Potential Misuse of Avian Density as a Conservation Metric

Abstract: Effective conservation metrics are needed to evaluate the success of management in a rapidly changing world. Reproductive rates and densities of breeding birds (as a surrogate for reproductive rate) have been used to indicate the quality of avian breeding habitat, but the underlying assumptions of these metrics rarely have been examined. When birds are attracted to breeding areas in part by the presence of conspecifics and when breeding in groups influences predation rates, the effectiveness of density and reproductive rate as indicators of habitat quality is reduced. It is beneficial to clearly distinguish between individual‐ and population‐level processes when evaluating habitat quality. We use the term reproductive rate to refer to both levels and further distinguish among levels by using the terms per capita fecundity (number of female offspring per female per year, individual level) and population growth rate (the product of density and per capita fecundity, population level). We predicted how density and reproductive rate interact over time under density‐independent and density‐dependent scenarios, assuming the ideal free distribution model of how birds settle in breeding habitats. We predicted population density of small populations would be correlated positively with both per capita fecundity and population growth rate due to the Allee effect. For populations in the density‐dependent growth phase, we predicted no relation between density and per capita fecundity (because individuals in all patches will equilibrate to the same success rate) and a positive relation between density and population growth rate. Several ecological theories collectively suggest that positive correlations between density and per capita fecundity would be difficult to detect. We constructed a decision tree to guide interpretation of positive, neutral, nonlinear, and negative relations between density and reproductive rates at individual and population levels.     

Use of Inverse Spatial Conservation Prioritization to Avoid Biological Diversity Loss Outside Protected Areas

Globally expanding human land use sets constantly increasing pressure for maintenance of biological diversity and functioning ecosystems. To fight the decline of biological diversity, conservation science has broken ground with methods such as the operational model of systematic conservation planning (SCP), which focuses on design and on‐the‐ground implementation of conservation areas. The most commonly used method in SCP is reserve selection that focuses on the spatial design of reserve networks and their expansion. We expanded these methods by introducing another form of spatial allocation of conservation effort relevant for land‐use zoning at the landscape scale that avoids negative ecological effects of human land use outside protected areas. We call our method inverse spatial conservation prioritization. It can be used to identify areas suitable for economic development while simultaneously limiting total ecological and environmental effects of that development at the landscape level by identifying areas with highest economic but lowest ecological value. Our method is not based on a priori targets, and as such it is applicable to cases where the effects of land use on, for example, individual species or ecosystem types are relatively small and would not lead to violation of regional or national conservation targets. We applied our method to land‐use allocation to peat mining. Our method identified a combination of profitable production areas that provides the needed area for peat production while retaining most of the landscape‐level ecological value of the ecosystem. The results of this inverse spatial conservation prioritization are being used in land‐use zoning in the province of Central Finland.     

Global population collapse in a superabundant migratory bird and illegal trapping in China

Persecution and overexploitation by humans are major causes of species extinctions. Rare species, often confined to small geographic ranges, are usually at highest risk, whereas extinctions of superabundant species with very large ranges are rare. The Yellow‐breasted Bunting (Emberiza aureola) used to be one of the most abundant songbirds of the Palearctic, with a very large breeding range stretching from Scandinavia to the Russian Far East. Anecdotal information about rapid population declines across the range caused concern about unsustainable trapping along the species’ migration routes. We conducted a literature review and used long‐term monitoring data from across the species’ range to model population trend and geographical patterns of extinction. The population declined by 84.3–94.7% between 1980 and 2013, and the species’ range contracted by 5000 km. Quantitative evidence from police raids suggested rampant illegal trapping of the species along its East Asian flyway in China. A population model simulating an initial harvest level of 2% of the population, and an annual increase of 0.2% during the monitoring period produced a population trajectory that matched the observed decline. We suggest that trapping strongly contributed to the decline because the consumption of Yellow‐breasted Bunting and other songbirds has increased as a result of economic growth and prosperity in East Asia. The magnitude and speed of the decline is unprecedented among birds with a comparable range size, with the exception of the Passenger Pigeon (Ectopistes migratorius), which went extinct in 1914 due to industrial‐scale hunting. Our results demonstrate the urgent need for an improved monitoring of common and widespread species’ populations, and consumption levels throughout East Asia.     

Testing Decision Rules for Categorizing Species’ Extinction Risk to Help Develop Quantitative Listing Criteria for the U.S. Endangered Species Act

Lack of guidance for interpreting the definitions of endangered and threatened in the U.S. Endangered Species Act (ESA) has resulted in case‐by‐case decision making leaving the process vulnerable to being considered arbitrary or capricious. Adopting quantitative decision rules would remedy this but requires the agency to specify the relative urgency concerning extinction events over time, cutoff risk values corresponding to different levels of protection, and the importance given to different types of listing errors. We tested the performance of 3 sets of decision rules that use alternative functions for weighting the relative urgency of future extinction events: a threshold rule set, which uses a decision rule of x% probability of extinction over y years; a concave rule set, where the relative importance of future extinction events declines exponentially over time; and a shoulder rule set that uses a sigmoid shape function, where relative importance declines slowly at first and then more rapidly. We obtained decision cutoffs by interviewing several biologists and then emulated the listing process with simulations that covered a range of extinction risks typical of ESA listing decisions. We evaluated performance of the decision rules under different data quantities and qualities on the basis of the relative importance of misclassification errors. Although there was little difference between the performance of alternative decision rules for correct listings, the distribution of misclassifications differed depending on the function used. Misclassifications for the threshold and concave listing criteria resulted in more overprotection errors, particularly as uncertainty increased, whereas errors for the shoulder listing criteria were more symmetrical. We developed and tested the framework for quantitative decision rules for listing species under the U.S. ESA. If policy values can be agreed on, use of this framework would improve the implementation of the ESA by increasing transparency and consistency. Evaluando Reglas de Decisión para Categorizar el Riesgo de Extinción de Especies con el Fin de Desarrollar de Criterios Cuantitativos de Alistamiento en el Acta de Especies en Peligro de los EE. UU.     

Representation of critical natural capital in China

Traditional means of assessing representativeness of conservation value in protected areas depend on measures of structural biodiversity. The effectiveness of priority conservation areas at representing critical natural capital (CNC) (i.e., an essential and renewable subset of natural capital) remains largely unknown. We analyzed the representativeness of CNC‐conservation priority areas in national nature reserves (i.e., nature reserves under jurisdiction of the central government with large spatial distribution across the provinces) in China with a new biophysical‐based composite indicator approach. With this approach, we integrated the net primary production of vegetation, topography, soil, and climate variables to map and rank terrestrial ecosystems capacities to generate CNC. National nature reserves accounted for 6.7% of CNC‐conservation priority areas across China. Considerable gaps (35.2%) existed between overall (or potential) CNC representativeness nationally and CNC representation in national reserves, and there was significant spatial heterogeneity of representativeness in CNC‐conservation priority areas at the regional and provincial levels. For example, the best and worst representations were, respectively, 13.0% and 1.6% regionally and 28.9% and 0.0% provincially. Policy in China is transitioning toward the goal of an ecologically sustainable civilization. We identified CNC‐conservation priority areas and conservation gaps and thus contribute to the policy goals of optimization of the national nature reserve network and the demarcation of areas critical to improving the representativeness and conservation of highly functioning areas of natural capital. Moreover, our method for assessing representation of CNC can be easily adapted to other large‐scale networks of conservation areas because few data are needed, and our model is relatively simple.