This article needs additional citations for verification .(December 2018) |
Species distribution modelling (SDM), also known as environmental(or ecological) niche modelling (ENM), habitat modelling, predictive habitat distribution modelling, and range mapping [1] uses ecological models 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.
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) and his 1908 work of the same name. [3] Andrew Murray used the environment to explain the distribution of mammals in his 1866 The Geographical Distribution of Mammals. [4] 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. [5] 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]
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, a model defines 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 models 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 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 [6] for a comparison between mechanistic and correlative models.
There are a variety of mathematical methods that can be used for fitting, selecting, and evaluating correlative SDMs. Models include "profile" methods, which are simple statistical techniques that use e.g. environmental distance to known sites of occurrence such as BIOCLIM [7] [8] and DOMAIN; "regression" methods (e.g. forms of generalized linear models); and "machine learning" methods such as maximum entropy (MAXENT). Ten machine learning techiniques used in SDM can be seen in. [9] An incomplete list of models that have been used for niche modelling includes:
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.
SPACES is an online Environmental niche modeling platform that allows users to design and run dozens of the most prominent methods 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 methods.
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 methods, and easily visualize, 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
Another example is Ecocrop, which is used to determine the suitability of a crop to a specific environment. [11] This database system can also project crop yields and evaluate the impact of environmental factors such as climate change on plant growth and suitability. [12]
Most niche modelling methods 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 Archived 2012-08-06 at the Wayback Machine 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.
Ecology is the natural science of the relationships among living organisms, including humans, and their physical environment. Ecology considers organisms at the individual, population, community, ecosystem, and biosphere levels. Ecology overlaps with the closely related sciences of biogeography, evolutionary biology, genetics, ethology, and natural history.
Theoretical ecology is the scientific discipline devoted to the study of ecological systems using theoretical methods such as simple conceptual models, mathematical models, computational simulations, and advanced data analysis. Effective models improve understanding of the natural world by revealing how the dynamics of species populations are often based on fundamental biological conditions and processes. Further, the field aims to unify a diverse range of empirical observations by assuming that common, mechanistic processes generate observable phenomena across species and ecological environments. Based on biologically realistic assumptions, theoretical ecologists are able to uncover novel, non-intuitive insights about natural processes. Theoretical results are often verified by empirical and observational studies, revealing the power of theoretical methods in both predicting and understanding the noisy, diverse biological world.
In ecology, a niche is the match of a species to a specific environmental condition. It describes how an organism or population responds to the distribution of resources and competitors and how it in turn alters those same factors. "The type and number of variables comprising the dimensions of an environmental niche vary from one species to another [and] the relative importance of particular environmental variables for a species may vary according to the geographic and biotic contexts".
Biogeography is the study of the distribution of species and ecosystems in geographic space and through geological time. Organisms and biological communities often vary in a regular fashion along geographic gradients of latitude, elevation, isolation and habitat area. Phytogeography is the branch of biogeography that studies the distribution of plants. Zoogeography is the branch that studies distribution of animals. Mycogeography is the branch that studies distribution of fungi, such as mushrooms.
Paleoecology is the study of interactions between organisms and/or interactions between organisms and their environments across geologic timescales. As a discipline, paleoecology interacts with, depends on and informs a variety of fields including paleontology, ecology, climatology and biology.
Biological dispersal refers to both the movement of individuals from their birth site to their breeding site, as well as the movement from one breeding site to another . Dispersal is also used to describe the movement of propagules such as seeds and spores. Technically, dispersal is defined as any movement that has the potential to lead to gene flow. The act of dispersal involves three phases: departure, transfer, and settlement. There are different fitness costs and benefits associated with each of these phases. Through simply moving from one habitat patch to another, the dispersal of an individual has consequences not only for individual fitness, but also for population dynamics, population genetics, and species distribution. Understanding dispersal and the consequences, both for evolutionary strategies at a species level and for processes at an ecosystem level, requires understanding on the type of dispersal, the dispersal range of a given species, and the dispersal mechanisms involved. Biological dispersal can be correlated to population density. The range of variations of a species' location determines the expansion range.
Realized niche width is a phrase relating to ecology, is defined by the actual space that an organism inhabits and the resources it can access as a result of limiting pressures from other species. An organism's ecological niche is determined by the biotic and abiotic factors that make up that specific ecosystem that allow that specific organism to survive there. The width of an organism's niche is set by the range of conditions a species is able to survive in that specific environment.
Spatial ecology studies the ultimate distributional or spatial unit occupied by a species. In a particular habitat shared by several species, each of the species is usually confined to its own microhabitat or spatial niche because two species in the same general territory cannot usually occupy the same ecological niche for any significant length of time.
Ensemble forecasting is a method used in or within numerical weather prediction. Instead of making a single forecast of the most likely weather, a set of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere.
Health geography is the application of geographical information, perspectives, and methods to the study of health, disease, and health care. Medical geography, a sub-discipline of, or sister field of health geography, focuses on understanding spatial patterns of health and disease in relation to the natural and social environment. Conventionally, there are two primary areas of research within medical geography: the first deals with the spatial distribution and determinants of morbidity and mortality, while the second deals with health planning, help-seeking behavior, and the provision of health services.
Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.
Species distribution, or speciesdispersion, is the manner in which a biological taxon is spatially arranged. The geographic limits of a particular taxon's distribution is its range, often represented as shaded areas on a map. Patterns of distribution change depending on the scale at which they are viewed, from the arrangement of individuals within a small family unit, to patterns within a population, or the distribution of the entire species as a whole (range). Species distribution is not to be confused with dispersal, which is the movement of individuals away from their region of origin or from a population center of high density.
The following outline is provided as an overview of and topical guide to ecology:
In ecology, the occupancy–abundance (O–A) relationship is the relationship between the abundance of species and the size of their ranges within a region. This relationship is perhaps one of the most well-documented relationships in macroecology, and applies both intra- and interspecifically. In most cases, the O–A relationship is a positive relationship. Although an O–A relationship would be expected, given that a species colonizing a region must pass through the origin and could reach some theoretical maximum abundance and distribution, the relationship described here is somewhat more substantial, in that observed changes in range are associated with greater-than-proportional changes in abundance. Although this relationship appears to be pervasive, and has important implications for the conservation of endangered species, the mechanism(s) underlying it remain poorly understood.
Ecological forecasting uses knowledge of physics, ecology and physiology to predict how ecological populations, communities, or ecosystems will change in the future in response to environmental factors such as climate change. The goal of the approach is to provide natural resource managers with information to anticipate and respond to short and long-term climate conditions.
Relative species abundance is a component of biodiversity and is a measure of how common or rare a species is relative to other species in a defined location or community. Relative abundance is the percent composition of an organism of a particular kind relative to the total number of organisms in the area. Relative species abundances tend to conform to specific patterns that are among the best-known and most-studied patterns in macroecology. Different populations in a community exist in relative proportions; this idea is known as relative abundance.
Professor Jane Elith is an ecologist in the School of Botany at the University of Melbourne. She graduated from the School of Agriculture and Forestry at the University of Melbourne in 1977. She specialises in ecological models that focus on spatial analysis and prediction of the habitat of plant and animal species. Following graduation, she was a research assistant and tutor for three years, and then spent the following 12 years raising her children. She returned to the University of Melbourne in 1992 and later commenced a part-time PhD in the School of Botany. She was awarded her PhD in 2002 on 'Predicting the distribution of plants'. Since then, she has been a research fellow in the School of Botany. She is currently an ARC Future Fellow and sits within the Centre of Excellence for Biosecurity Risk Analysis at the University of Melbourne.
Climate change and invasive species refers to the process of the environmental destabilization caused by climate change. This environmental change facilitates the spread of invasive species — species that are not historically found in a certain region, and often bring about a negative impact to that region's native species. This complex relationship is notable because climate change and invasive species are also considered by the USDA to be two of the top four causes of global biodiversity loss.
Lauren B. Buckley is an evolutionary ecologist and professor of biology at the University of Washington. She researches the relationship between organismal physiological and life history features and response to global climate change.
Warren P. Porter is a biophysical ecologist, environmental toxicologist, and an academic. He is an emeritus Professor in the Department of Integrative Biology at the University of Wisconsin, Madison.
{{citation}}
: CS1 maint: multiple names: authors list (link) CS1 maint: numeric names: authors list (link)