We propose a biodiversity credit system for trading endangered species habitat designed to minimize and reverse the negative effects of habitat loss and fragmentation, the leading cause of species endangerment in the United States. Given the increasing demand for land, approaches that explicitly balance economic goals against conservation goals are required. The Endangered Species Act balances these conflicts based on the cost to replace habitat. Conservation banking is a means to manage this balance, and we argue for its use to mitigate the effects of habitat fragmentation. Mitigating the effects of land development on biodiversity requires decisions that recognize regional ecological effects resulting from local economic decisions. We propose Landscape Equivalency Analysis (LEA), a landscape-scale approach similar to HEA, as an accounting system to calculate conservation banking credits so that habitat trades do not exacerbate regional ecological effects of local decisions. Credits purchased by public agencies or NGOs for purposes other than mitigating a take create a net investment in natural capital leading to habitat defragmentation. Credits calculated by LEA use metapopulation genetic theory to estimate sustainability criteria against which all trades are judged. The approach is rooted in well-accepted ecological, evolutionary, and economic theory, which helps compensate for the degree of uncertainty regarding the effects of habitat loss and fragmentation on endangered species. LEA requires application of greater scientific rigor than typically applied to endangered species management on private lands but provides an objective, conceptually sound basis for achieving the often conflicting goals of economic efficiency and long-term ecological sustainability.
The relative influence of habitat loss, fragmentation and matrix heterogeneity on the viability of populations is a critical area of conservation research that remains unresolved. Using simulation modelling, we provide an analysis of the influence both patch size and patch isolation have on abundance, effective population size (N(e)) and F(ST). An individual-based, spatially explicit population model based on 15 years of field work on the red-cockaded woodpecker (Picoides borealis) was applied to different landscape configurations. The variation in landscape patterns was summarized using spatial statistics based on O-ring statistics. By regressing demographic and genetics attributes that emerged across the landscape treatments against proportion of total habitat and O-ring statistics, we show that O-ring statistics provide an explicit link between population processes, habitat area, and critical thresholds of fragmentation that affect those processes. Spatial distances among land cover classes that affect biological processes translated into critical scales at which the measures of landscape structure correlated best with genetic indices. Therefore our study infers pattern from process, which contrasts with past studies of landscape genetics. We found that population genetic structure was more strongly affected by fragmentation than population size, which suggests that examining only population size may limit recognition of fragmentation effects that erode genetic variation. If effective population size is used to set recovery goals for endangered species, then habitat fragmentation effects may be sufficiently strong to prevent evaluation of recovery based on the ratio of census:effective population size alone.
Molecular-genetic technology and statistical methods based on principles of population genetics provide valuable information to wildlife managers. Genetic data analyzed in a hierarchical, spatial context among individuals and among populations at micro-and macro-geographic scales has been widely used to provide information on the degree of population structure and to estimate rates of dispersal. Our goals were to (1) provide an overview of spatial statistics commonly used in empirical population genetics, and (2) introduce analytical designs that can be employed to extend hypothesis-testing capabilities by incorporating space-time interactions and by using information on habitat quality, distribution, and degree of connectivity. We show that genetics data can be used to quantify the degree of habitat permeability to dispersal and to qualify the negative consequences of habitat loss. We highlight empirical examples that use information on spatial genetic structure in areas of harvest derivation for admixed migratory species, wildlife disease, and habitat equivalency analysis.
Many species of conservation concern are spatially structured and require dispersal to be persistent. For such species, altering the distribution of suitable habitats on the landscape can affect population dynamics in ways that are difficult to predict from simple models. We argue that for such species, individual-based and spatially explicit population models (SEPMs) should be used to determine appropriate levels of off-site restoration to compensate for on-site loss of ecologic resources. Such approaches are necessary when interactions between biologic processes occur at different spatial scales (i.e., local [recruitment] and landscape [migration]). The sites of restoration and habitat loss may be linked to each other, but, more importantly, they may be linked to other resources in the landscape by regional biologic processes, primarily migration. The common management approach for determining appropriate levels of off-site restoration is to derive mitigation ratios based on best professional judgment or pre-existing data. Mitigation ratios assume that the ecologic benefits at the site of restoration are independent of the ecologic costs at the site of habitat loss. Using an SEPM for endangered red-cockaded woodpeckers, we show that the spatial configuration of habitat restoration can simultaneously influence both the rate of recruitment within breeding groups and the rate of migration among groups, implying that simple mitigation ratios may be inadequate.
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