 2 locations (and 12 specimens) in the western Himalayas in Uttarakhand, north-west India. The last verifiable record was in 1876 near the hill station of Mussoorie.

Jennifer Lee (scientist)

Jennifer Lee is an Antarctic researcher, best known for her work on invasion biology. She is the Environment Officer in the Government of South Georgia and the South Sandwich Islands.

List of birds of India

This is a list of the bird species of India and includes extant and recently extinct species recorded within the political limits of the Republic of India as defined by the Indian government are known to have around 1266 species as of 2016, of which sixty-one are endemic to the country, one has been introduced by humans and twenty-five are rare or accidental. Two species are suspected have been extirpated in India and eighty-two species are globally threatened. The Indian peafowl (Pavo cristatus) is the national bird of India. This list does not cover species in Indian jurisdiction areas such as Dakshin Gangothri and oceanic species are delineated by an arbitrary cutoff distance. The list does not include fossil bird species or escapees from captivity.

Two of the most recently discovered birds of India are the Himalayan forest thrush and Bugun liocichla both discovered in Arunachal Pradesh in 2016 and 2006. Also, a few birds considered to be extinct, such as the Jerdon's courser, have been rediscovered. Several species have been elevated from subspecies to full species.

The following tags have been used to highlight several categories. The commonly occurring native species do not fit within any of these categories.

(A) Accidental - Also known as a rarity, it refers to a species that rarely or accidentally occurs in India-typically less than ten confirmed records.

(E) Endemic - a species endemic to India

(Ex) Extirpated - a species that no longer occurs in India although populations exist elsewhere

(NB) Non-breeding range

Main Ridge, Tobago

Main Ridge is the main mountainous ridge on the island of Tobago, Trinidad and Tobago. It is a 29-kilometre (18 mi) chain of hills which runs from southwest to northeast between the Caribbean Sea and the Southern Tobago fault system and reaches a maximum height of 572 m (1,877 ft). The Main Ridge Forest Reserve, which was legally established in 1776, is one of the oldest protected areas in the world. It is an popular site for birdwatching and ecotourism. Main Ridge provides important habitat for native plants and animals, including several species endemic to Tobago.

Mola alexandrini

The southern sunfish (Mola alexandrini), also known as the Ramsay's sunfish, southern ocean sunfish, short sunfish or bump-head sunfish in many parts of the world, is a fish belonging to the family Molidae. It is closely related to its congener, much wider known Mola mola, and is found in the Southern Hemisphere. It can be found basking on its side occasionally near the surface, which is thought to be used to re-heat themselves after diving in cold water for prey, recharge their oxygen stores, and attract seagulls to free them of parasites.

Pest risk analysis

Pest risk analysis (PRA) is a form of risk analysis conducted by regulatory plant health authorities to identify the appropriate phytosanitary measures required to protect plant resources against new or emerging pests and regulated pests of plants or plant products. Specifically pest risk analysis is a term used within the International Plant Protection Convention (IPPC) (Article 2.1) and is defined within the glossary of phytosanitary terms. as "the process of evaluating biological or other scientific and economic evidence to determine whether an organism is a pest, whether it should be regulated, and the strength of any phytosanitary measures to be taken against it". In a phytosanitary context, the term plant pest, or simply pest, refers to any species, strain or biotype of plant, animal or pathogenic agent injurious to plants or plant products and includes plant pathogenic bacteria, fungi, fungus-like organisms, viruses and virus like organisms, as well as insects, mites, nematodes and weeds.

Phoebe L. Zarnetske

Phoebe L. Zarnetske is a community ecologist and associate professor in the Department of Forestry and in the Department of Fisheries and Wildlife at Michigan State University. Her work focuses on the ecological and evolutionary mechanisms that shape natural communities across multiple spatial scales.


Rhynchocyon is a genus of elephant shrew (or sengi) in the family Macroscelididae. Members of this genus are known colloquially as the checkered elephant shrews or giant sengis.

It contains the following four species:

Golden-rumped elephant shrew, Rhynchocyon chrysopygus

Checkered elephant shrew, Rhynchocyon cirnei

Black and rufous elephant shrew, Rhynchocyon petersi

Grey-faced sengi, Rhynchocyon udzungwensisThe giant sengis are endemic to Africa, and usually live in lowland montane and dense forests. They typically eat insects such as beetles, termites, and centipedes, using their proboscises to dig them from the soil and its tongue to lick them up. They typically build ground level nests for shelter requiring dry leaf litter. Sengis live in monogamous pairs, defending hectare-sized territories. R. chrysopyguus, R. cirnei, and R. petersi are allopatrically distributed; with the more recently discovered R. udzungwensis and subspecies R. cirnei reichardi exhibiting parapatric distributions. Some introgression (hybridization) has taken place between R. udzungwensis and R. cirnei as detected by mtDNA.

Food webs
Example webs
Ecology: Modelling ecosystems: Other components


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