An integrated approach for predicting fates of reintroductions with demographic data from multiple populations

We devised a novel approach to model reintroduced populations whereby demographic data collected from multiple sites are integrated into a Bayesian hierarchical model. Integrating data from multiple reintroductions allows more precise population-growth projections to be made, especially for populations for which data are sparse, and allows projections that account for random site-to-site variation to be made before new reintroductions are attempted. We used data from reintroductions of the North Island Robin (Petroica longipes), an endemic New Zealand passerine, to 10 sites where non-native mammalian predators are controlled. A comparison of candidate models that we based on deviance information criterion showed that rat-tracking rate (an index of rat density) was a useful predictor of robin fecundity and adult female survival, that landscape connectivity and a binary measure of whether sites were on a peninsula were useful predictors of apparent juvenile survival (probably due to differential dispersal away from reintroduction sites), and that there was unexplained random variation among sites in all demographic rates. We used the two best supported models to estimate the finite rate of increase (λ) for populations at each of the 10 sites, and for a proposed reintroduction site, under different levels of rat control. Only three of the reintroduction sites had λ distributions completely >1 for either model. At two sites, λ was expected to be >1 if rat-tracking rates were <5%. At the other five reintroduction sites, λ was predicted to be close to 1, and it was unclear whether growth was expected. Predictions of λ for the proposed reintroduction site were less precise than for other sites because distributions incorporated the full range of site-to-site random variation in vital rates. Our methods can be applied to any species for which postrelease data on demographic rates are available and potentially can be extended to model multiple species simultaneously.     

Land trust evaluation of progress toward conservation goals

In the United States local land trusts preserve and conserve land to protect water quality and supply, farmlands, habitat for native plants and animals, areas of cultural or historical significance, and scenic views. We surveyed the 24 active, local land trusts in North Carolina (U.S.A.) to determine how they report progress toward attaining such conservation goals. Twenty-two land trusts responded to our survey. Of these, eight reported developing specific conservation goals for all of the properties they protect, five identified conservation targets on all properties, and two reported monitoring biological indicators on all of their protected properties. On the basis of these results, we believe most of the land trusts surveyed could not determine whether they were meeting conservation goals because most did not identify explicit, measurable goals and monitor progress toward them. Instead they reported success in terms of the amount of land protected and money raised. We think this is a lost opportunity for land trusts to build sound approaches to environmental management, engage the public, entice new donors, and attain funding for additional conservation activities. We propose conservation professionals help local land trusts adopt the Open Standards for the Practice of Conservation, a framework developed by a consortium of international conservation organizations, to develop conservation goals and measure whether the goals are achieved.     

Costs of expanding the network of protected areas as a response to climate change in the Cape Floristic Region

The expansion of protected areas is a critical component of strategies to promote the continued existence of biodiversity (i.e., life at all levels of biological organization) as climate changes, but scientific, social, and economic uncertainties associated with climate change are some of the major obstacles preventing such expansion. New models of climate change and species distribution and new methods of conservation planning now make it possible to explore the uncertainties associated with climate changes and species responses. Yet few reliable estimates of the costs of expanding protected areas and methods for determining these costs exist, largely because of the many (and uncertain) determinants of these costs. We developed a cost-accounting model to estimate the range in costs of various options for expanding protected areas and to explore the variables that drive these costs. Model development was informed by an existing plan to expand protected areas in the Cape Floristic Region of South Africa to address species conservation under a scenario of climate change. The 50-year present value of total costs varied from US$260 million ($1077/ha) for an off-reserve option that involves agreements with landowners and no compensation of forgone production and associated revenue to $1020 million ($4228/ha) for an on-reserve option that involves land acquisition and protection. The costs of acquiring land or compensating landowners for forgone production and development opportunities were the major drivers of the total costs across all options because most of the area identified in the protected-area expansion plan consisted of urban and high-quality agricultural lands. Total costs were also affected by changes in protected area extent and discount rate. Model-generated outputs such as these may be useful for informing implementation strategies and the allocation of future efforts in monitoring, data collection, and model development.     

Considering extinction of dependent species during translocation, ex situ conservation, and assisted migration of threatened hosts

Translocation, introduction, reintroduction, and assisted migrations are species conservation strategies that are attracting increasing attention, especially in the face of climate change. However, preventing the extinction of the suite of dependent species whose host species are threatened is seldom considered, and the effects on dependent species of moving threatened hosts are unclear. There is no published guidance on how to decide whether to move species, given this uncertainty. We examined the dependent-host system of 4 disparate taxonomic groups: insects on the feather-leaf banksia (Banksia brownii), montane banksia (B. montana), and Stirling Range beard heath (Leucopogon gnaphalioides); parasites of wild cats; mites and ticks on Duvaucel's gecko (Hoplodactylus duvaucelii) and tuatara (Sphenodon punctatus); and internal coccidian parasites of Cirl Bunting (Emberiza cirlus) and Hihi (Notiomystis cincta). We used these case studies to demonstrate a simple process for use in species- and community-level assessments of efforts to conserve dependents with their hosts. The insects dependent on Stirling Range beard heath and parasites on tigers (Panthera tigris) appeared to represent assemblages that would not be conserved by ex situ host conservation. In contrast, for the cases of dependent species we examined involving a single dependent species (internal parasites of birds and the mite Geckobia naultina on Duvaucel's gecko), ex situ conservation of the host species would also conserve the dependent species. However, moving dependent species with their hosts may be insufficient to maintain viable populations of the dependent species, and additional conservation strategies such as supplementing populations may be needed.     

Predicting species distributions from samples collected along roadsides

Predictive models of species distributions are typically developed with data collected along roads. Roadside sampling may provide a biased (nonrandom) sample; however, it is currently unknown whether roadside sampling limits the accuracy of predictions generated by species distribution models. We tested whether roadside sampling affects the accuracy of predictions generated by species distribution models by using a prospective sampling strategy designed specifically to address this issue. We built models from roadside data and validated model predictions at paired locations on unpaved roads and 200 m away from roads (off road), spatially and temporally independent from the data used for model building. We predicted species distributions of 15 bird species on the basis of point-count data from a landbird monitoring program in Montana and Idaho (U.S.A.). We used hierarchical occupancy models to account for imperfect detection. We expected predictions of species distributions derived from roadside-sampling data would be less accurate when validated with data from off-road sampling than when it was validated with data from roadside sampling and that model accuracy would be differentially affected by whether species were generalists, associated with edges, or associated with interior forest. Model performance measures (kappa, area under the curve of a receiver operating characteristic plot, and true skill statistic) did not differ between model predictions of roadside and off-road distributions of species. Furthermore, performance measures did not differ among edge, generalist, and interior species, despite a difference in vegetation structure along roadsides and off road and that 2 of the 15 species were more likely to occur along roadsides. If the range of environmental gradients is surveyed in roadside-sampling efforts, our results suggest that surveys along unpaved roads can be a valuable, unbiased source of information for species distribution models.     

Influence of habitat quality, population size, patch size, and connectivity on patch-occupancy dynamics of the middle spotted woodpecker

Despite extensive research on the effects of habitat fragmentation, the ecological mechanisms underlying colonization and extinction processes are poorly known, but knowledge of these mechanisms is essential to understanding the distribution and persistence of populations in fragmented habitats. We examined these mechanisms through multiseason occupancy models that elucidated patch-occupancy dynamics of Middle Spotted Woodpeckers (Dendrocopos medius) in northwestern Spain. The number of occupied patches was relatively stable from 2000 to 2010 (15-24% of 101 patches occupied every year) because extinction was balanced by recolonization. Larger and higher quality patches (i.e., higher density of oaks >37 cm dbh [diameter at breast height]) were more likely to be occupied. Habitat quality (i.e., density of large oaks) explained more variation in patch colonization and extinction than did patch size and connectivity, which were both weakly associated with probabilities of turnover. Patches of higher quality were more likely to be colonized than patches of lower quality. Populations in high-quality patches were less likely to become extinct. In addition, extinction in a patch was strongly associated with local population size but not with patch size, which means the latter may not be a good surrogate of population size in assessments of extinction probability. Our results suggest that habitat quality may be a primary driver of patch-occupancy dynamics and may increase the accuracy of models of population survival. We encourage comparisons of competing models that assess occupancy, colonization, and extinction probabilities in a single analytical framework (e.g., dynamic occupancy models) so as to shed light on the association of habitat quality and patch geometry with colonization and extinction processes in different settings and species.     

A generalized approach for producing,quantifying, and validating citizen science data from wildlife images

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org , more than 28,000 volunteers classified 1.51 million images taken in a large‐scale camera‐trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert‐verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post‐hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large‐scale monitoring of African wildlife.     

Characterizing Spatial Uncertainty when Integrating Social Data in Conservation Planning

Recent conservation planning studies have presented approaches for integrating spatially referenced social (SRS) data with a view to improving the feasibility of conservation action. We reviewed the growing conservation literature on SRS data, focusing on elicited or stated preferences derived through social survey methods such as choice experiments and public participation geographic information systems. Elicited SRS data includes the spatial distribution of willingness to sell, willingness to pay, willingness to act, and assessments of social and cultural values. We developed a typology for assessing elicited SRS data uncertainty which describes how social survey uncertainty propagates when projected spatially and the importance of accounting for spatial uncertainty such as scale effects and data quality. These uncertainties will propagate when elicited SRS data is integrated with biophysical data for conservation planning and may have important consequences for assessing the feasibility of conservation actions. To explore this issue further, we conducted a systematic review of the elicited SRS data literature. We found that social survey uncertainty was commonly tested for, but that these uncertainties were ignored when projected spatially. Based on these results we developed a framework which will help researchers and practitioners estimate social survey uncertainty and use these quantitative estimates to systematically address uncertainty within an analysis. This is important when using SRS data in conservation applications because decisions need to be made irrespective of data quality and well characterized uncertainty can be incorporated into decision theoretic approaches.     

Foundations of Resilience Thinking

Through 3 broad and interconnected streams of thought, resilience thinking has influenced the science of ecology and natural resource management by generating new multidisciplinary approaches to environmental problem solving. Resilience science, adaptive management (AM), and ecological policy design (EPD) contributed to an internationally unified paradigm built around the realization that change is inevitable and that science and management must approach the world with this assumption, rather than one of stability. Resilience thinking treats actions as experiments to be learned from, rather than intellectual propositions to be defended or mistakes to be ignored. It asks what is novel and innovative and strives to capture the overall behavior of a system, rather than seeking static, precise outcomes from discrete action steps. Understanding the foundations of resilience thinking is an important building block for developing more holistic and adaptive approaches to conservation. We conducted a comprehensive review of the history of resilience thinking because resilience thinking provides a working context upon which more effective, synergistic, and systems‐based conservation action can be taken in light of rapid and unpredictable change. Together, resilience science, AM, and EPD bridge the gaps between systems analysis, ecology, and resource management to provide an interdisciplinary approach to solving wicked problems. Fundamentos del Pensamiento sobre Resiliencia     

在校大学生肩宽及疏散速度的测量研究

为了确定疏散软件中符合中国人的疏散基本参数,采用现场测量的方法对在校男大学生的肩宽及各种情况下的疏散速度进行统计,并对测量结果进行分析。结果表明,在校男大学生平均肩宽为46．5 cm,正常状态下水平行走速度为1．37m／s,上楼速度为0．66m／s,下楼速度为1．36m／s,上坡速度为1．34m／s,下坡速度为1．59 m／s;在无障碍的情况下,紧急状态的疏散速度要比正常状态下的速度快得多,测量得到肩宽和疏散速度的数据,为疏散软件的改进及进一步研究人员疏散问题提供科学依据。