In macroecology and community ecology , an occupancy frequency distribution ( OFD ) is the distribution of the numbers of species occupying different numbers of areas. It was first reported in 1918 by the Danish botanist Christen C. Raunkiær in his study on plant communities. The OFD is also known as the species-range size distribution in literature.
52-704: OFD may refer to: Occupancy frequency distribution Oakland Fire Department Orange Fire Department Ogof Ffynnon Ddu , an extensive cave system in South Wales Orofaciodigital syndrome One Fine Day (U.S. TV series) , a 2007–2008 American Internet Protocol television series produced in conjunction with students from a variety of Big Ten Universities Open Fixed Document（ 国家版式文档格式 ），A fixed document format defined by GB/T 33190-2016 aims to replace PDF in Chinese public institutions. Topics referred to by
104-419: A right-skewed unimodal shape , 27% bimodal, and 27% uniform . A recent study reaffirms about 24% bimodal OFDs in among 289 real communities. As pointed out by Gleason, the variety shapes of OFD can be explained, to a large degree, by the size of the sampling interval. For instance, McGeoch and Gaston (2002) show that the number of satellite (rare) species declines with the increase of sampling grains, but
156-442: A given species was proportional to its frequency) may produce bimodal occupancy distributions. This model is not particularly sensitive or informative as to the mechanisms generating bimodality in occupancy frequency distributions, because the mechanisms generating the lognormal species abundance distribution are still under heavy debate. Bimodality may be generated by colonization-extinction metapopulation dynamics associated with
208-447: 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
260-438: A power relationship with spatial scales, and we therefore adopt a power-scaling assumption for modeling species occupancy distributions. The bimodality in occupancy frequency distributions that is common in species communities, is confirmed to a result for certain mathematical and statistical properties of the probability distribution of occupancy. The results thus demonstrate that the use of the bisection method in combination with
312-586: A power-scaling assumption is more appropriate for modeling species distributions than the use of a self-similarity assumption, particularly at fine scales. This model further provokes the Harte-Maddux debate: Harte et al. demonstrated that the power law form of the species–area relationship may be derived from a bisected, self-similar landscape and a community-level probability rule. However, Maddux showed that this self-similarity model generates biologically unrealistic predictions. Hui and McGeoch (2008) resolve
364-462: A region. Although intuitive, Gaston et al. and Gaston and Blackburn note that, due to the n -dimensional nature of the niche, this hypothesis is, in effect, untestable. Many species exhibit density-dependent dispersal and habitat 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 Source–sink dynamics ), thus increasing
416-553: 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. IUCN typically uses a cell size of 2 × 2 km in calculating AOO. In much of macroecology , 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 biogeographical in nature,
468-517: 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
520-437: A strong rescue effect . This model is appropriate to explain the range structure of a community that is influenced by metapopulation processes, such as dispersal and local extinction . However, it is not robust because the shape of the occupancy frequency distribution generated by this model is highly sensitive to species immigration and extinction parameters. The metapopulation model does also not explain scale dependence in
572-653: 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. Figure 2. Holt et al.'s model under different Hcrit values. Figure 2 a. shows
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#1732851517186624-470: 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 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
676-494: 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". The phylogenetic 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
728-495: Is different from Wikidata All article disambiguation pages All disambiguation pages Occupancy frequency distribution A typical form of OFD is a bimodal distribution , indicating the species in a community is either rare or common, known as Raunkiaer's law of distribution of frequencies. That is, with each species assigned to one of five 20%-wide occupancy classes, Raunkiaer's law predicts bimodal distributions within homogenous plant formations with modes in
780-441: 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 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
832-475: 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 Ideal Free Distribution (IFD). Gaston et al. cites Tyler and Hargrove who examined
884-510: Is measured by the convex hull or occupancy (occurrence), is part of the percolation process and can be explained by the percolation theory , 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: One way to deal with observed O–A relationships is, in essence, to deny their existence. An argument against
936-531: 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. A second statistical explanation involves the use of statistical distributions such as the Poisson or negative-binomial . 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
988-579: The area of occupancy (AOO) (see also the Scaling pattern of occupancy , 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
1040-571: The occupancy-abundance relationship . Other factors that have been proposed to be able to affect the shape of OFD include the degree of habitat heterogeneity, species specificity, landscape productivity, position in the geographic range, species dispersal ability and the extinction–colonization dynamics. Three basic models have been proposed to explain the bimodality found in occupancy frequency distributions. Random sampling of individuals from either lognormal or log-series rank abundance distributions (where random choice of an individual from
1092-491: The occupancy–abundance ( O–A ) relationship is the relationship between the abundance of species and the size of their ranges within a region. This relationship is perhaps one of the most well-documented relationships in macroecology , and applies both intra- and interspecifically (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
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#17328515171861144-438: 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. No matter which concept we use in studies, it
1196-534: The Harte–Maddux debate by demonstrating that the problems identified by Maddux result from an assumption that the probability of occurrence of a species at one scale is independent of its probability of occurrence at the next, and further illustrate the importance of considering patterns of species co-occurrence, and the way in which species occupancy patterns change with scale, when modeling species distributions. Occupancy-abundance relationship In ecology ,
1248-540: 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. In a classical metapopulation 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,
1300-472: The conservation of endangered species , the mechanism(s) underlying it remain poorly understood. Range – means the total area occupied by the species of interest in the region under study (see below 'Measures of species geographic range') Abundance – 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) Intraspecific occupancy–abundance relationship – means
1352-504: The contribution of individual species to the overall relationship and they showed that the main mechanisms in operation may be different for different species groups. 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, constituting different endpoints of
1404-472: The degrees of freedom available for testing the relationship. However Gaston et al. cite several studies documenting significant O–A relationships in spite of controlling for phylogenetic non-independence. 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
1456-454: 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
1508-408: 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 is expected under situations where habitat quality varies through time (more or less area above Hcrit. Most of the different explanations that have been forwarded to explain
1560-439: The emergent patterns across 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. By incorporating specific information on a species' diet, reproduction, dispersal and habitat specialisation Verberk et al. could successfully explain
1612-448: 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 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
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1664-410: The fact that in any association there are more species with few individuals than with many, that the law is most apparent when quadrats are chosen of the most serviceable size to show frequency, and that it is obscured or lost if the quadrats are either too large or too small." Evidently, there are different shapes of OFD found in literature. Tokeshi reported that approximately 46% of observations have
1716-402: The first (0-20%) and last (81-100%) classes. Although Raunkiaer's law has long been discounted as an index of plant community homogeneity, the method of using occupancy classes to construct OFDs is still commonly used for both plant and animal assemblages. Henry Gleason commented on this law in a 1929 Ecology article: "In conclusion we may say that Raunkiaer's law is merely an expression of
1768-411: 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
1820-405: The identity of a species. Therefore, it may not be too surprising that neutral models can accurately describe these community properties. 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
1872-439: 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. The vital rates of a species (in particular r – the intrinsic rate of increase; see Population dynamics ) interact with
1924-417: The intraspecific O–A relationships within the region 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 realized range is the portion of the potential range that
1976-414: The margin of their range. If this is 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
2028-410: 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. 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 extent of occurrence ( EOO ) (For definition: see ALA and Fig.1 ) and
2080-411: The number of core (common) species increases, showing a tendency from a bimodal OFD towards a right-skewed unimodal distribution. This is because species range , measured as occupancy, is strongly affected by the spatial scale and its aggregation structure, known often as the scaling pattern of occupancy . Such scale dependence of occupancy has a profound effect on other macroecological patterns, such as
2132-555: The occupancy frequency distribution. The third model that describes bimodality in the occupancy frequency distribution is based on the scaling pattern of occupancy under a self-similar assumption of species distributions (called the occupancy probability transition [OPT] model). The OPT model is based on Harte et al.'s bisection scheme (although not on their probability rule) and the recursion probability of occupancy at different scales. The OPT model has been shown to support two empirical observations: The OPT model demonstrates that
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2184-439: The patterns in species abundance including a 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
2236-413: 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. Brown suggested that species with a broad ecological niche would, as a consequence, be able to obtain higher local densities, and
2288-410: 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
2340-751: 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 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. 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
2392-405: The relationship between abundance and range size within a single species generated using time series data 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
2444-403: The same term [REDACTED] This disambiguation page lists articles associated with the title OFD . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=OFD&oldid=1237171764 " Category : Disambiguation pages Hidden categories: Short description
2496-402: The sample grain of a study, sampling adequacy, and the distribution of species saturation coefficients (a measure of the fractal dimensionality of a species distribution) in a community are together largely able to explain the patterns commonly found in empirical occupancy distributions. Hui and McGeoch (2007) further show that the self-similarity in species distributions breaks down according to
2548-436: 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
2600-420: 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
2652-412: The system is maintained by dispersal between patches (e.g. dispersal from patches with high 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
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#17328515171862704-448: 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 inclusion of discontinuities within the EOO could significantly influence results. In the extreme case where occupied habitats are distributed at random throughout
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