Effectiveness of Environmental Surrogates for the Selection of Conservation Area Networks

Abstract:  Rapid biodiversity assessment and conservation planning require the use of easily quantified and estimated surrogates for biodiversity. Using data sets from Québec and Queensland, we applied four methods to assess the extent to which environmental surrogates can represent biodiversity components: (1) surrogacy graphs; (2) marginal representation plots; (3) Hamming distance function; and (4) Syrjala statistical test for spatial congruence. For Québec we used 719 faunal and floral species as biodiversity components, and for Queensland we used 2348 plant species. We used four climatic parameter types (annual mean temperature, minimum temperature during the coldest quarter, maximum temperature during the hottest quarter, and annual precipitation), along with slope, elevation, aspect, and soil types, as environmental surrogates. To study the effect of scale, we analyzed the data at seven spatial scales ranging from 0.01° to 0.10° longitude and latitude. At targeted representations of 10% for environmental surrogates and biodiversity components, all four methods indicated that using a full set of environmental surrogates systematically provided better results than selecting areas at random, usually ensuring that ≥90% of the biodiversity components achieved the 10% targets at scales coarser than 0.02°. The performance of surrogates improved with coarser spatial resolutions. Thus, environmental surrogate sets are useful tools for biodiversity conservation planning. A recommended protocol for the use of such surrogates consists of randomly selecting a set of areas for which distributional data are available, identifying an optimal surrogate set based on these areas, and subsequently prioritizing places for conservation based on the optimal surrogate set.     

Genetic Structure of Endangered Clapper Rail (Rallus longirostris) Populations in Southern California

We assessed the genetic structure of two subspecies of endangered Clapper Rails ( Rallus longirostris ) in Southern California using DNA fingerprinting to uncover variation in minisatellite DNA. Minisatellite DNA variation in the Salton Sea population of the R. l. yumanensis subspecies was at a level typical of outbred avian species (average proportion of fragments shared, or S, was 0.33). Variation was extremely low (S from 0.63 to 0.77), however, within four coastal, salt-marsh populations of the subspecies R. l. levipes located along a transect extending about 260 km northwest from the Mexican border. Between-population similarity (S_ij) was also high for the four levipes populations, although individuals of the small, isolated population at Mugu Lagoon consistently clustered separately in phenograms constructed using neighbor-joining or other algorithms. Individuals of yumanensis always clustered as a sister group to all levipes individuals. The minisatellite data were contrasted with the extremely low mtDNA and RAPD variation we found in both subspecies. We propose that variation in these less-mutable markers was lost in a bottleneck that occurred at least 1000 years ago, thus allowing sufficient time for recovery of variation in the rapidly mutating (μ≈} 0.001/gamete/generation) minisatellites (t = 1/μ, or 1000 generations). A second, more-recent bottleneck, or series of bottlenecks within a metapopulation structure, likely resulted in the depauparate variation seen in levipes today. We suggest that translocations from large to small levipes populations could restore important genetic variation to the small populations and would not compromise genetic boundaries.     

Analyzing Variability and the Rate of Decline of Migratory Shorebirds in Moreton Bay,Australia

Abstract: Estimating the abundance of migratory species is difficult because sources of variability differ substantially among species and populations. Recently developed state‐space models address this variability issue by directly modeling both environmental and measurement error, although their efficacy in detecting declines is relatively untested for empirical data. We applied state‐space modeling, generalized least squares (with autoregression error structure), and standard linear regression to data on abundance of wetland birds (shorebirds and terns) at Moreton Bay in southeast Queensland, Australia. There are internationally significant numbers of 8 species of waterbirds in the bay, and it is a major terminus of the large East Asian‐Australasian Flyway. In our analyses, we considered 22 migrant and 8 resident species. State‐space models identified abundances of 7 species of migrants as significantly declining and abundance of one species as significantly increasing. Declines in migrant abundance over 15 years were 43–79%. Generalized least squares with an autoregressive error structure showed abundance changes in 11 species, and standard linear regression showed abundance changes in 15 species. The higher power of the regression models meant they detected more declines, but they also were associated with a higher rate of false detections. If the declines in Moreton Bay are consistent with trends from other sites across the flyway as a whole, then a large number of species are in significant decline.