Occupancy–abundance relationship

286:) interact with the habitat quality of an occupied patch to determine local density, and in multiple patches, can result in an O–A relationship. Holt et al. modelled a system where dispersal between habitat patches could ensure that all suitable habitat patches were occupied, but where dispersal was sufficiently limited so that immigration did not significantly affect the population size in occupied patches. In this system the population size within any given habitat patch was a function only of birth and death rates. By causing habitat quality to vary (increasing or decreasing birth and death rates) Holt was able to generate a positive intraspecific O–A relationship. Holt et al.'s model requires many data to test even for intraspecific relationships (i.e. vital rates of all populations through time). Freckleton et al. use a version of the model proposed by Holt et al., but with varying habitat quality between patches to evaluate parameters that could be observed in species O–A data. Freckleton et al. show that aggregation of individuals within sites, and the skewness of population size should correlate with density and occupancy, depending on specific arrangements of habitat quality, and demonstrate that these parameters vary in accordance with positive intra- and interspecific O–A relationships for common farmland birds in Britain. 267:

populations can 'rescue' populations near or at extinction in other patches). Freckleton et al. have shown that, with a few assumptions (habitat patches of equal suitability, density-independent extinction, and restricted dispersal between patches), varying overall habitat suitability in a metapopulation can generate a positive intraspecific O-A relationship. However, there is currently debate regarding how many populations actually fit a classical metapopulation model. In experimental systems using moss-dwelling microarthropods showed that the fragmentation of habitat caused declines in abundance and occupancy. The addition of habitat corridors arrested these declines, providing evidence that metapopulation dynamics (extinction and immigration) maintain the interspecific O-A relationship, however, Warren and Gaston were able to detect a positive interspecific O–A relationship even in the absence of dispersal, indicating that a more general set of extinction and colonization processes (than metapopulation processes per se) may maintain the O–A relationship.

139:, and for a definition, see Fig. 2 and ALA). The EOO can best be thought of as the minimum convex polygon encompassing all known normal occurrences of a particular species and is the measure of range most commonly found in field guides. The AOO is the subset of the EOO where the species actually occurs. In essence, the AOO acknowledges that there are holes in the distribution of a species within its EOO, and attempts to correct for these vacancies. A common way to describe the AOO of a species is to divide the study region into a matrix of cells and record if the species is present in or absent from each cell. For example, in describing O–A relationships for common British birds, Quinn et al. found that the occupancy at the finest resolution (10 x 10 km squares) best explained abundance patterns. In a similar manner, Zuckerberg et al. used Breeding Bird Atlas data measured on cells 5 × 5 km to describe breeding bird occupancy in New York State. 250:), thus increasing the size of the species geographic range. An initial argument against this hypothesis is that when a species colonizes formerly empty habitats, the average abundance of that species across all occupied habitats drops, negating an O–A relationship. However, all species will occur at low densities in some occupied habitats, while only the abundant species will be able to reach high densities in some of their occupied habitats. Thus it is expected that both common and uncommon species will have similar minimum densities in occupied habitats, but that it is the maximum densities obtained by common species in some habitats that drive the positive relationship between mean densities and AOO. If density-dependent habitat selection were to determine positive O–A relationships, the distribution of a species would follow an 340:), and has led to debate over whether the EOO or AOO measure of species range is more appropriate (Gaston and Fuller 2009). For example, Zuckerberg et al. (2009) have demonstrated that for breeding birds in New York, most species that underwent changes in abundance (positive or negative) between 1985 and 2005 showed concurrent changes in range size. Using a dipswitch test with 15 criteria, Hui et al. (2009) examined the ability of eight models of this kind to estimate the abundance of 610 southern African bird species. Models based on the scaling pattern of occupancy (i.e., those that reflect the scale dependence of species range size) produced the most reliable abundance estimates, and therefore are recommended for assemblage-scale regional abundance estimation. 62:, and applies both intra- and interspecifically (within and among species). 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 (zero abundance, zero occupancy) and could reach some theoretical maximum abundance and distribution (that is, occupancy and abundance can be expected to co-vary), 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 (e.g. Gaston 1996 and references therein), and has important implications for the conservation of 114:

realized range is the portion of the potential range that the species currently occupies. The realized range can be further subdivided, for example, into the breeding and non-reproductive ranges. Explicit consideration of a particular portion of the realized range in analysis of range size can significantly influence the results. For example, many seabirds forage over vast areas of ocean, but breed only on small islands, thus the breeding range is significantly smaller than the non-reproductive range. However, in many terrestrial bird species, the pattern is reversed, with the winter (non-reproductive) range somewhat smaller than the breeding range.

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true, then it can be expected that as a species expands or contracts its range within the region of interest, it will more or less closely resemble populations at the core of its range, leading to a positive intraspecific O–A relationship. In the same manner, an assemblage of species within the study region can be expected to contain some species near the core and some near the periphery of their ranges, leading to a positive interspecific O–A relationship. Although this explanation may contribute to the understanding of O–A relationships where partial ranges are considered, it cannot explain relationships documented for entire geographic ranges.

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inclusion of discontinuities within the EOO could significantly influence results. In the extreme case where occupied habitats are distributed at random throughout the EOO, a relationship between abundance and range size (EOO) would not be expected. Because O–A relationships have strong conservation implications, Gaston and Fuller have argued that clear distinctions need to be made as to the purpose of the EOO and AOO as measures of range size, and that in association with O-A relationships the AOO is the more useful measure of species abundance.

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positive occupancy–abundance relationship. This does not necessarily imply niche differences among species are not important; being able to accurately model real life patterns does not mean that the model assumptions also reflect the actual mechanisms underlying these real-life patterns. In fact, occupancy–abundance relationship are generated across many species, without taking into account the identity of a species. Therefore, it may not be too surprising that neutral models can accurately describe these community properties.

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species occupying fewer sites than common ones, even if the underlying occupancy distribution is the same. However, this explanation makes only one prediction, that is, that with sufficient sampling, no relationship will be found to exist. This prediction is readily falsified, given that exceptionally well studied taxa such as breeding birds (e.g. Zuckerberg et al. 2009, Gaston) show well documented O-A relationships.

196:. This explanation suggests that due to the underlying distribution of aggregation and density, and observed O–A relationship would be expected. However, Gaston et al. question whether this is a suitably mechanistic explanation. Indeed, Gaston et al. suggest that "to argue that spatial aggregation explains abundance-occupancy relationships is simply to supplant one poorly understood pattern with another". 375:

leading to global extinction. This may be confounded by the difficulty in surveying locally rare species due to both their low detectability and restricted distribution (see above). Finally, because rare species are expected to have restricted distributions, conservation programmes aimed at prioritizing sites for multi-species conservation will include fewer habitats for rare species than common species.

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difficult to detect. Gaston et al. note that because of this relationship, the intensiveness of a sampling scheme cannot be traded off for extensiveness. In effect, an intensive survey of a few sites will miss species with restricted distribution occurring at other sites, while a low-intensity extensive survey will miss species with low densities across most sites.

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the mean environmental conditions where a species occurs and mean environmental conditions across a region) could influence its local abundance and range size, if species with lower niche position are more able to use resources typical of a region. Although intuitive, Gaston et al. and Gaston and Blackburn note that, due to the

347:, the proportion of the total population of a species expected to be captured at a given effort is expected to increase as range size decreases. Given a positive intraspecific O–A relationship, it would be expected that with decreases in abundance there would be a decrease in range size, further increasing the potential for 357:– The existence of positive intraspecific O–A relationships would exacerbate the risks faced by imperilled species. Not only would reductions in range size and number of sites occupied directly increase the threat of extinction, but extinction risk would be further increased by the concurrent decline in abundance. 254:(IFD). Gaston et al. cites Tyler and Hargrove who examined the IFD using simulation models and found several instances (e.g. when resources had a fractal distribution, or when the scale of resource distribution poorly matched the organisms dispersal capabilities) where IFDs poorly described species distributions. 694:

Hanski, I., J. Kouki, and A. Halkka. 1993. Three explanations of the positive relationship between distribution and abundance of species. In R.E. Ricklefs and D. Schulter (eds) Species Diversity in Ecological Communities: Historical and Geographical Perspectives. University of Chicago Press, Chicago,

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would, as a consequence, be able to obtain higher local densities, and a wider distribution than species with a narrow niche breadth. This relationship would generate a positive O-A relationship. In a similar manner, a species' niche position, (niche position represents the absolute distance between

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Neutral dynamics assume species and habitats are equivalent and patterns in species abundance and distribution arise from stochastic occurrences of birth, death, immigration, extinction and speciation. Modelling this type of dynamics can simulate many of the patterns in species abundance including a

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Most of the different explanations that have been forwarded to explain the regularities in species abundance and geographic distribution mentioned above similarly predict a positive distribution–abundance relationship. This makes it difficult to test the validity of each explanation. A key challenge

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Holt et al.'s model under different Hcrit values. Figure 2 a. shows the effect of increasing the critical threshold for occupancy on population size and AOO. Figure 2b. shows the effect of decreasing Hcrit. Because the AOO and total abundance covary, an intraspecific occupancy abundance relationship

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One way to deal with observed O–A relationships is, in essence, to deny their existence. An argument against the existence of O–A relationships is that they are merely sampling artefacts. Given that rare species are less likely to be sampled, at a given sampling effort, one can expect to detect rare

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In the discussion of relationships with range size, it is important to define which range is under investigation. Gaston (following Udvardy) describes the potential range of a species as the theoretical maximum range that a species could occupy should all barriers to dispersal be removed, while the

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Interspecific occupancy–abundance relationship – means the relationship between relative abundance and range size of an assemblage of closely related species at a specific point in time (or averaged across a short time period). The interspecific O-A relationship may arise from the combination of the

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Most evaluations of O–A relationships do not evaluate species over their entire (global) range, but document abundance and occupancy patterns within a specific region. It is believed that species decline in abundance and become more patchily distributed towards the margin of their range. If this is

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inventory – An interspecific O–A relationship implies that those species that have a restricted distribution (and hence will be important for conservation reasons) will also have low abundance within their range. Thus, when it is especially important that a species be detected, that species may be

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Indexing abundance – Documenting the abundance of a species is a resource-intensive, and time-consuming process. However, if the abundance of a species can be estimated from its AOO, then assessments of population size can be made more rapidly. This assumption underlies the use of range sizes when

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Neutral dynamics may be relatively important in some cases, depending on the species, environmental conditions and the spatial and temporal scale level under consideration, whereas in other circumstances, niche dynamics may dominate. Thus niche and neutral dynamics may be operating simultaneously,

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non-independence hypothesis is a third statistical explanation, specific to observed interspecific O–A relationships. This hypothesis suggests that, as closely related species are not truly independent their inclusion into analyses artificially inflates the degrees of freedom available for testing

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model, habitat occurs in discrete patches, with a population in any one patch facing a substantial risk of extinction at any given time. Because population dynamics in individual patches are asynchronous, the system is maintained by dispersal between patches (e.g. dispersal from patches with high

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No matter which concept we use in studies, it is essential to realize that occupancy is only a reflection of species distribution under a certain spatial scale. Occupancy, as well as other measures of species distributions (e.g. over-dispersion and spatial autocorrelation), is scale-dependent. As

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Conservation – As with the intraspecific relationship, the interspecific O–A relationship implies that species will not only be at risk of extinction due to low abundance, but because species with low abundance are expected to have restricted distributions, they are at risk of local catastrophe

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in nature, the variables of interest can be expected to vary most from one extent of occurrence to the opposite, and less so through discontinuities contained within the total EOO. However, when investigating O-A relationships, the area occupied by a species is the variable of interest, and the

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Niche dynamics assume differences among species in their fundamental niche which should give rise to patterns in the abundance and distribution of species (i.e. their realized niches). In this framework, the abundance and distribution of a single species and hence the emergent patterns across

381:– In essence, the logic relating positive O–A relationships to invasion biology is the same as that relating O–A relationships to conservation concerns. Specifically, as an invading species increases in local abundance, its range can be expected to expand, further confounding control efforts. 313:

By incorporating specific information on a species' diet, reproduction, dispersal and habitat specialisation Verberk et al. could successfully explain the contribution of individual species to the overall relationship and they showed that the main mechanisms in operation may be different for

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is therefore to distinguish between the various mechanisms that have been proposed to underlie these near universal patterns. The effect of either niche dynamics or neutral dynamics represent two opposite views and many explanations take up intermediate positions.

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such, studies on the comparison of O–A relationships should be aware of the issue of scale sensitivity (compare text of Fig 1 & Fig.2). Furthermore, measuring species range, whether it is measured by the convex hull or occupancy (occurrence), is part of the

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multiple species, are driven by causal mechanisms operating at the level of that species. Therefore, examining how differences between individual species shape these patterns, rather than analyzing the pattern itself, may help to understand these patterns.

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A suite of possible explanations have been proposed to describe why positive intra- and interspecific O–A relationships are observed. Following Gaston et al. 1997 Gaston and Blackburn 2000 Gaston et al. 2000, and Gaston 2003 these reasons include:

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Webb T.J., Noble D. & Freckleton R.P. (2007). Abundance-occupancy dynamics in a human dominated environment: linking interspecific and intraspecific trends in British farmland and woodland birds. Journal of Animal Ecology, 76,

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Verberk, W.C.E.P., G. van der Velde and H. Esselink. 2010. Explaining abundance-occupancy relationships in specialists and generalists: a case study on aquatic macroinvertebrates in standing waters. Journal of Animal Ecology 79:

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Hui, C., McGeoch, M.A., Reyers, B., le Roux, P.C., Greve, M. & Chown, S.L. (2009) Extrapolating population size from the occupancy-abundance relationship and the scaling pattern of occupancy. Ecological Applications, 19,

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Area of Occupancy (AOO) plot using a 30 km x 30 km grid. There are 81 occupied cells, giving an AOO of 81 x 900 (72900) square kilometres (and illustrating the dependence of AOO on scale or grid

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Gonzalez, A., J.H. Lawton, F.S. Gilbert, T.M. Blackburn, and I. Evans-Freke. 1998. Metapopulation dynamics maintain the positive species abundance-distribution relationship. Science 281: 2045–2047.

150:, the use of EOO as a measure of range size may be appropriate; however, AOO is a more appropriate measure when evaluating O–A relationships. In macroecological investigations that are primarily 756:

Freckleton, R.P., D. Noble, J.A. Gill, and A.R. Watkinson. 2005. Abundance-occupancy relationships and the scaling from local to regional population size. Journal of Animal Ecology 74: 353–364.

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Zuckerberg, B., W.F. Porter, and K. Corwin. 2009. The consistency and stability of abundance-occupancy relationships in large-scale population dynamics. Journal of Animal Ecology 78: 172–181.

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selection. For species exhibiting this pattern, dispersal into what would otherwise be sub-optimal habitats can occur when local abundances are high in high quality habitats (see

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Tyler, J. A. and W. W. Hargrove. 1997. "Predicting spatial distribution of foragers over large resource landscapes: a modeling analysis of the ideal free distribution".

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Gaston, K.J., T.M. Blackburn, and J.H. Lawton. 1997. Interspecific abundance–range size relationships: an appraisal of mechanisms. Journal of Animal Ecology 66: 579–601.

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Fisher J.A.D. & Frank K.T. (2004). Abundance-distribution relationships and conservation of exploited marine fishes. Marine Ecology Progress Series, 279, 201–213.

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Freckleton, R.P., D. Noble, and T.J. Webb. 2006. Distributions of habitat suitability and the abundance-occupancy relationship. The American Naturalist 167: 260–275.

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Warren, P.H., and K.J. Gaston. 1992. Interspecific abundance-occupancy relationships: a test of mechanisms using microcosms. Journal of Animal Ecology 66: 730–742.

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the relationship. However Gaston et al. cite several studies documenting significant O–A relationships in spite of controlling for phylogenetic non-independence.

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Intraspecific occupancy–abundance relationship – means the relationship between abundance and range size within a single species generated using time series data

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Holt, R.D., J.H. Lawton, K.J. Gaston, and T.M Blackburn. 1997. On the relationship between range size and local abundance: back to basics. Oikos 78: 183–190.

83:– means the average density of the species of interest across all occupied patches (i.e. average abundance does not include the area of unoccupied patches) 117:

The definition of range is further confounded by how the total realized range size is measured. There are two types of measurements commonly in use, the

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Hui, C., Veldtman, R. & McGeoch, M.A. 2010. Measures, perceptions and scaling patterns of aggregated species distributions. Ecography 33: 95–102.

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Quinn, R.M., K.J. Gaston, and H.R. Arnold. 1996. Relative measures of geographic range size: empirical comparisons. Oecologia 107: 179–188.

1806: 819:"Influence of habitat suitability and sex-related detectability on density and population size estimates of habitat-specialist warblers" 77:– means the total area occupied by the species of interest in the region under study (see below 'Measures of species geographic range') 1876: 1448: 1413: 648:

Hui, C. & McGeoch, M.A. (2007) Capturing the "droopy-tail" in the occupancy-abundance relationship. Ecoscience, 14, 103–108.

1886: 1614: 676:<Gaston, K.J., and T.M. Blackburn. 2000. Pattern and Process in Macroecology. Blackwell Science Ltd. United Kingdom. 377 pp. 17: 685:

Brown, J.H. 1984. On the relationship between abundance and distribution of species. The American Naturalist 122: 295–299.

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Udvardy, M.D.F. 1969. Dynamic Zoogeography: with special reference to land animals. Van Nostrand Reinhold, New York.

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Gaston, K.J. 2003. The Structure and Dynamics of Geographic Ranges. Oxford University Press. Oxford, UK. 266 pp.

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Gaston, K.J. and R.A. Fuller. 2009. The sizes of species geographic ranges. Journal of Applied Ecology 46: 1–9.

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Important implications of both the intra- and interspecific O–A relationships are discussed by Gaston et al.

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is expected under situations where habitat quality varies through time (more or less area above Hcrit.

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Gaston, K.J. (1996). "The multiple forms of the interspecific abundance-distribution relationship".

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Rosenzweig, M. L. 1991. "Habitat selection and population interactions: the search for mechanism".

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Gaston, K.J.; Blackburn, T.M.; Greenwood, J.J.D.; Gregory, R.D.; Quinn, R.M.; Lawton, J.H (2000).

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within a region. This relationship is perhaps one of the most well-documented relationships in

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Freckleton, R.P. 2003. Are all plant populations metapopulations? Journal of Ecology 91: 321

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A second statistical explanation involves the use of statistical distributions such as the

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Frias, O.; Bautista, L. M.; Dénes, F. V.; Cuevas, J. A.; Martínez, F.; Blanco, G. (2018).

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Amarasekare, P. 2004. "The role of density-dependant dispersal in source-sink dynamics".

283: 80: 47: 837: 2448: 2397: 2392: 2201: 2164: 1906: 1862: 1827: 1684: 1609: 1511: 1443: 1433: 1367: 1314: 1125: 1070: 1032: 957: 856: 519: 463: 455: 164: 63: 2337: 2306: 2094: 1921: 1729: 1594: 1571: 1309: 1085: 997: 982: 967: 947: 861: 514: 497: 467: 2291: 2154: 2146: 2064: 1946: 1931: 1867: 1847: 1764: 1754: 1749: 1714: 1546: 1486: 1357: 1158: 1100: 1012: 972: 851: 841: 509: 447: 378: 222: 2427: 2286: 2256: 2251: 2241: 2174: 2159: 2039: 2019: 1901: 1769: 1675: 1566: 1476: 1418: 1002: 928: 846: 74: 55: 2407: 2231: 2184: 2114: 2109: 2004: 1871: 1744: 1551: 1541: 1521: 1324: 1289: 1228: 1105: 1060: 952: 348: 263: 2461: 2432: 1408: 1382: 1339: 1329: 1284: 1251: 1143: 977: 932: 337: 230:-dimensional nature of the niche, this hypothesis is, in effect, untestable. 200: 98: 704:

Van Horne, B. 1983. "Density as a misleading indicator of habitat quality".

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typically uses a cell size of 2 × 2 km in calculating AOO.

1709: 1256: 1218: 1193: 1183: 1148: 1095: 1075: 160: 2422: 1999: 1964: 1604: 1556: 1501: 1471: 1377: 1294: 1238: 1115: 1065: 523: 459: 2327: 2281: 2009: 1453: 1423: 1223: 1178: 1153: 1090: 1080: 1055: 1047: 992: 923: 451: 2382: 2311: 1842: 1670: 1349: 1261: 1208: 1163: 361: 329: 296: 27:

Ecological relationship between species' range sizes and abundance

2372: 2179: 2049: 2044: 1671: 1619: 1279: 924: 243: 51: 31: 495: 672: 670: 140: 66:, the mechanism(s) underlying it remain poorly understood. 667: 816: 318:

constituting different endpoints of the same continuum.

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deciding on the conservation status of a species (see

233: 882: 750: 660: 658: 656: 654: 612: 603: 768: 800: 793: 791: 789: 491: 489: 487: 485: 483: 481: 479: 477: 343:Setting harvest rates – Especially in the case of 90:intraspecific O–A relationships within the region 688: 651: 580:"ALA: Area of Occupancy and Extent of Occurrence" 2459: 724: 362:Importance of the interspecific O–A relationship 330:Importance of the intraspecific O–A relationship 786: 759: 711: 594: 474: 297:Explaining the occupancy–abundance relationship 698: 530: 129:) (For definition: see ALA and Fig.1) and the 1656: 909: 551: 549: 547: 545: 543: 433: 431: 278:The vital rates of a species (in particular 216: 558: 179: 1877:Latitudinal gradients in species diversity 1663: 1649: 916: 902: 679: 540: 428: 257: 221:Brown suggested that species with a broad 855: 845: 810: 513: 1775:Predator–prey (Lotka–Volterra) equations 1414:Tritrophic interactions in plant defense 170: 97: 1807:Random generalized Lotka–Volterra model 238:Many species exhibit density-dependent 14: 2460: 1615:Herbivore adaptations to plant defense 437: 282:– the intrinsic rate of increase; see 1644: 897: 574: 572: 570: 94:Measures of species' geographic range 1630:Predator avoidance in schooling fish 163:process and can be explained by the 2080:Intermediate disturbance hypothesis 498:"Abundance-occupancy relationships" 234:Density-dependent habitat selection 24: 1833:Ecological effects of biodiversity 567: 69: 25: 2479: 1169:Generalist and specialist species 207: 1892:Occupancy–abundance relationship 515:10.1046/j.1365-2664.2000.00485.x 46:is the relationship between the 1912:Relative abundance distribution 1625:Plant defense against herbivory 1492:Competitive exclusion principle 1204:Mesopredator release hypothesis 872: 642: 633: 321: 1497:Consumer–resource interactions 732:Journal of Theoretical Biology 706:Journal of Wildlife Management 273: 13: 1: 2343:Biological data visualization 2170:Environmental niche modelling 1897:Population viability analysis 422: 1828:Density-dependent inhibition 847:10.1371/journal.pone.0201482 137:Scaling pattern of occupancy 7: 2297:Liebig's law of the minimum 2132:Resource selection function 1023:Metabolic theory of ecology 582:. Atlas of Living Australia 407:Metabolic theory of ecology 385: 10: 2484: 2197:Niche apportionment models 1917:Relative species abundance 1121:Primary nutritional groups 1018:List of feeding behaviours 502:Journal of Applied Ecology 417:Relative species abundance 412:Niche apportionment models 392:Body size-species richness 314:different species groups. 2446: 2378:Ecosystem based fisheries 2320: 2220: 2145: 2018: 1990:Interspecific competition 1955: 1882:Minimum viable population 1815: 1740:Maximum sustainable yield 1725:Intraspecific competition 1720:Effective population size 1683: 1600:Anti-predator adaptations 1585: 1464: 1391: 1348: 1270: 1237: 1134: 1111:Photosynthetic efficiency 1046: 940: 217:Resource use explanations 2368:Ecological stoichiometry 2333:Alternative stable state 180:Statistical explanations 2212:Ontogenetic niche shift 2075:Ideal free distribution 1985:Ecological facilitation 1735:Malthusian growth model 1705:Consumer-resource model 1562:Paradox of the plankton 1527:Energy systems language 1247:Chemoorganoheterotrophy 1214:Optimal foraging theory 1189:Heterotrophic nutrition 719:The American Naturalist 402:Ideal free distribution 258:Metapopulation dynamics 252:Ideal Free Distribution 132:area of occupancy (AOO) 2358:Ecological forecasting 2302:Marginal value theorem 2100:Landscape epidemiology 2035:Cross-boundary subsidy 1970:Biological interaction 1320:Microbial intelligence 1008:Green world hypothesis 110: 54:and the size of their 2363:Ecological humanities 2262:Ecological energetics 2207:Niche differentiation 2070:Habitat fragmentation 1838:Ecological extinction 1785:Small population size 1537:Feed conversion ratio 1517:Ecological succession 1449:San Francisco Estuary 1363:Ecological efficiency 1305:Microbial cooperation 171:Possible explanations 101: 2388:Evolutionary ecology 2353:Ecological footprint 2348:Ecological economics 2272:Ecological threshold 2267:Ecological indicator 2137:Source–sink dynamics 2090:Land change modeling 2085:Insular biogeography 1937:Species distribution 1676:Modelling ecosystems 1335:Microbial metabolism 1174:Intraguild predation 963:Biogeochemical cycle 929:Modelling ecosystems 397:Conservation biology 355:Conservation biology 345:commercial fisheries 248:Source–sink dynamics 120:extent of occurrence 105:Lawrencia densiflora 18:Extent of occurrence 2438:Theoretical ecology 2413:Natural environment 2277:Ecosystem diversity 2247:Ecological collapse 2237:Bateman's principle 2192:Limiting similarity 2105:Landscape limnology 1927:Species homogeneity 1765:Population modeling 1760:Population dynamics 1577:Trophic state index 838:2018PLoSO..1301482F 284:Population dynamics 36:occupancy–abundance 2449:Outline of ecology 2398:Industrial ecology 2393:Functional ecology 2257:Ecological deficit 2202:Niche construction 2165:Ecosystem engineer 1942:Species–area curve 1863:Introduced species 1678:: Other components 1610:Deimatic behaviour 1512:Ecological network 1444:North Pacific Gyre 1429:hydrothermal vents 1368:Ecological pyramid 1315:Microbial food web 1126:Primary production 1071:Foundation species 165:percolation theory 111: 64:endangered species 2455: 2454: 2338:Balance of nature 2095:Landscape ecology 1980:Community ecology 1922:Species diversity 1858:Indicator species 1853:Gradient analysis 1730:Logistic function 1638: 1637: 1595:Animal coloration 1572:Trophic mutualism 1310:Microbial ecology 1101:Photoheterotrophs 1086:Myco-heterotrophy 998:Ecosystem ecology 983:Carrying capacity 948:Abiotic component 194:negative-binomial 16:(Redirected from 2475: 2155:Ecological niche 2127:selection theory 1947:Umbrella species 1932:Species richness 1868:Invasive species 1848:Flagship species 1755:Population cycle 1750:Overexploitation 1715:Ecological yield 1665: 1658: 1651: 1642: 1641: 1547:Mesotrophic soil 1487:Climax community 1419:Marine food webs 1358:Biomagnification 1159:Chemoorganotroph 1013:Keystone species 973:Biotic component 918: 911: 904: 895: 894: 889: 886: 880: 876: 870: 869: 859: 849: 823: 814: 808: 804: 798: 795: 784: 781: 775: 772: 766: 763: 757: 754: 748: 741: 735: 728: 722: 715: 709: 702: 696: 692: 686: 683: 677: 674: 665: 662: 649: 646: 640: 637: 631: 628: 622: 619: 610: 607: 601: 598: 592: 591: 589: 587: 576: 565: 562: 556: 553: 538: 534: 528: 527: 517: 493: 472: 471: 435: 379:Invasive species 223:ecological niche 21: 2483: 2482: 2478: 2477: 2476: 2474: 2473: 2472: 2458: 2457: 2456: 2451: 2442: 2428:Systems ecology 2316: 2287:Extinction debt 2252:Ecological debt 2242:Bioluminescence 2223: 2216: 2185:marine habitats 2160:Ecological trap 2141: 2021: 2014: 1957: 1951: 1907:Rapoport's rule 1902:Priority effect 1843:Endemic species 1811: 1770:Population size 1686: 1679: 1669: 1639: 1634: 1587: 1581: 1567:Trophic cascade 1477:Bioaccumulation 1460: 1387: 1344: 1266: 1233: 1130: 1042: 1003:Ecosystem model 936: 922: 892: 887: 883: 877: 873: 821: 815: 811: 805: 801: 796: 787: 782: 778: 773: 769: 764: 760: 755: 751: 742: 738: 729: 725: 716: 712: 703: 699: 693: 689: 684: 680: 675: 668: 663: 652: 647: 643: 638: 634: 629: 625: 620: 613: 608: 604: 599: 595: 585: 583: 578: 577: 568: 563: 559: 554: 541: 535: 531: 494: 475: 452:10.2307/3546192 436: 429: 425: 388: 364: 332: 324: 299: 276: 270: 262:In a classical 260: 236: 219: 210: 182: 173: 152:biogeographical 96: 72: 70:Important terms 28: 23: 22: 15: 12: 11: 5: 2481: 2471: 2470: 2453: 2452: 2447: 2444: 2443: 2441: 2440: 2435: 2430: 2425: 2420: 2415: 2410: 2408:Microecosystem 2405: 2400: 2395: 2390: 2385: 2380: 2375: 2370: 2365: 2360: 2355: 2350: 2345: 2340: 2335: 2330: 2324: 2322: 2318: 2317: 2315: 2314: 2309: 2307:Thorson's rule 2304: 2299: 2294: 2289: 2284: 2279: 2274: 2269: 2264: 2259: 2254: 2249: 2244: 2239: 2234: 2232:Assembly rules 2228: 2226: 2218: 2217: 2215: 2214: 2209: 2204: 2199: 2194: 2189: 2188: 2187: 2177: 2172: 2167: 2162: 2157: 2151: 2149: 2143: 2142: 2140: 2139: 2134: 2129: 2117: 2115:Patch dynamics 2112: 2110:Metapopulation 2107: 2102: 2097: 2092: 2087: 2082: 2077: 2072: 2067: 2062: 2057: 2052: 2047: 2042: 2037: 2032: 2026: 2024: 2016: 2015: 2013: 2012: 2007: 2005:Storage effect 2002: 1997: 1992: 1987: 1982: 1977: 1972: 1967: 1961: 1959: 1953: 1952: 1950: 1949: 1944: 1939: 1934: 1929: 1924: 1919: 1914: 1909: 1904: 1899: 1894: 1889: 1887:Neutral theory 1884: 1879: 1874: 1872:Native species 1865: 1860: 1855: 1850: 1845: 1840: 1835: 1830: 1825: 1819: 1817: 1813: 1812: 1810: 1809: 1804: 1803: 1802: 1797: 1787: 1782: 1777: 1772: 1767: 1762: 1757: 1752: 1747: 1745:Overpopulation 1742: 1737: 1732: 1727: 1722: 1717: 1712: 1707: 1702: 1697: 1691: 1689: 1681: 1680: 1668: 1667: 1660: 1653: 1645: 1636: 1635: 1633: 1632: 1627: 1622: 1617: 1612: 1607: 1602: 1597: 1591: 1589: 1583: 1582: 1580: 1579: 1574: 1569: 1564: 1559: 1554: 1552:Nutrient cycle 1549: 1544: 1542:Feeding frenzy 1539: 1534: 1529: 1524: 1522:Energy quality 1519: 1514: 1509: 1504: 1499: 1494: 1489: 1484: 1482:Cascade effect 1479: 1474: 1468: 1466: 1462: 1461: 1459: 1458: 1457: 1456: 1451: 1446: 1441: 1436: 1431: 1426: 1416: 1411: 1406: 1401: 1395: 1393: 1389: 1388: 1386: 1385: 1380: 1375: 1370: 1365: 1360: 1354: 1352: 1346: 1345: 1343: 1342: 1337: 1332: 1327: 1325:Microbial loop 1322: 1317: 1312: 1307: 1302: 1297: 1292: 1290:Lithoautotroph 1287: 1282: 1276: 1274: 1272:Microorganisms 1268: 1267: 1265: 1264: 1259: 1254: 1249: 1243: 1241: 1235: 1234: 1232: 1231: 1229:Prey switching 1226: 1221: 1216: 1211: 1206: 1201: 1196: 1191: 1186: 1181: 1176: 1171: 1166: 1161: 1156: 1151: 1146: 1140: 1138: 1132: 1131: 1129: 1128: 1123: 1118: 1113: 1108: 1106:Photosynthesis 1103: 1098: 1093: 1088: 1083: 1078: 1073: 1068: 1063: 1061:Chemosynthesis 1058: 1052: 1050: 1044: 1043: 1041: 1040: 1035: 1030: 1025: 1020: 1015: 1010: 1005: 1000: 995: 990: 985: 980: 975: 970: 965: 960: 955: 953:Abiotic stress 950: 944: 942: 938: 937: 921: 920: 913: 906: 898: 891: 890: 881: 871: 809: 799: 785: 776: 767: 758: 749: 736: 723: 710: 697: 687: 678: 666: 650: 641: 632: 623: 611: 602: 593: 566: 557: 539: 529: 473: 446:(2): 211–220. 426: 424: 421: 420: 419: 414: 409: 404: 399: 394: 387: 384: 383: 382: 376: 372: 363: 360: 359: 358: 352: 349:overharvesting 341: 331: 328: 323: 320: 298: 295: 275: 272: 264:metapopulation 259: 256: 235: 232: 218: 215: 209: 208:Range position 206: 181: 178: 172: 169: 135:(see also the 95: 92: 71: 68: 26: 9: 6: 4: 3: 2: 2480: 2469: 2466: 2465: 2463: 2450: 2445: 2439: 2436: 2434: 2433:Urban ecology 2431: 2429: 2426: 2424: 2421: 2419: 2416: 2414: 2411: 2409: 2406: 2404: 2401: 2399: 2396: 2394: 2391: 2389: 2386: 2384: 2381: 2379: 2376: 2374: 2371: 2369: 2366: 2364: 2361: 2359: 2356: 2354: 2351: 2349: 2346: 2344: 2341: 2339: 2336: 2334: 2331: 2329: 2326: 2325: 2323: 2319: 2313: 2310: 2308: 2305: 2303: 2300: 2298: 2295: 2293: 2292:Kleiber's law 2290: 2288: 2285: 2283: 2280: 2278: 2275: 2273: 2270: 2268: 2265: 2263: 2260: 2258: 2255: 2253: 2250: 2248: 2245: 2243: 2240: 2238: 2235: 2233: 2230: 2229: 2227: 2225: 2219: 2213: 2210: 2208: 2205: 2203: 2200: 2198: 2195: 2193: 2190: 2186: 2183: 2182: 2181: 2178: 2176: 2173: 2171: 2168: 2166: 2163: 2161: 2158: 2156: 2153: 2152: 2150: 2148: 2144: 2138: 2135: 2133: 2130: 2128: 2126: 2122: 2118: 2116: 2113: 2111: 2108: 2106: 2103: 2101: 2098: 2096: 2093: 2091: 2088: 2086: 2083: 2081: 2078: 2076: 2073: 2071: 2068: 2066: 2065:Foster's rule 2063: 2061: 2058: 2056: 2053: 2051: 2048: 2046: 2043: 2041: 2038: 2036: 2033: 2031: 2028: 2027: 2025: 2023: 2017: 2011: 2008: 2006: 2003: 2001: 1998: 1996: 1993: 1991: 1988: 1986: 1983: 1981: 1978: 1976: 1973: 1971: 1968: 1966: 1963: 1962: 1960: 1954: 1948: 1945: 1943: 1940: 1938: 1935: 1933: 1930: 1928: 1925: 1923: 1920: 1918: 1915: 1913: 1910: 1908: 1905: 1903: 1900: 1898: 1895: 1893: 1890: 1888: 1885: 1883: 1880: 1878: 1875: 1873: 1869: 1866: 1864: 1861: 1859: 1856: 1854: 1851: 1849: 1846: 1844: 1841: 1839: 1836: 1834: 1831: 1829: 1826: 1824: 1821: 1820: 1818: 1814: 1808: 1805: 1801: 1798: 1796: 1793: 1792: 1791: 1788: 1786: 1783: 1781: 1778: 1776: 1773: 1771: 1768: 1766: 1763: 1761: 1758: 1756: 1753: 1751: 1748: 1746: 1743: 1741: 1738: 1736: 1733: 1731: 1728: 1726: 1723: 1721: 1718: 1716: 1713: 1711: 1708: 1706: 1703: 1701: 1698: 1696: 1693: 1692: 1690: 1688: 1682: 1677: 1673: 1666: 1661: 1659: 1654: 1652: 1647: 1646: 1643: 1631: 1628: 1626: 1623: 1621: 1618: 1616: 1613: 1611: 1608: 1606: 1603: 1601: 1598: 1596: 1593: 1592: 1590: 1584: 1578: 1575: 1573: 1570: 1568: 1565: 1563: 1560: 1558: 1555: 1553: 1550: 1548: 1545: 1543: 1540: 1538: 1535: 1533: 1530: 1528: 1525: 1523: 1520: 1518: 1515: 1513: 1510: 1508: 1505: 1503: 1500: 1498: 1495: 1493: 1490: 1488: 1485: 1483: 1480: 1478: 1475: 1473: 1470: 1469: 1467: 1463: 1455: 1452: 1450: 1447: 1445: 1442: 1440: 1437: 1435: 1432: 1430: 1427: 1425: 1422: 1421: 1420: 1417: 1415: 1412: 1410: 1407: 1405: 1402: 1400: 1397: 1396: 1394: 1390: 1384: 1383:Trophic level 1381: 1379: 1376: 1374: 1371: 1369: 1366: 1364: 1361: 1359: 1356: 1355: 1353: 1351: 1347: 1341: 1340:Phage ecology 1338: 1336: 1333: 1331: 1330:Microbial mat 1328: 1326: 1323: 1321: 1318: 1316: 1313: 1311: 1308: 1306: 1303: 1301: 1298: 1296: 1293: 1291: 1288: 1286: 1285:Bacteriophage 1283: 1281: 1278: 1277: 1275: 1273: 1269: 1263: 1260: 1258: 1255: 1253: 1252:Decomposition 1250: 1248: 1245: 1244: 1242: 1240: 1236: 1230: 1227: 1225: 1222: 1220: 1217: 1215: 1212: 1210: 1207: 1205: 1202: 1200: 1199:Mesopredators 1197: 1195: 1192: 1190: 1187: 1185: 1182: 1180: 1177: 1175: 1172: 1170: 1167: 1165: 1162: 1160: 1157: 1155: 1152: 1150: 1147: 1145: 1144:Apex predator 1142: 1141: 1139: 1137: 1133: 1127: 1124: 1122: 1119: 1117: 1114: 1112: 1109: 1107: 1104: 1102: 1099: 1097: 1094: 1092: 1089: 1087: 1084: 1082: 1079: 1077: 1074: 1072: 1069: 1067: 1064: 1062: 1059: 1057: 1054: 1053: 1051: 1049: 1045: 1039: 1036: 1034: 1031: 1029: 1026: 1024: 1021: 1019: 1016: 1014: 1011: 1009: 1006: 1004: 1001: 999: 996: 994: 991: 989: 986: 984: 981: 979: 978:Biotic stress 976: 974: 971: 969: 966: 964: 961: 959: 956: 954: 951: 949: 946: 945: 943: 939: 934: 930: 926: 919: 914: 912: 907: 905: 900: 899: 896: 885: 875: 867: 863: 858: 853: 848: 843: 839: 835: 832:(7): 020148. 831: 827: 820: 813: 803: 794: 792: 790: 780: 771: 762: 753: 746: 740: 734:226: 159–168. 733: 727: 720: 714: 707: 701: 691: 682: 673: 671: 661: 659: 657: 655: 645: 636: 627: 618: 616: 606: 597: 581: 575: 573: 571: 561: 552: 550: 548: 546: 544: 533: 525: 521: 516: 511: 508:(S1): 39–59. 507: 503: 499: 492: 490: 488: 486: 484: 482: 480: 478: 469: 465: 461: 457: 453: 449: 445: 441: 434: 432: 427: 418: 415: 413: 410: 408: 405: 403: 400: 398: 395: 393: 390: 389: 380: 377: 373: 369: 366: 365: 356: 353: 350: 346: 342: 339: 338:IUCN Red List 334: 333: 327: 319: 315: 311: 307: 303: 294: 291: 287: 285: 281: 271: 268: 265: 255: 253: 249: 245: 241: 231: 229: 224: 214: 205: 202: 197: 195: 191: 186: 177: 168: 166: 162: 156: 153: 149: 144: 142: 138: 134: 133: 128: 127: 122: 121: 115: 107: 106: 100: 91: 87: 84: 82: 78: 76: 67: 65: 61: 57: 53: 49: 45: 41: 37: 33: 19: 2468:Biodiversity 2418:Regime shift 2403:Macroecology 2124: 2120: 2060:Edge effects 2030:Biogeography 1975:Commensalism 1891: 1823:Biodiversity 1700:Allee effect 1439:kelp forests 1392:Example webs 1257:Detritivores 1096:Organotrophs 1076:Kinetotrophs 1028:Productivity 884: 874: 829: 825: 812: 802: 779: 770: 761: 752: 747:79: 376–386. 744: 739: 731: 726: 721:137: S5–S28. 718: 713: 705: 700: 690: 681: 644: 635: 626: 605: 596: 584:. Retrieved 560: 532: 505: 501: 443: 439: 368:Biodiversity 325: 322:Implications 316: 312: 308: 304: 300: 289: 288: 279: 277: 269: 261: 237: 227: 220: 211: 201:phylogenetic 198: 187: 183: 174: 157: 148:macroecology 145: 131: 130: 125: 124: 119: 118: 116: 112: 103: 88: 85: 79: 73: 60:macroecology 44:relationship 43: 39: 35: 29: 2055:Disturbance 1958:interaction 1780:Recruitment 1710:Depensation 1502:Copiotrophs 1373:Energy flow 1295:Lithotrophy 1239:Decomposers 1219:Planktivore 1194:Insectivore 1184:Heterotroph 1149:Bacterivore 1116:Phototrophs 1066:Chemotrophs 1038:Restoration 988:Competition 274:Vital rates 161:percolation 146:In much of 2423:Sexecology 2000:Parasitism 1965:Antibiosis 1800:Resistance 1795:Resilience 1685:Population 1605:Camouflage 1557:Oligotroph 1472:Ascendency 1434:intertidal 1424:cold seeps 1378:Food chain 1179:Herbivores 1154:Carnivores 1081:Mixotrophs 1056:Autotrophs 935:components 879:2038–2048. 708:47:893–901 423:References 2328:Allometry 2282:Emergence 2010:Symbiosis 1995:Mutualism 1790:Stability 1695:Abundance 1507:Dominance 1465:Processes 1454:tide pool 1350:Food webs 1224:Predation 1209:Omnivores 1136:Consumers 1091:Mycotroph 1048:Producers 993:Ecosystem 958:Behaviour 586:21 August 290:Figure 2. 240:dispersal 81:Abundance 48:abundance 2462:Category 2383:Endolith 2312:Xerosere 2224:networks 2040:Ecocline 1586:Defense, 1262:Detritus 1164:Foraging 1033:Resource 866:30059562 826:PLOS ONE 807:589–601. 537:123–134. 468:85357216 386:See also 2373:Ecopath 2180:Habitat 2050:Ecotype 2045:Ecotone 2022:ecology 2020:Spatial 1956:Species 1816:Species 1687:ecology 1672:Ecology 1620:Mimicry 1588:counter 1532:f-ratio 1280:Archaea 968:Biomass 941:General 933:Trophic 925:Ecology 857:6066240 834:Bibcode 524:2655767 460:3546192 244:habitat 190:Poisson 102:Fig 2. 52:species 32:ecology 1404:Rivers 1300:Marine 864: 854: 522: 466: 458: 109:size). 56:ranges 34:, the 2321:Other 2222:Other 2175:Guild 2147:Niche 1399:Lakes 822:(PDF) 745:Oikos 520:JSTOR 464:S2CID 456:JSTOR 440:Oikos 75:Range 1409:Soil 862:PMID 695:USA. 588:2018 242:and 199:The 141:IUCN 852:PMC 842:doi 510:doi 448:doi 192:or 126:EOO 50:of 40:O–A 30:In 2464:: 1870:/ 1674:: 931:: 927:: 860:. 850:. 840:. 830:13 828:. 824:. 788:^ 669:^ 653:^ 614:^ 569:^ 542:^ 518:. 506:37 504:. 500:. 476:^ 462:. 454:. 444:75 442:. 430:^ 167:, 42:) 2125:K 2123:/ 2121:r 1664:e 1657:t 1650:v 917:e 910:t 903:v 868:. 844:: 836:: 590:. 526:. 512:: 470:. 450:: 351:. 280:r 228:n 123:( 38:( 20:)

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