Occupancy–abundance relationship

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 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 the intraspecific O–A relationships within the region == Measures of species' geographic range ==

'' 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 size). 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 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.) and the area of occupancy (AOO) (see also the Scaling pattern of occupancy, and for a definition, see Fig. 2 and ALA 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. IUCN typically uses a cell size of 2 × 2 km in calculating AOO. 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 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 is measured by the convex hull or occupancy (occurrence), is part of the percolation process and can be explained by the percolation theory, Temporal occupancy-abundance relationship The temporal version of the occupancy-abundance relationship has been studied less extensively than the spatial version above, a similar pattern emerges. In a long-term time series of fish abundance in the Bristol Channel, it was observed that species that were present very occasionally were also in low abundance (comprising most species; a well-known general pattern observed in species abundance distributions), whereas species that were frequently present were also the most abundant. This is also a temporal analogue of Hanski's core-satellite hypothesis -- that species may be split into occasionally present and frequently present species. == Possible explanations ==

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, suggested that species with a broad ecological niche 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 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. 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 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 Ideal Free Distribution (IFD). Gaston et al. 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. Metapopulation dynamics 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, 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 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. Figure 2. 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 is expected under situations where habitat quality varies through time (more or less area above Hcrit. == Explaining the occupancy–abundance relationship ==

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 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 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 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 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 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 the same continuum. == Implications ==

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

• Indexing abundance – Documenting the abundance of a species is a resource-intensive, and time-consuming process. • Setting harvest rates – Especially in the case of commercial fisheries, 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 overharvesting. • Conservation biology – 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. == Importance of the interspecific O–A relationship ==

• Biodiversity 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 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. • 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 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. • Invasive species – 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. == See also ==