Effects of publication bias on conservation planning

Conservation planners need reliable information on spatial patterns of biodiversity. However, existing data sets are skewed because some ecosystems, taxa, and locations are underrepresented. We determined how many articles have been published in recent decades on the biodiversity of different countries and their constituent provinces. We searched the Web of Science catalogues Science Citation Index (SCI) and Social Science Citation Index (SSCI) for biodiversity-related articles published from 1993 to 2016 that included country and province names. We combined data on research publication frequency with other provincial-scale factors hypothesized to affect the likelihood of research activity (i.e., economic development, human presence, infrastructure, and remoteness). Areas that appeared understudied relative to the biodiversity expected based on site climate likely have been inaccessible to researchers for reasons, notably armed conflict. Geographic publication bias is of most concern in the most remote areas of sub-Saharan Africa and South America. Our provincial-scale model may help compensate for publication biases in conservation planning by revealing the spatial extent of research needs and the low cost of redoing this analysis annually.     

Monitoring,imperfect detection,and risk optimization of a Tasmanian devil insurance population

Most species are imperfectly detected during biological surveys, which creates uncertainty around their abundance or presence at a given location. Decision makers managing threatened or pest species are regularly faced with this uncertainty. Wildlife diseases can drive species to extinction; thus, managing species with disease is an important part of conservation. Devil facial tumor disease (DFTD) is one such disease that led to the listing of the Tasmanian devil (Sarcophilus harrisii) as endangered. Managers aim to maintain devils in the wild by establishing disease‐free insurance populations at isolated sites. Often a resident DFTD‐affected population must first be removed. In a successful collaboration between decision scientists and wildlife managers, we used an accessible population model to inform monitoring decisions and facilitate the establishment of an insurance population of devils on Forestier Peninsula. We used a Bayesian catch‐effort model to estimate population size of a diseased population from removal and camera trap data. We also analyzed the costs and benefits of declaring the area disease‐free prior to reintroduction and establishment of a healthy insurance population. After the monitoring session in May–June 2015, the probability that all devils had been successfully removed was close to 1, even when we accounted for a possible introduction of a devil to the site. Given this high probability and the baseline cost of declaring population absence prematurely, we found it was not cost‐effective to carry out any additional monitoring before introducing the insurance population. Considering these results within the broader context of Tasmanian devil management, managers ultimately decided to implement an additional monitoring session before the introduction. This was a conservative decision that accounted for uncertainty in model estimates and for the broader nonmonetary costs of mistakenly declaring the area disease‐free.     

Assessing citizen contributions to butterfly monitoring in two large cities

Citizen science may be especially effective in urban landscapes due to the large pool of potential volunteers. However, there have been few evaluations of the contributions of citizen scientists to knowledge of biological communities in and around cities. To assess the effectiveness of citizen scientists' monitoring of species in urban areas, we compared butterfly data collected over 10 years in Chicago, Illinois (U.S.A.), and New York City, New York (U.S.A.). The dates, locations, and methods of data collection in Chicago were standardized, whereas data from New York were collected at any location at any time. For each city, we evaluated whether the number of observers, observation days (days on which observations were reported), and sampling locations were associated with the reported proportion of the estimated regional pool of butterfly species. We also compared the number of volunteers, duration of volunteer involvement, and consistency of sampling efforts at individual locations within each city over time. From 2001 to 2010, there were 73 volunteers in Chicago and 89 in New York. During this period, volunteers observed 86% and 89% of the estimated number of butterfly species present in Chicago and New York, respectively. Volunteers in New York reported a greater proportion of the estimated pool of butterfly species per year. In addition, more species were observed per volunteer and observation day in New York, largely due to the unrestricted sampling season in New York. Chicago volunteers were active for more years and monitored individual locations more consistently over time than volunteers in New York. Differences in monitoring protocol--especially length of sampling season and selection protocol for monitoring locations--influenced the relationship between species accrual and sampling effort, which suggests these factors are important in volunteer-based species-monitoring programs.