Effectiveness of multiple release sites in reintroduction of Persian fallow deer

Releasing animals in more than one location may increase or decrease the probability of success of a reintroduction project, yet the question of how many release sites to use has received little attention. We used empirical data from the reintroduction program of the Persian fallow deer (Dama mesopotamica) (Galilee region in northern Israel) in an individual-based spatially explicit simulation model to assess the effects of releasing deer from multiple sites. We examined whether multiple release sites increase reintroduction success, and if so, whether the optimal number of sites for a given scenario can be determined and whether the outcome differs if animals are released alternately (i.e., the location of the release alternates yearly between sites) or consecutively (i.e., one release site is used for several years, then another is used, and so forth). We selected 8 potential release sites in addition to the original site and simulated the release of 180 individuals at a rate of 10 individuals per year in different combinations of the original site and 1-4 additional sites. In our model, releasing animals into the wild at multiple sites produced higher population growth and greater spatial expansion than releasing animals at only one site and a consecutive-release approach was superior to an alternate-release approach. We suggest that through the use of simulation modeling that is based on empirical data from previous releases, managers can make better-informed decisions regarding the use of multiple release sites and greatly improve the probability of reintroduction success.     

Using machine learning to disentangle homonyms in large text corpora

Systematic reviews are an increasingly popular decision‐making tool that provides an unbiased summary of evidence to support conservation action. These reviews bridge the gap between researchers and managers by presenting a comprehensive overview of all studies relating to a particular topic and identify specifically where and under which conditions an effect is present. However, several technical challenges can severely hinder the feasibility and applicability of systematic reviews, for example, homonyms (terms that share spelling but differ in meaning). Homonyms add noise to search results and cannot be easily identified or removed. We developed a semiautomated approach that can aid in the classification of homonyms among narratives. We used a combination of automated content analysis and artificial neural networks to quickly and accurately sift through large corpora of academic texts and classify them to distinct topics. As an example, we explored the use of the word reintroduction in academic texts. Reintroduction is used within the conservation context to indicate the release of organisms to their former native habitat; however, a Web of Science search for this word returned thousands of publications in which the term has other meanings and contexts. Using our method, we automatically classified a sample of 3000 of these publications with over 99% accuracy, relative to a manual classification. Our approach can be used easily with other homonyms and can greatly facilitate systematic reviews or similar work in which homonyms hinder the harnessing of large text corpora. Beyond homonyms we see great promise in combining automated content analysis and machine‐learning methods to handle and screen big data for relevant information in conservation science.