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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
122:
176:). 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).
25:
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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.
336:
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"
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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
346:. 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.
265:
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.
1215:- 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;
365:
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
315:
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
159:
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
341:
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
337:
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.
299:
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
548:
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
593:
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.
517:
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.
502:
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,
397:
125:
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.
1203:- 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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443:
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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.
1098:
210:, use independently derived information about a species' physiology to develop a model of the environmental conditions under which the species can exist.
901:
Morin, X.; Thuiller (2009). "Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change".
503:
172:. These models can be used to understand how environmental conditions influence the occurrence or abundance of a species, and for predictive purposes (
378:(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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42:
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Kearney, Michael; Porter, Warren (2009). "Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges".
89:
61:
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Elith, Jane; Leathwick, John R. (2009-02-06). "Species
Distribution Models: Ecological Explanation and Prediction Across Space and Time".
2181:
999:
Real, Raimundo; Barbosa, A. Márcia; Vargas, J. Mario (2006). "Obtaining
Environmental Favourability Functions from Logistic Regression".
2251:
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68:
467:
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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.
75:
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1989:
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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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57:
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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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968:"Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea"
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2030:
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The
Climate-Smart Agriculture Papers: Investigating the Business of a Productive, Resilient and Low Emission Future
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2004:
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1999:
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273:(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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2015:
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1227:- 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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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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152:
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951:
Nix HA (1986). "A biogeographic analysis of
Australian elapid snakes". In Longmore (ed.).
654:
198:, 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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1307:
1099:"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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953:
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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1239:- 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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1994:
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1209:- online tool with workflows to generate ecological niche models
803:
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539:
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).
938:
Research Report, CSIRO Division of Water and Land Resources
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739:(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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955:. Bureau of Flora and Fauna, Canberra. pp. 4–15.
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Concurrency and Computation: Practice and Experience
1134:
Annual Review of Ecology, Evolution, and Systematics
643:
Annual Review of Ecology, Evolution, and Systematics
625:
284:
578:The Collaboratory for Adaptation to Climate Change
49:. Unsourced material may be challenged and removed.
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736:Pflanzen-Geographie auf physiologischer Grundlage
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243:Pflanzengeographie auf physiologischer Grundlage
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571:Software developers may want to build on the
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1163:"Species distribution modeling in the cloud"
839:: CS1 maint: multiple names: authors list (
1233:- global predictive maps for marine species
1072:. Cham, Switzerland: Springer. p. 41.
222:, that increase the difference between the
2252:Latitudinal gradients in species diversity
2038:
2024:
1291:
1277:
843:) CS1 maint: numeric names: authors list (
247:Plant Geography Upon a Physiological Basis
1020:
983:
468:Genetic Algorithm for Rule Set Production
145:predictive habitat distribution modelling
109:Learn how and when to remove this message
2150:Predator–prey (Lotka–Volterra) equations
1789:Tritrophic interactions in plant defense
787:The geographical distribution of mammals
433:Multivariate adaptive regression splines
255:The Geographical Distribution of Mammals
120:
2182:Random generalized Lotka–Volterra model
1201:Climate Envelope Modeling Working Group
1001:Environmental and Ecological Statistics
944:
929:
2835:
1990:Herbivore adaptations to plant defense
1207:BioVeL Ecological Niche Modeling (ENM)
950:
935:
510:Niche modelling software (correlative)
476:(BRT)/gradient boosting machines (GBM)
249:) and his 1908 work of the same name.
151:uses ecological models to predict the
2019:
1272:
1221:- open source niche modelling library
1146:10.1146/annurev.ecolsys.110308.120159
655:10.1146/annurev.ecolsys.110308.120159
381:
137:(or ecological) niche modelling (ENM)
2005:Predator avoidance in schooling fish
682:
680:
130:Species distribution modelling (SDM)
47:adding citations to reliable sources
18:
2455:Intermediate disturbance hypothesis
857:
331:
289:
269:The adoption of more sophisticated
13:
2208:Ecological effects of biodiversity
1090:
311:(the environments where a species
303:(the environments where a species
179:There are two main types of SDMs.
14:
2874:
1544:Generalist and specialist species
1194:
677:
553:on plant growth and suitability.
285:Correlative vs mechanistic models
2267:Occupancy–abundance relationship
1245:
790:, London: Day and Son, limited,
701:10.1111/j.1461-0248.2008.01277.x
398:Ecological niche factor analysis
58:"Species distribution modelling"
23:
2287:Relative abundance distribution
2000:Plant defense against herbivory
1867:Competitive exclusion principle
1579:Mesopredator release hypothesis
1061:
1037:
992:
34:needs additional citations for
1872:Consumer–resource interactions
1258:environmental niche modelling
959:
894:
851:
774:
723:
591:online version of openModeller
1:
2718:Biological data visualization
2545:Environmental niche modelling
2272:Population viability analysis
618:
2203:Density-dependent inhibition
529:implements various methods.
7:
2672:Liebig's law of the minimum
2507:Resource selection function
1398:Metabolic theory of ecology
596:
451:Machine learning techniques
415:Regression-based techniques
194:resource selection function
10:
2879:
2572:Niche apportionment models
2292:Relative species abundance
1496:Primary nutritional groups
1393:List of feeding behaviours
462:Artificial neural networks
427:Generalized additive model
361:Niche models (correlative)
307:found), as opposed to the
233:
2821:
2753:Ecosystem based fisheries
2695:
2595:
2520:
2393:
2365:Interspecific competition
2330:
2257:Minimum viable population
2190:
2115:Maximum sustainable yield
2100:Intraspecific competition
2095:Effective population size
2058:
1975:Anti-predator adaptations
1960:
1839:
1766:
1723:
1645:
1612:
1509:
1486:Photosynthetic efficiency
1421:
1315:
1013:10.1007/s10651-005-0003-3
782:Murray, Andrew, 1812-1878
320:will be smaller than the
271:generalised linear models
2743:Ecological stoichiometry
2708:Alternative stable state
474:Boosted regression trees
421:Generalized linear model
228:biodiversity informatics
2863:Environmental modelling
2587:Ontogenetic niche shift
2450:Ideal free distribution
2360:Ecological facilitation
2110:Malthusian growth model
2080:Consumer-resource model
1937:Paradox of the plankton
1902:Energy systems language
1622:Chemoorganoheterotrophy
1589:Optimal foraging theory
1564:Heterotrophic nutrition
1106:Lessons in Conservation
796:10.5962/BHL.TITLE.15762
745:10.5962/BHL.TITLE.46243
486:Support vector machines
277:and the development of
218:, geologic history, or
185:climate envelope models
2733:Ecological forecasting
2677:Marginal value theorem
2475:Landscape epidemiology
2410:Cross-boundary subsidy
2345:Biological interaction
1695:Microbial intelligence
1383:Green world hypothesis
1097:Pearson, R.G. (2007).
444:Favourability Function
174:ecological forecasting
126:
2738:Ecological humanities
2637:Ecological energetics
2582:Niche differentiation
2445:Habitat fragmentation
2213:Ecological extinction
2160:Small population size
1912:Feed conversion ratio
1892:Ecological succession
1824:San Francisco Estuary
1738:Ecological efficiency
1680:Microbial cooperation
261:work with plants and
124:
2763:Evolutionary ecology
2728:Ecological footprint
2723:Ecological economics
2647:Ecological threshold
2642:Ecological indicator
2512:Source–sink dynamics
2465:Land change modeling
2460:Insular biogeography
2312:Species distribution
2051:Modelling ecosystems
1710:Microbial metabolism
1549:Intraguild predation
1338:Biogeochemical cycle
1304:Modelling ecosystems
1237:Ecological Modelling
404:Mahalanobis distance
204:process-based models
162:conservation biology
155:of a species across
43:improve this article
2853:Information science
2813:Theoretical ecology
2788:Natural environment
2652:Ecosystem diversity
2622:Ecological collapse
2612:Bateman's principle
2567:Limiting similarity
2480:Landscape limnology
2302:Species homogeneity
2140:Population modeling
2135:Population dynamics
1952:Trophic state index
985:10.3390/app10248900
940:. 1983–1985: 59–60.
589:has implemented an
344:population dynamics
220:biotic interactions
2824:Outline of ecology
2773:Industrial ecology
2768:Functional ecology
2632:Ecological deficit
2577:Niche construction
2540:Ecosystem engineer
2317:Species–area curve
2238:Introduced species
2053:: Other components
1985:Deimatic behaviour
1887:Ecological network
1819:North Pacific Gyre
1804:hydrothermal vents
1743:Ecological pyramid
1690:Microbial food web
1501:Primary production
1446:Foundation species
1255:has a profile for
872:10.1007/BF00119222
585:2012-08-06 at the
382:Profile techniques
374:" methods such as
263:Robert MacArthur's
259:Robert Whittaker's
208:biophysical models
189:bioclimatic models
127:
2858:Landscape ecology
2830:
2829:
2713:Balance of nature
2470:Landscape ecology
2355:Community ecology
2297:Species diversity
2233:Indicator species
2228:Gradient analysis
2105:Logistic function
2013:
2012:
1970:Animal coloration
1947:Trophic mutualism
1685:Microbial ecology
1476:Photoheterotrophs
1461:Myco-heterotrophy
1373:Ecosystem ecology
1358:Carrying capacity
1323:Abiotic component
1261:
915:10.1890/08-0134.1
731:A. F. W. Schimper
613:Quantum evolution
322:fundamental niche
309:fundamental niche
239:A. F. W. Schimper
141:habitat modelling
119:
118:
111:
93:
2870:
2848:Ecological niche
2530:Ecological niche
2502:selection theory
2322:Umbrella species
2307:Species richness
2243:Invasive species
2223:Flagship species
2130:Population cycle
2125:Overexploitation
2090:Ecological yield
2040:
2033:
2026:
2017:
2016:
1922:Mesotrophic soil
1862:Climax community
1794:Marine food webs
1733:Biomagnification
1534:Chemoorganotroph
1388:Keystone species
1348:Biotic component
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1286:
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1190:
1179:10.1002/cpe.3030
1173:(4): 1056–1079.
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1128:
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1120:. Archived from
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1056:
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1041:
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1024:
996:
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972:Applied Sciences
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778:
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721:
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684:
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638:
504:consensus models
372:machine learning
332:Mechanistic SDMs
290:Correlative SDMs
202:, also known as
200:Mechanistic SDMs
183:, also known as
181:Correlative SDMs
132:, also known as
114:
107:
103:
100:
94:
92:
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27:
19:
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2817:
2803:Systems ecology
2691:
2662:Extinction debt
2627:Ecological debt
2617:Bioluminescence
2598:
2591:
2560:marine habitats
2535:Ecological trap
2516:
2396:
2389:
2332:
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2282:Rapoport's rule
2277:Priority effect
2218:Endemic species
2186:
2145:Population size
2061:
2054:
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2014:
2009:
1962:
1956:
1942:Trophic cascade
1852:Bioaccumulation
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1505:
1417:
1378:Ecosystem model
1311:
1297:
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1265:
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1093:
1091:Further reading
1088:
1087:
1080:
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1043:
1042:
1038:
997:
993:
964:
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856:
852:
832:
831:
779:
775:
728:
724:
689:Ecology Letters
685:
678:
639:
626:
621:
608:Ecosystem model
599:
587:Wayback Machine
512:
500:ensemble models
453:
417:
409:Isodar analysis
384:
376:maximum entropy
363:
334:
292:
287:
236:
115:
104:
98:
95:
52:
50:
40:
28:
17:
12:
11:
5:
2876:
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2822:
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2810:
2805:
2800:
2795:
2790:
2785:
2783:Microecosystem
2780:
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2770:
2765:
2760:
2755:
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2745:
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2725:
2720:
2715:
2710:
2705:
2699:
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2693:
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2690:
2689:
2684:
2682:Thorson's rule
2679:
2674:
2669:
2664:
2659:
2654:
2649:
2644:
2639:
2634:
2629:
2624:
2619:
2614:
2609:
2607:Assembly rules
2603:
2601:
2593:
2592:
2590:
2589:
2584:
2579:
2574:
2569:
2564:
2563:
2562:
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2537:
2532:
2526:
2524:
2518:
2517:
2515:
2514:
2509:
2504:
2492:
2490:Patch dynamics
2487:
2485:Metapopulation
2482:
2477:
2472:
2467:
2462:
2457:
2452:
2447:
2442:
2437:
2432:
2427:
2422:
2417:
2412:
2407:
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2399:
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2388:
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2382:
2380:Storage effect
2377:
2372:
2367:
2362:
2357:
2352:
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2342:
2336:
2334:
2328:
2327:
2325:
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2319:
2314:
2309:
2304:
2299:
2294:
2289:
2284:
2279:
2274:
2269:
2264:
2262:Neutral theory
2259:
2254:
2249:
2247:Native species
2240:
2235:
2230:
2225:
2220:
2215:
2210:
2205:
2200:
2194:
2192:
2188:
2187:
2185:
2184:
2179:
2178:
2177:
2172:
2162:
2157:
2152:
2147:
2142:
2137:
2132:
2127:
2122:
2120:Overpopulation
2117:
2112:
2107:
2102:
2097:
2092:
2087:
2082:
2077:
2072:
2066:
2064:
2056:
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2028:
2020:
2011:
2010:
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2002:
1997:
1992:
1987:
1982:
1977:
1972:
1966:
1964:
1958:
1957:
1955:
1954:
1949:
1944:
1939:
1934:
1929:
1927:Nutrient cycle
1924:
1919:
1917:Feeding frenzy
1914:
1909:
1904:
1899:
1897:Energy quality
1894:
1889:
1884:
1879:
1874:
1869:
1864:
1859:
1857:Cascade effect
1854:
1849:
1843:
1841:
1837:
1836:
1834:
1833:
1832:
1831:
1826:
1821:
1816:
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1740:
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1729:
1727:
1721:
1720:
1718:
1717:
1712:
1707:
1702:
1700:Microbial loop
1697:
1692:
1687:
1682:
1677:
1672:
1667:
1665:Lithoautotroph
1662:
1657:
1651:
1649:
1647:Microorganisms
1643:
1642:
1640:
1639:
1634:
1629:
1624:
1618:
1616:
1610:
1609:
1607:
1606:
1604:Prey switching
1601:
1596:
1591:
1586:
1581:
1576:
1571:
1566:
1561:
1556:
1551:
1546:
1541:
1536:
1531:
1526:
1521:
1515:
1513:
1507:
1506:
1504:
1503:
1498:
1493:
1488:
1483:
1481:Photosynthesis
1478:
1473:
1468:
1463:
1458:
1453:
1448:
1443:
1438:
1436:Chemosynthesis
1433:
1427:
1425:
1419:
1418:
1416:
1415:
1410:
1405:
1400:
1395:
1390:
1385:
1380:
1375:
1370:
1365:
1360:
1355:
1350:
1345:
1340:
1335:
1330:
1328:Abiotic stress
1325:
1319:
1317:
1313:
1312:
1296:
1295:
1288:
1281:
1273:
1251:
1244:
1243:
1242:
1241:
1240:
1234:
1228:
1225:Lifemapper 2.0
1222:
1216:
1210:
1204:
1196:
1195:External links
1193:
1192:
1191:
1158:
1129:
1127:on 2019-02-26.
1092:
1089:
1086:
1085:
1078:
1060:
1036:
1007:(2): 237–245.
991:
958:
943:
928:
909:(5): 1301–13.
893:
866:(2): 127–139.
850:
773:
722:
695:(4): 334–350.
676:
649:(1): 677–697.
623:
622:
620:
617:
616:
615:
610:
605:
598:
595:
551:climate change
511:
508:
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395:
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383:
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359:
333:
330:
318:realized niche
301:realized niche
291:
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275:remote sensing
235:
232:
224:realized niche
117:
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31:
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22:
15:
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3:
2:
2875:
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2825:
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2814:
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2809:
2808:Urban ecology
2806:
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2799:
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2698:
2694:
2688:
2685:
2683:
2680:
2678:
2675:
2673:
2670:
2668:
2667:Kleiber's law
2665:
2663:
2660:
2658:
2655:
2653:
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2648:
2645:
2643:
2640:
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2466:
2463:
2461:
2458:
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2446:
2443:
2441:
2440:Foster's rule
2438:
2436:
2433:
2431:
2428:
2426:
2423:
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2418:
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2411:
2408:
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2158:
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2151:
2148:
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2128:
2126:
2123:
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2111:
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2106:
2103:
2101:
2098:
2096:
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2091:
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2065:
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2041:
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2034:
2029:
2027:
2022:
2021:
2018:
2006:
2003:
2001:
1998:
1996:
1993:
1991:
1988:
1986:
1983:
1981:
1978:
1976:
1973:
1971:
1968:
1967:
1965:
1959:
1953:
1950:
1948:
1945:
1943:
1940:
1938:
1935:
1933:
1930:
1928:
1925:
1923:
1920:
1918:
1915:
1913:
1910:
1908:
1905:
1903:
1900:
1898:
1895:
1893:
1890:
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1878:
1875:
1873:
1870:
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1865:
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1797:
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1790:
1787:
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1780:
1777:
1775:
1772:
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1769:
1765:
1759:
1758:Trophic level
1756:
1754:
1751:
1749:
1746:
1744:
1741:
1739:
1736:
1734:
1731:
1730:
1728:
1726:
1722:
1716:
1715:Phage ecology
1713:
1711:
1708:
1706:
1705:Microbial mat
1703:
1701:
1698:
1696:
1693:
1691:
1688:
1686:
1683:
1681:
1678:
1676:
1673:
1671:
1668:
1666:
1663:
1661:
1660:Bacteriophage
1658:
1656:
1653:
1652:
1650:
1648:
1644:
1638:
1635:
1633:
1630:
1628:
1627:Decomposition
1625:
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1619:
1617:
1615:
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1602:
1600:
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1577:
1575:
1574:Mesopredators
1572:
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1560:
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1550:
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1532:
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1527:
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1522:
1520:
1519:Apex predator
1517:
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1502:
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1379:
1376:
1374:
1371:
1369:
1366:
1364:
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1359:
1356:
1354:
1353:Biotic stress
1351:
1349:
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1111:
1107:
1100:
1095:
1094:
1081:
1079:9783319927978
1075:
1071:
1064:
1050:
1049:ECHOcommunity
1046:
1045:"FAO Ecocrop"
1040:
1032:
1028:
1023:
1018:
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995:
986:
981:
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546:
545:
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536:
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530:
528:
524:
522:
518:
516:
507:
505:
501:
498:Furthermore,
493:
490:
487:
484:
481:
480:Random forest
478:
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394:
391:
389:
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385:
379:
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369:
358:
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351:
347:
345:
340:
339:micro-climate
329:
325:
323:
319:
314:
310:
306:
302:
296:
282:
280:
276:
272:
267:
264:
260:
256:
252:
251:Andrew Murray
248:
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240:
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225:
221:
217:
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201:
197:
195:
190:
186:
182:
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175:
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158:
154:
150:
149:range mapping
146:
142:
138:
135:
134:environmental
131:
123:
113:
110:
102:
99:December 2018
91:
88:
84:
81:
77:
74:
70:
67:
63:
60: –
59:
55:
54:Find sources:
48:
44:
38:
37:
32:This article
30:
26:
21:
20:
2843:Biogeography
2793:Regime shift
2778:Macroecology
2544:
2499:
2495:
2435:Edge effects
2405:Biogeography
2350:Commensalism
2198:Biodiversity
2075:Allee effect
1814:kelp forests
1767:Example webs
1632:Detritivores
1471:Organotrophs
1451:Kinetotrophs
1403:Productivity
1257:
1219:openModeller
1170:
1166:
1137:
1133:
1122:the original
1109:
1105:
1069:
1063:
1052:. Retrieved
1048:
1039:
1004:
1000:
994:
978:(24): 8900.
975:
971:
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931:
906:
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863:
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853:
786:
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735:
725:
692:
688:
646:
642:
603:Biogeography
580:adapt.nd.edu
577:
573:openModeller
570:
555:
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153:distribution
148:
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136:
133:
129:
128:
105:
96:
86:
79:
72:
65:
53:
41:Please help
36:verification
33:
2430:Disturbance
2333:interaction
2155:Recruitment
2085:Depensation
1877:Copiotrophs
1748:Energy flow
1670:Lithotrophy
1614:Decomposers
1594:Planktivore
1569:Insectivore
1559:Heterotroph
1524:Bacterivore
1491:Phototrophs
1441:Chemotrophs
1413:Restoration
1363:Competition
1140:: 677–697.
1022:10174/20244
2837:Categories
2798:Sexecology
2375:Parasitism
2340:Antibiosis
2175:Resistance
2170:Resilience
2060:Population
1980:Camouflage
1932:Oligotroph
1847:Ascendency
1809:intertidal
1799:cold seeps
1753:Food chain
1554:Herbivores
1529:Carnivores
1456:Mixotrophs
1431:Autotrophs
1310:components
1260:(Q5381340)
1054:2019-08-19
769:Q117084350
619:References
157:geographic
69:newspapers
2703:Allometry
2657:Emergence
2385:Symbiosis
2370:Mutualism
2165:Stability
2070:Abundance
1882:Dominance
1840:Processes
1829:tide pool
1725:Food webs
1599:Predation
1584:Omnivores
1511:Consumers
1466:Mycotroph
1423:Producers
1368:Ecosystem
1333:Behaviour
1112:: 54–89.
880:1573-5052
860:Vegetatio
828:Q51421963
820:16272962M
761:24353101M
709:1461-0248
663:1543-592X
575:project.
562:'biomod2'
279:GIS-based
216:dispersal
170:evolution
2758:Endolith
2687:Xerosere
2599:networks
2415:Ecocline
1961:Defense,
1637:Detritus
1539:Foraging
1408:Resource
1231:AquaMaps
1187:45203952
1154:86460963
1118:40254248
1031:34887643
923:19537550
888:25941018
835:citation
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804:04035567
784:(1866),
765:Wikidata
753:12120623
733:(1908),
717:19292794
671:86460963
597:See also
583:Archived
533:DIVA-GIS
2748:Ecopath
2555:Habitat
2425:Ecotype
2420:Ecotone
2397:ecology
2395:Spatial
2331:Species
2191:Species
2062:ecology
2047:Ecology
1995:Mimicry
1963:counter
1907:f-ratio
1655:Archaea
1343:Biomass
1316:General
1308:Trophic
1300:Ecology
1253:Scholia
903:Ecology
812:8680065
558:'dismo'
492:XGBoost
439:Maxlike
388:BIOCLIM
368:BIOCLIM
234:History
166:ecology
83:scholar
1779:Rivers
1675:Marine
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566:'mopa'
527:ModEco
521:MaxEnt
515:SPACES
470:(GARP)
457:MAXENT
435:(MARS)
400:(ENFA)
393:DOMAIN
196:models
147:, and
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2696:Other
2597:Other
2550:Guild
2522:Niche
1774:Lakes
1183:S2CID
1150:S2CID
1125:(PDF)
1114:S2CID
1102:(PDF)
1027:S2CID
884:S2CID
667:S2CID
494:(XGB)
488:(SVM)
464:(ANN)
429:(GAM)
423:(GLM)
191:, or
90:JSTOR
76:books
1784:Soil
1074:ISBN
919:PMID
876:ISSN
845:link
841:link
808:OCLC
800:LCCN
749:OCLC
713:PMID
705:ISSN
659:ISSN
564:and
544:here
482:(RF)
446:(FF)
168:and
62:news
1175:doi
1142:doi
1017:hdl
1009:doi
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