Incorporating Uncertainty of Management Costs in Sensitivity Analyses of Matrix Population Models

The importance of accounting for economic costs when making environmental‐management decisions subject to resource constraints has been increasingly recognized in recent years. In contrast, uncertainty associated with such costs has often been ignored. We developed a method, on the basis of economic theory, that accounts for the uncertainty in population‐management decisions. We considered the case where, rather than taking fixed values, model parameters are random variables that represent the situation when parameters are not precisely known. Hence, the outcome is not precisely known either. Instead of maximizing the expected outcome, we maximized the probability of obtaining an outcome above a threshold of acceptability. We derived explicit analytical expressions for the optimal allocation and its associated probability, as a function of the threshold of acceptability, where the model parameters were distributed according to normal and uniform distributions. To illustrate our approach we revisited a previous study that incorporated cost‐efficiency analyses in management decisions that were based on perturbation analyses of matrix population models. Incorporating derivations from this study into our framework, we extended the model to address potential uncertainties. We then applied these results to 2 case studies: management of a Koala (Phascolarctos cinereus) population and conservation of an olive ridley sea turtle (Lepidochelys olivacea) population. For low aspirations, that is, when the threshold of acceptability is relatively low, the optimal strategy was obtained by diversifying the allocation of funds. Conversely, for high aspirations, the budget was directed toward management actions with the highest potential effect on the population. The exact optimal allocation was sensitive to the choice of uncertainty model. Our results highlight the importance of accounting for uncertainty when making decisions and suggest that more effort should be placed on understanding the distributional characteristics of such uncertainty. Our approach provides a tool to improve decision making.     

Inferring National and Regional Declines of Rare Orchid Species with Probabilistic Models

Abstract: Fragmentation of natural habitats can increase numbers of rare species. Conservation of rare species requires experts and resources, which may be lacking for many species. In the absence of regular surveys and expert knowledge, historical sighting records can provide data on the distribution of a species. Numerous models have been developed recently to make inferences regarding the threat status of a taxon on the basis of variation in trends of sightings over time. We applied 5 such models to national and regional (county) data on 3 red‐listed orchid species (Cephalanthera longifolia, Hammarbya paludosa, and Pseudorchis albida) and 1 species that has recently come to the attention of conservation authorities (Neotinea maculata) in the Republic of Ireland. In addition, we used an optimal linear estimate to calculate the time of extinction for each species overall and within each county. To account for bias in recording effort over time, we used rarefaction analysis. On the basis of sighting records, we inferred that these species are not threatened with extinction and, although there have been declines, there is no clear geographical pattern of decline in any species. Most counties where these orchid species occurred had a low number of sightings; hence, we were cautious in our interpretation of output from statistical models. We suggest the main drivers of decline in these species in Ireland are modification of habitats for increased agricultural production and lack of appropriate management. Our results show that the application of probabilistic models can be used even when sighting data are scarce, provided multiple models are used simultaneously and rarefaction is used to account for bias in recording effort among species over time. These models could be used frequently when making an initial conservation assessment of species in a region, particularly if there is a relatively constant recording rate and some knowledge of the underlying recording process. Regional‐scale analyses, such as ours, complement World Conservation Union criteria for assessment of the extinct category and are useful for highlighting areas of under recording and focusing conservation efforts of rare and endangered species.     

Integrating Biological and Social Values When Prioritizing Places for Biodiversity Conservation

The consideration of information on social values in conjunction with biological data is critical for achieving both socially acceptable and scientifically defensible conservation planning outcomes. However, the influence of social values on spatial conservation priorities has received limited attention and is poorly understood. We present an approach that incorporates quantitative data on social values for conservation and social preferences for development into spatial conservation planning. We undertook a public participation GIS survey to spatially represent social values and development preferences and used species distribution models for 7 threatened fauna species to represent biological values. These spatially explicit data were simultaneously included in the conservation planning software Zonation to examine how conservation priorities changed with the inclusion of social data. Integrating spatially explicit information about social values and development preferences with biological data produced prioritizations that differed spatially from the solution based on only biological data. However, the integrated solutions protected a similar proportion of the species’ distributions, indicating that Zonation effectively combined the biological and social data to produce socially feasible conservation solutions of approximately equivalent biological value. We were able to identify areas of the landscape where synergies and conflicts between different value sets are likely to occur. Identification of these synergies and conflicts will allow decision makers to target communication strategies to specific areas and ensure effective community engagement and positive conservation outcomes. Integración de Valores Biológicos y Sociales al Priorizar Sitios para la Conservación de la Biodiversidad