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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
133:
187:). 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).
36:
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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.
347:
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. These models aim
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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
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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"
552:
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
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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 for a
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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
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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
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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
357:. 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.
276:
work with birds strongly established the role the environment plays in species distributions. Elgene O. Box constructed environmental envelope models to predict the range of tree species. His computer simulations were among the earliest uses of species distribution modelling.
1226:- 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;
376:
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
326:
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
170:
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
352:
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
348:
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.
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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
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Another example is
Ecocrop, which is used to determine the suitability of a crop to a specific environment. This database system can also project crop yields and evaluate the impact of environmental factors such as
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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.
528:
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.
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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,
408:
136:
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.
1214:- Online gathering place for scientists, practitioners, managers, and developers to discuss, support, and develop climate Environmental Niche Modeling tools and platforms
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454:
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Dispersal, biotic interactions, and evolutionary processes present challenges, as they aren’t usually incorporated into either correlative or mechanistic models.
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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.
1109:
221:, use independently derived information about a species' physiology to develop a model of the environmental conditions under which the species can exist.
912:
Morin, X.; Thuiller (2009). "Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change".
514:
183:. These models can be used to understand how environmental conditions influence the occurrence or abundance of a species, and for predictive purposes (
389:(MAXENT). Ten machine learning techiniques used in SDM can be seen in. An incomplete list of models that have been used for niche modelling includes:
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53:
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Kearney, Michael; Porter, Warren (2009). "Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges".
100:
72:
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Elith, Jane; Leathwick, John R. (2009-02-06). "Species
Distribution Models: Ecological Explanation and Prediction Across Space and Time".
2192:
1010:
Real, Raimundo; Barbosa, A. Márcia; Vargas, J. Mario (2006). "Obtaining
Environmental Favourability Functions from Logistic Regression".
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79:
478:
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is the most widely used method/software uses presence only data and performs well when there are few presence records available.
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environmental modelling increase the amount of environmental information available for model-building and made it easier to use.
86:
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2000:
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Elith J.; Leathwick J.R. (2009). "Species distribution models: ecological explanation and prediction across space and time".
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68:
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species range is not at equilibrium (e.g. if a species has been recently introduced and is actively expanding its range).
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979:"Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea"
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The
Climate-Smart Agriculture Papers: Investigating the Business of a Productive, Resilient and Low Emission Future
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2015:
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2010:
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284:(GLMs) made it possible to create more sophisticated and realistic species distribution models. The expansion of
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and the fundamental niche. Environmental niche modelling may be considered a part of the discipline of
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1238:- video of presentation by Aimee Stewart, Kansas University, at O'Reilly Where 2.0 Conference 2008
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Effrosynidis, Dimitrios; Tsikliras, Athanassios; Arampatzis, Avi; Sylaios, Georgios (2020-12-13).
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1947:
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Box, Elgene O. (1981-05-01). "Predicting physiognomic vegetation types with climate variables".
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models require many assumptions and parameter estimates, and they can become very complicated.
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distribution data as model input; and the influence of various factors such as barriers to
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962:
Nix HA (1986). "A biogeographic analysis of
Australian elapid snakes". In Longmore (ed.).
665:
209:, model the observed distribution of a species as a function of environmental conditions.
8:
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used geographical and environmental factors to explain plant distributions in his 1898
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and DOMAIN; "regression" methods (e.g. forms of generalized linear models); and "
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Algorithmic prediction of the distribution of a species across geographic space
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1110:"Species' distribution modeling for conservation educators and practitioners"
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has an easy to use (and good for educational use) implementation of BIOCLIM
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Nix HA (1986). "BIOCLIM — a
Bioclimatic Analysis and Prediction System".
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978:
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Atlas of Elapid Snakes of
Australia. Australian Flora and Fauna Series 7
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used the environment to explain the distribution of mammals in his 1866
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1250:- International Journal on Ecological Modelling and Systems Ecology
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Candela L.; Castelli D.; Coro G.; Pagano P.; Sinibaldi F. (2013).
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2005:
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1220:- online tool with workflows to generate ecological niche models
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The Biodiversity and Climate Change Virtual Laboratory (BCCVL)
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Most niche modelling methods are available in the R packages
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Rosenstock, Todd S.; Nowak, Andreea; Girvetz, Evan (2018).
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Research Report, CSIRO Division of Water and Land Resources
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750:(in German) (2nd ed.), Jena: Gustav Fischer Verlag,
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EUBrazilOpenBio SpeciesLab Virtual Research Environment
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comparison between mechanistic and correlative models.
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966:. Bureau of Flora and Fauna, Canberra. pp. 4–15.
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Concurrency and Computation: Practice and Experience
1145:
Annual Review of Ecology, Evolution, and Systematics
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Annual Review of Ecology, Evolution, and Systematics
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295:
589:The Collaboratory for Adaptation to Climate Change
60:. Unsourced material may be challenged and removed.
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747:Pflanzen-Geographie auf physiologischer Grundlage
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254:Pflanzengeographie auf physiologischer Grundlage
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582:Software developers may want to build on the
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1174:"Species distribution modeling in the cloud"
850:: CS1 maint: multiple names: authors list (
1244:- global predictive maps for marine species
1083:. Cham, Switzerland: Springer. p. 41.
233:, that increase the difference between the
2263:Latitudinal gradients in species diversity
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854:) CS1 maint: numeric names: authors list (
258:Plant Geography Upon a Physiological Basis
1031:
994:
479:Genetic Algorithm for Rule Set Production
156:predictive habitat distribution modelling
120:Learn how and when to remove this message
2161:Predator–prey (Lotka–Volterra) equations
1800:Tritrophic interactions in plant defense
798:The geographical distribution of mammals
444:Multivariate adaptive regression splines
266:The Geographical Distribution of Mammals
131:
2193:Random generalized Lotka–Volterra model
1212:Climate Envelope Modeling Working Group
1012:Environmental and Ecological Statistics
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940:
14:
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2001:Herbivore adaptations to plant defense
1218:BioVeL Ecological Niche Modeling (ENM)
961:
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521:Niche modelling software (correlative)
487:(BRT)/gradient boosting machines (GBM)
260:) and his 1908 work of the same name.
162:uses ecological models to predict the
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1283:
1232:- open source niche modelling library
1157:10.1146/annurev.ecolsys.110308.120159
666:10.1146/annurev.ecolsys.110308.120159
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148:(or ecological) niche modelling (ENM)
2016:Predator avoidance in schooling fish
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141:Species distribution modelling (SDM)
58:adding citations to reliable sources
29:
2466:Intermediate disturbance hypothesis
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342:
300:
280:The adoption of more sophisticated
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2219:Ecological effects of biodiversity
1101:
322:(the environments where a species
314:(the environments where a species
190:There are two main types of SDMs.
25:
2885:
1555:Generalist and specialist species
1205:
688:
564:on plant growth and suitability.
296:Correlative vs mechanistic models
2278:Occupancy–abundance relationship
1256:
801:, London: Day and Son, limited,
712:10.1111/j.1461-0248.2008.01277.x
409:Ecological niche factor analysis
69:"Species distribution modelling"
34:
2298:Relative abundance distribution
2011:Plant defense against herbivory
1878:Competitive exclusion principle
1590:Mesopredator release hypothesis
1072:
1048:
1003:
45:needs additional citations for
1883:Consumer–resource interactions
1269:environmental niche modelling
970:
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862:
785:
734:
602:online version of openModeller
13:
1:
2729:Biological data visualization
2556:Environmental niche modelling
2283:Population viability analysis
629:
2214:Density-dependent inhibition
540:implements various methods.
7:
2683:Liebig's law of the minimum
2518:Resource selection function
1409:Metabolic theory of ecology
607:
462:Machine learning techniques
426:Regression-based techniques
205:resource selection function
10:
2890:
2583:Niche apportionment models
2303:Relative species abundance
1507:Primary nutritional groups
1404:List of feeding behaviours
473:Artificial neural networks
438:Generalized additive model
372:Niche models (correlative)
318:found), as opposed to the
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2832:
2764:Ecosystem based fisheries
2706:
2606:
2531:
2404:
2376:Interspecific competition
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2268:Minimum viable population
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2126:Maximum sustainable yield
2111:Intraspecific competition
2106:Effective population size
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1986:Anti-predator adaptations
1971:
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1497:Photosynthetic efficiency
1432:
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1024:10.1007/s10651-005-0003-3
793:Murray, Andrew, 1812-1878
331:will be smaller than the
282:generalised linear models
2754:Ecological stoichiometry
2719:Alternative stable state
485:Boosted regression trees
432:Generalized linear model
239:biodiversity informatics
2874:Environmental modelling
2598:Ontogenetic niche shift
2461:Ideal free distribution
2371:Ecological facilitation
2121:Malthusian growth model
2091:Consumer-resource model
1948:Paradox of the plankton
1913:Energy systems language
1633:Chemoorganoheterotrophy
1600:Optimal foraging theory
1575:Heterotrophic nutrition
1117:Lessons in Conservation
807:10.5962/BHL.TITLE.15762
756:10.5962/BHL.TITLE.46243
497:Support vector machines
288:and the development of
229:, geologic history, or
196:climate envelope models
2744:Ecological forecasting
2688:Marginal value theorem
2486:Landscape epidemiology
2421:Cross-boundary subsidy
2356:Biological interaction
1706:Microbial intelligence
1394:Green world hypothesis
1108:Pearson, R.G. (2007).
455:Favourability Function
185:ecological forecasting
137:
2749:Ecological humanities
2648:Ecological energetics
2593:Niche differentiation
2456:Habitat fragmentation
2224:Ecological extinction
2171:Small population size
1923:Feed conversion ratio
1903:Ecological succession
1835:San Francisco Estuary
1749:Ecological efficiency
1691:Microbial cooperation
272:work with plants and
135:
2774:Evolutionary ecology
2739:Ecological footprint
2734:Ecological economics
2658:Ecological threshold
2653:Ecological indicator
2523:Source–sink dynamics
2476:Land change modeling
2471:Insular biogeography
2323:Species distribution
2062:Modelling ecosystems
1721:Microbial metabolism
1560:Intraguild predation
1349:Biogeochemical cycle
1315:Modelling ecosystems
1248:Ecological Modelling
415:Mahalanobis distance
215:process-based models
173:conservation biology
166:of a species across
54:improve this article
2864:Information science
2824:Theoretical ecology
2799:Natural environment
2663:Ecosystem diversity
2633:Ecological collapse
2623:Bateman's principle
2578:Limiting similarity
2491:Landscape limnology
2313:Species homogeneity
2151:Population modeling
2146:Population dynamics
1963:Trophic state index
996:10.3390/app10248900
951:. 1983–1985: 59–60.
600:has implemented an
355:population dynamics
231:biotic interactions
2835:Outline of ecology
2784:Industrial ecology
2779:Functional ecology
2643:Ecological deficit
2588:Niche construction
2551:Ecosystem engineer
2328:Species–area curve
2249:Introduced species
2064:: Other components
1996:Deimatic behaviour
1898:Ecological network
1830:North Pacific Gyre
1815:hydrothermal vents
1754:Ecological pyramid
1701:Microbial food web
1512:Primary production
1457:Foundation species
1266:has a profile for
883:10.1007/BF00119222
596:2012-08-06 at the
393:Profile techniques
385:" methods such as
274:Robert MacArthur's
270:Robert Whittaker's
219:biophysical models
200:bioclimatic models
138:
2869:Landscape ecology
2841:
2840:
2724:Balance of nature
2481:Landscape ecology
2366:Community ecology
2308:Species diversity
2244:Indicator species
2239:Gradient analysis
2116:Logistic function
2024:
2023:
1981:Animal coloration
1958:Trophic mutualism
1696:Microbial ecology
1487:Photoheterotrophs
1472:Myco-heterotrophy
1384:Ecosystem ecology
1369:Carrying capacity
1334:Abiotic component
1272:
926:10.1890/08-0134.1
742:A. F. W. Schimper
624:Quantum evolution
333:fundamental niche
320:fundamental niche
250:A. F. W. Schimper
152:habitat modelling
130:
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16:(Redirected from
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2859:Ecological niche
2541:Ecological niche
2513:selection theory
2333:Umbrella species
2318:Species richness
2254:Invasive species
2234:Flagship species
2141:Population cycle
2136:Overexploitation
2101:Ecological yield
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1933:Mesotrophic soil
1873:Climax community
1805:Marine food webs
1744:Biomagnification
1545:Chemoorganotroph
1399:Keystone species
1359:Biotic component
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1184:(4): 1056–1079.
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1131:. Archived from
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515:consensus models
383:machine learning
343:Mechanistic SDMs
301:Correlative SDMs
213:, also known as
211:Mechanistic SDMs
194:, also known as
192:Correlative SDMs
143:, also known as
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2673:Extinction debt
2638:Ecological debt
2628:Bioluminescence
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2571:marine habitats
2546:Ecological trap
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2293:Rapoport's rule
2288:Priority effect
2229:Endemic species
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2156:Population size
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1953:Trophic cascade
1863:Bioaccumulation
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1428:
1389:Ecosystem model
1322:
1308:
1278:
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1276:
1261:
1257:
1208:
1135:
1112:
1104:
1102:Further reading
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843:
842:
790:
786:
739:
735:
700:Ecology Letters
696:
689:
650:
637:
632:
619:Ecosystem model
610:
598:Wayback Machine
523:
511:ensemble models
464:
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420:Isodar analysis
395:
387:maximum entropy
374:
345:
303:
298:
247:
126:
115:
109:
106:
63:
61:
51:
39:
28:
23:
22:
18:Niche modelling
15:
12:
11:
5:
2887:
2877:
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2856:
2839:
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2833:
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2806:
2801:
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2794:Microecosystem
2791:
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2736:
2731:
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2721:
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2700:
2695:
2693:Thorson's rule
2690:
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2680:
2675:
2670:
2665:
2660:
2655:
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2645:
2640:
2635:
2630:
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2618:Assembly rules
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2604:
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2601:
2600:
2595:
2590:
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2580:
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2574:
2573:
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2548:
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2537:
2535:
2529:
2528:
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2520:
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2503:
2501:Patch dynamics
2498:
2496:Metapopulation
2493:
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2473:
2468:
2463:
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2391:Storage effect
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2305:
2300:
2295:
2290:
2285:
2280:
2275:
2273:Neutral theory
2270:
2265:
2260:
2258:Native species
2251:
2246:
2241:
2236:
2231:
2226:
2221:
2216:
2211:
2205:
2203:
2199:
2198:
2196:
2195:
2190:
2189:
2188:
2183:
2173:
2168:
2163:
2158:
2153:
2148:
2143:
2138:
2133:
2131:Overpopulation
2128:
2123:
2118:
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2108:
2103:
2098:
2093:
2088:
2083:
2077:
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2067:
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2031:
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2021:
2019:
2018:
2013:
2008:
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1998:
1993:
1988:
1983:
1977:
1975:
1969:
1968:
1966:
1965:
1960:
1955:
1950:
1945:
1940:
1938:Nutrient cycle
1935:
1930:
1928:Feeding frenzy
1925:
1920:
1915:
1910:
1908:Energy quality
1905:
1900:
1895:
1890:
1885:
1880:
1875:
1870:
1868:Cascade effect
1865:
1860:
1854:
1852:
1848:
1847:
1845:
1844:
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1842:
1837:
1832:
1827:
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1711:Microbial loop
1708:
1703:
1698:
1693:
1688:
1683:
1678:
1676:Lithoautotroph
1673:
1668:
1662:
1660:
1658:Microorganisms
1654:
1653:
1651:
1650:
1645:
1640:
1635:
1629:
1627:
1621:
1620:
1618:
1617:
1615:Prey switching
1612:
1607:
1602:
1597:
1592:
1587:
1582:
1577:
1572:
1567:
1562:
1557:
1552:
1547:
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1532:
1526:
1524:
1518:
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1515:
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1509:
1504:
1499:
1494:
1492:Photosynthesis
1489:
1484:
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1474:
1469:
1464:
1459:
1454:
1449:
1447:Chemosynthesis
1444:
1438:
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1427:
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1421:
1416:
1411:
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1396:
1391:
1386:
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1376:
1371:
1366:
1361:
1356:
1351:
1346:
1341:
1339:Abiotic stress
1336:
1330:
1328:
1324:
1323:
1307:
1306:
1299:
1292:
1284:
1262:
1255:
1254:
1253:
1252:
1251:
1245:
1239:
1236:Lifemapper 2.0
1233:
1227:
1221:
1215:
1207:
1206:External links
1204:
1203:
1202:
1169:
1140:
1138:on 2019-02-26.
1103:
1100:
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1089:
1071:
1047:
1018:(2): 237–245.
1002:
969:
954:
939:
920:(5): 1301–13.
904:
877:(2): 127–139.
861:
784:
733:
706:(4): 334–350.
687:
660:(1): 677–697.
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633:
631:
628:
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562:climate change
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329:realized niche
312:realized niche
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286:remote sensing
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235:realized niche
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2819:Urban ecology
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2709:
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2699:
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2678:Kleiber's law
2676:
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2451:Foster's rule
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2038:
2033:
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2017:
2014:
2012:
2009:
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2002:
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1997:
1994:
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1989:
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1984:
1982:
1979:
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1769:Trophic level
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1737:
1733:
1727:
1726:Phage ecology
1724:
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1717:
1716:Microbial mat
1714:
1712:
1709:
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1702:
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1697:
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1674:
1672:
1671:Bacteriophage
1669:
1667:
1664:
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1659:
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1641:
1639:
1638:Decomposition
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1585:Mesopredators
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1530:Apex predator
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1390:
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1377:
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1365:
1364:Biotic stress
1362:
1360:
1357:
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1105:
1092:
1090:9783319927978
1086:
1082:
1075:
1061:
1060:ECHOcommunity
1057:
1056:"FAO Ecocrop"
1051:
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539:
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533:
529:
527:
518:
516:
512:
509:Furthermore,
504:
501:
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492:
491:Random forest
489:
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351:
350:micro-climate
340:
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317:
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287:
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278:
275:
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263:
262:Andrew Murray
259:
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208:
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201:
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174:
169:
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161:
160:range mapping
157:
153:
149:
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145:environmental
142:
134:
124:
121:
113:
110:December 2018
102:
99:
95:
92:
88:
85:
81:
78:
74:
71: –
70:
66:
65:Find sources:
59:
55:
49:
48:
43:This article
41:
37:
32:
31:
19:
2854:Biogeography
2804:Regime shift
2789:Macroecology
2555:
2510:
2506:
2446:Edge effects
2416:Biogeography
2361:Commensalism
2209:Biodiversity
2086:Allee effect
1825:kelp forests
1778:Example webs
1643:Detritivores
1482:Organotrophs
1462:Kinetotrophs
1414:Productivity
1268:
1230:openModeller
1181:
1177:
1148:
1144:
1133:the original
1120:
1116:
1080:
1074:
1063:. Retrieved
1059:
1050:
1015:
1011:
1005:
989:(24): 8900.
986:
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864:
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746:
736:
703:
699:
657:
653:
614:Biogeography
591:adapt.nd.edu
588:
584:openModeller
581:
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164:distribution
159:
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147:
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140:
139:
116:
107:
97:
90:
83:
76:
64:
52:Please help
47:verification
44:
2441:Disturbance
2344:interaction
2166:Recruitment
2096:Depensation
1888:Copiotrophs
1759:Energy flow
1681:Lithotrophy
1625:Decomposers
1605:Planktivore
1580:Insectivore
1570:Heterotroph
1535:Bacterivore
1502:Phototrophs
1452:Chemotrophs
1424:Restoration
1374:Competition
1151:: 677–697.
1033:10174/20244
2848:Categories
2809:Sexecology
2386:Parasitism
2351:Antibiosis
2186:Resistance
2181:Resilience
2071:Population
1991:Camouflage
1943:Oligotroph
1858:Ascendency
1820:intertidal
1810:cold seeps
1764:Food chain
1565:Herbivores
1540:Carnivores
1467:Mixotrophs
1442:Autotrophs
1321:components
1271:(Q5381340)
1065:2019-08-19
780:Q117084350
630:References
168:geographic
80:newspapers
2714:Allometry
2668:Emergence
2396:Symbiosis
2381:Mutualism
2176:Stability
2081:Abundance
1893:Dominance
1851:Processes
1840:tide pool
1736:Food webs
1610:Predation
1595:Omnivores
1522:Consumers
1477:Mycotroph
1434:Producers
1379:Ecosystem
1344:Behaviour
1123:: 54–89.
891:1573-5052
871:Vegetatio
839:Q51421963
831:16272962M
772:24353101M
720:1461-0248
674:1543-592X
586:project.
573:'biomod2'
290:GIS-based
227:dispersal
181:evolution
2769:Endolith
2698:Xerosere
2610:networks
2426:Ecocline
1972:Defense,
1648:Detritus
1550:Foraging
1419:Resource
1242:AquaMaps
1198:45203952
1165:86460963
1129:40254248
1042:34887643
934:19537550
899:25941018
846:citation
835:Wikidata
815:04035567
795:(1866),
776:Wikidata
764:12120623
744:(1908),
728:19292794
682:86460963
608:See also
594:Archived
544:DIVA-GIS
2759:Ecopath
2566:Habitat
2436:Ecotype
2431:Ecotone
2408:ecology
2406:Spatial
2342:Species
2202:Species
2073:ecology
2058:Ecology
2006:Mimicry
1974:counter
1918:f-ratio
1666:Archaea
1354:Biomass
1327:General
1319:Trophic
1311:Ecology
1264:Scholia
914:Ecology
823:8680065
569:'dismo'
503:XGBoost
450:Maxlike
399:BIOCLIM
379:BIOCLIM
245:History
177:ecology
94:scholar
1790:Rivers
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577:'mopa'
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526:SPACES
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468:MAXENT
446:(MARS)
411:(ENFA)
404:DOMAIN
207:models
158:, and
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2608:Other
2561:Guild
2533:Niche
1785:Lakes
1194:S2CID
1161:S2CID
1136:(PDF)
1125:S2CID
1113:(PDF)
1038:S2CID
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678:S2CID
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499:(SVM)
475:(ANN)
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202:, or
101:JSTOR
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1085:ISBN
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