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Investigating the utility of correlative distribution models to conservation science and macroecology
Abstract
BLDSC reference no.: D237046. Supervisor: David Rogers. Thesis (D.Phil.)--University of Oxford, 2005. Includes bibliographical references.
... 22 By 2006, eight postgraduate students had completed theses based on analyses of the SABAP database. [32][33][34][35][36][37][38][39] These students were at five universities (three in South Africa and two in the U.K.), and explored the database from a variety of disciplines and perspectives, further emphasizing its richness. ...
The first Southern African Bird Atlas Project was launched in 1986 and gathered bird distribution data from six countries of southern Africa. The project culminated with the publication of The Atlas of SouthernAfrican Birds in 1997. The database generated by the project, seven million bird distribution records, has been widely used by four groups: environmental consultants (for example, to locate electricity transmission lines), conservationists (planning conservation strategies), research scientists (especially macro-ecologists and biogeographers) and birders (ecotourism materials). By 2007, the database had spawned 50 research publications and eight Ph.D.s and master’s degrees. These products are a tribute to the more than 5000 ‘citizen scientists’, who gathered the bulk of the data. The atlas concept has been extended to frogs, reptiles, spiders and butterflies; a second bird atlas started in 2007 and will, for example, facilitate knowledge of the impact of environmental change on birds. The South African National Biodiversity Institute
is playing a lead role in initiating these new projects.
... Elsewhere, the relatively fine-scale mapping of birds in Britain and Ireland has been extensively analysed in relation to land-use and other factors likely to be useful in explaining observed distributions (see, for example, Atkinson et al. 2002;Fuller 2000, 2001). McPherson (2005) found that models, including those of Carswell et al. (2005) can generate good predictive maps of the distributions of African birds. But, as one might expect, the coarsegrained data of larger grids are more difficult to use than fine-grained data, particularly georeferenced points (McPherson et al. 2006); though the large-grid data have been shown to be useful in pinpointing areas within which conservation efforts can be concentrated (Tushabe and Fjeldså 2008). ...
In this paper, we argue that bird atlases, and the databases from which they are produced, are becoming increasingly valuable resources – but only in some parts of the world. There is a striking lack of atlases for almost all of the world's species-rich areas, most notably tropical America and tropical Asia. Yet even comparatively modest data sets (we take Uganda as an example) can be used to create an atlas. Further, their data can yield interesting information with clear value for conservation planning. For instance, we can see that Uganda's main savanna parks are quite well-placed in relation to raptor species richness, whilst other species of conservation concern are less well covered. In contrast, the fine-scale data-rich atlas projects in many American and European countries provide detailed information of great value. Taking examples from England, we show some of their uses in planning both for physical developments and for conservation. Repeating atlas projects after an interval of several years highlights changing distributions and, increasingly, changing levels of abundance. We believe that every encouragement should be given to new (and repeat) atlasing projects - but most especially in the tropics.
... Measures of rainfall variability, for example, were on average more popular in individual species' models than indices of mean temperature, an observation that runs counter to the findings for species richness (see Results). Links between the environmental associations of individual species and the environmental correlates of species richness may therefore not always be straightforward and require further investigation (McPherson, 2005). ...
Aim Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey-based species counts; or (3) superimposing models of individual species’ distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns.
Location Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates.
Methods Four species richness maps were compiled based on range maps, field-derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat–water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness.
Results The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad-scale gradients in species diversity.
Main conclusions Because the ‘true’ spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large-scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data — here the distributions of individual species — and their environmental associations may offer important insights into the ultimate causes of observed broad-scale patterns.
Models of habitat associations for species often are developed with an implicit assumption that habitats are static, even though recent disturbance may have altered the landscape. We tested our hypothesis that trajectory and magnitude of habitat change influenced observed distribution and abundance of passerine birds breeding in shrubsteppe habitats of southwestern Idaho. Birds in this region live in dynamic landscapes undergoing predominantly large-scale, radical, and unidirectional habitat change because wildfires are converting shrublands into expanses of exotic annual grasslands. We used data from field surveys and satellite image analyses in a series of redundancy analyses to partition variances and to determine the relative contribution of habitat change and current landscapes. Although current habitats explained a greater proportion of total variation, changes in habitat and measures of habitat richness and texture also contributed to variation in abundance of Horned Larks (Eremophila alpestris), Brewer's Sparrows (Spizella breweri), and Sage Sparrows (Amphispiza belli). Abundance of birds was insensitive to scale for nonspatial habitat variables. In contrast, spatial measures of habitat richness and texture in the landscape were significant only at large spatial scales. Abundance of Horned Larks, Western Meadowlarks (Sturnella neglecta), and Brewer's Sparrows, but not Sage Thrashers (Oreoscoptes montanus) or Sage Sparrows, was positively correlated with changes toward stable habitats. Because dominant habitat changes were toward less stable conditions, regional declines of those birds in shrubsteppe habitats reflect current landscapes as well as the history, magnitude, and trajectory of habitat change.
Typical assessments of models where many species occurrences are predicted (e.g., from species-habitat matrices or Gap Analyses) report overall omission and commission errors. Yet species' attributes suggest that we may predict a priori that some species are more likely to be modeled correctly than others. Because the likelihood of modeling species correctly is related to species incidences in surveys, a method was created that ranked the 183 avian species known to be breeding in Maine as to how likely they would be to occur in surveys. Attributes (e.g., population level, niche width, aggregation) were used to model 79% of the variation in incidence within the Maine Breeding Bird Atlas. Likelihood of Occurrence Ranks (LOORs) were assigned to each species based upon the modeled incidences to reflect how likely the species are to be observed in future surveys. The occurrence of birds on areas with species checklists were then modeled and compared to the LOORs. For five of six areas, the number of species correctly modeled using species-habitat associations was highly correlated with LOORs: species judged a priori to be likely to be modeled correctly actually were. For one large area (9172 ha) with a checklist covering 52 years, the number of species correctly modeled was not correlated with LOORs, evidence that the checklist is essentially complete. In general, sites with checklists from many years (e.g., > 10 yr) and from large areas (e.g., > 1000 ha) yielded the lowest commission error. These results demonstrate that the confidence assigned to results where the occurrences of species are modeled (e.g., Gap Analysis) is highly dependent on the test sets and the species modeled.
Studies of factors influencing avian biodiversity yield very different results depending on the spatial scale at which species richness is calculated Ecological studies at small spatial scales (plot size 0.0025-0.4 km(2)) emphasize the importance of habitat diversity, whereas biogeographical studies at large spatial scales (quadrat size 400-50,000 km(2)) emphasize variables related to available energy such as temperature. In order to bridge the gap between those two approaches the bird atlas data set of Lake Constance was used to study factors determining avian species diversity at the intermediate spatial scales of landscapes (quadrat size 4-36 km(2)). At these spatial scales bird species richness was influenced by habitat diversity and not by variables related to available energy probably because, at the landscape scale, variation in available energy is small. Changing quadrat size between 4 and 36 km(2), but keeping the geographical extension of the study constant resulted in profound changes in the degree to which the amount of different habitat types was correlated with species richness. This suggests that high species diversity is achieved by different management regimes depending on the spatial scale at which species richness is calculated. However, generally avian species diversity seems to be determined by spatial heterogeneity at the corresponding spatial scale. Thus, protecting the diversity of landscapes and ecosystems appears to ensure also high levels of species diversity.
The successful integration of theories of habitat selection into landscape ecology depends upon the proper identification of habitat. Habitats must be distinguished at appropriate spatial scales, and evidence must be provided that individuals recognize and respond to the habitat classification. We use evolutionary theories to document how density-dependent habitat selection and habitat variation can be applied to identify habitats in landscapes. We apply our protocol to small-mammal (Clethrionomys gapperi) populations across a series of repeated landscapes in the Hudson Bay Lowland of Ontario, Canada. We postulated initially that the landscapes were composed of two easily distinguishable habitats, but our protocol demonstrated three habitat types influencing density-dependent habitat selection by C. gapperi. The third habitat has profound implications for population regulation in the lowland and highlights the importance of proper habitat identification.