Team creativity/innovation in culturally diverse teams: A meta‐analysis

This meta‐analysis investigates the direction and strength of the relationship between diversity in culturally diverse teams and team creativity/innovation. We distinguish the effects of two diversity levels (i.e., surface level vs. deep level) in culturally diverse teams and examine the moderators suggested by the socio‐technical systems framework (i.e., team virtuality and task characteristics in terms of task interdependence, complexity, and intellectiveness). Surface‐level diversity in culturally diverse teams is not related to team creativity/innovation, whereas deep‐level diversity in culturally diverse teams is positively related to team creativity/innovation. Moreover, surface‐level diversity in culturally diverse teams and team creativity/innovation are negatively related for simple tasks but unrelated for complex tasks. Deep‐level diversity in culturally diverse teams and team creativity/innovation is positively related for collocated teams and interdependent tasks but unrelated for noncollocated teams and independent tasks. We discuss the theoretical and practical implications.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

A test of the substitution–habitat hypothesis in amphibians

Most examples that support the substitution‐habitat hypothesis (human‐made habitats act as substitutes of original habitat) deal with birds and mammals. We tested this hypothesis in 14 amphibians by using percentage occupancy as a proxy of habitat quality (i.e., higher occupancy percentages indicate higher quality). We classified water body types as original habitat (no or little human influence) depending on anatomical, behavioral, or physiological adaptations of each amphibian species. Ten species had relatively high probabilities (0.16–0.28) of occurrence in original habitat, moderate probability of occurrence in substitution habitats (0.11–0.14), and low probability of occurrence in refuge habitats (0.05–0.08). Thus, the substitution–habitat hypothesis only partially applies to amphibians because the low occupancy of refuges could be due to the negligible human persecution of this group (indicating good conservation status). However, low occupancy of refuges could also be due to low tolerance of refuge conditions, which could have led to selective extinction or colonization problems due to poor dispersal capabilities. That original habitats had the highest probabilities of occupancy suggests amphibians have a good conservation status in the region. They also appeared highly adaptable to anthropogenic substitution habitats.     

Threshold responses of Amazonian stream fishes to timing and extent of deforestation

Deforestation is a primary driver of biodiversity change through habitat loss and fragmentation. Stream biodiversity may not respond to deforestation in a simple linear relationship. Rather, threshold responses to extent and timing of deforestation may occur. Identification of critical deforestation thresholds is needed for effective conservation and management. We tested for threshold responses of fish species and functional groups to degree of watershed and riparian zone deforestation and time since impact in 75 streams in the western Brazilian Amazon. We used remote sensing to assess deforestation from 1984 to 2011. Fish assemblages were sampled with seines and dip nets in a standardized manner. Fish species (n = 84) were classified into 20 functional groups based on ecomorphological traits associated with habitat use, feeding, and locomotion. Threshold responses were quantified using threshold indicator taxa analysis. Negative threshold responses to deforestation were common and consistently occurred at very low levels of deforestation (<20%) and soon after impact (<10 years). Sensitive species were functionally unique and associated with complex habitats and structures of allochthonous origin found in forested watersheds. Positive threshold responses of species were less common and generally occurred at >70% deforestation and >10 years after impact. Findings were similar at the community level for both taxonomic and functional analyses. Because most negative threshold responses occurred at low levels of deforestation and soon after impact, even minimal change is expected to negatively affect biodiversity. Delayed positive threshold responses to extreme deforestation by a few species do not offset the loss of sensitive taxa and likely contribute to biotic homogenization.     

Beehive fences as a multidimensional conflict‐mitigation tool for farmers coexisting with elephants

Increasing habitat fragmentation and human population growth in Africa has resulted in an escalation in human–elephant conflict between small‐scale farmers and free‐ranging African elephants (Loxodonta Africana). In 2012 Kenya Wildlife Service (KWS) implemented the national 10‐year Conservation and Management Strategy for the Elephant in Kenya, which includes an action aimed at testing whether beehive fences can be used to mitigate human–elephant conflict. From 2012 to 2015, we field‐tested the efficacy of beehive fences to protect 10 0.4‐ha farms next to Tsavo East National Park from elephants. We hung a series of beehives every 10 m around the boundary of each farm plot. The hives were linked with strong wire. After an initial pilot test with 2 farms, the remaining 8 of 10 beehive fences also contained 2‐dimensional dummy hives between real beehives to help reduce the cost of the fence. Each trial plot had a neighboring control plot of the same size within the same farm. Of the 131 beehives deployed 88% were occupied at least once during the 3.5‐year trial. Two hundred and fifty‐three elephants, predominantly 20–45 years old entered the community farming area, typically during the crop‐ ripening season. Eighty percent of the elephants that approached the trial farms were kept out of the areas protected by the beehive fences, and elephants that broke a fence were in smaller than average groups. Beehive fences not only kept large groups of elephants from invading the farmland plots but the farmers also benefited socially and financially from the sale of 228 kg of elephant‐friendly honey. As news of the success of the trial spread, a further 12 farmers requested to join the project, bringing the number of beehive fence protected farms to 22 and beehives to 297. This demonstrates positive adoption of beehive fences as a community mitigation tool. Understanding the response of elephants to the beehive fences, the seasonality of crop raiding and fence breaking, and the willingness of the community to engage with the mitigation method will help contribute to future management strategies for this high human–elephant conflict hotspot and other similar areas in Kenya.     

Successes and failures of compulsory risk mitigation: re‐evaluating the Turkish Catastrophe Insurance Pool

The Turkish Catastrophe Insurance Pool (TCIP) is one of the best practices of public–private partnerships in an emerging market designed to reduce economic losses from disasters. This paper reviews the application of this compulsory mechanism along with data relating to the performance of the scheme following recent earthquakes in Turkey. We also consider the current perceptions of Turkish society towards the TCIP and how they can be enhanced. Our conclusions aim to assist stakeholders in government, homeowners, insurance companies, media, banks and civil society to appreciate the value of the system and key actions necessary to improve it.     

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.     

A habitat‐based approach to predict impacts of marine protected areas on fishers

Although marine protected areas can simultaneously contribute to biodiversity conservation and fisheries management, the global network is biased toward particular ecosystem types because they have been established primarily in an ad hoc fashion. The optimization of trade‐offs between biodiversity benefits and socioeconomic values increases success of protected areas and minimizes enforcement costs in the long run, but it is often neglected in marine spatial planning (MSP). Although the acquisition of spatially explicit socioeconomic data is perceived as a costly or secondary step in MSP, it is critical to account for lost opportunities by people whose activities will be restricted, especially fishers. We developed an easily reproduced habitat‐based approach to estimate the spatial distribution of opportunity cost to fishers in data‐poor regions. We assumed the most accessible areas have higher economic and conservation values than less accessible areas and their designation as no‐take zones represents a loss of fishing opportunities. We estimated potential distribution of fishing resources from bathymetric ranges and benthic habitat distribution and the relative importance of the different resources for each port of total catches, revenues, and stakeholder perception. In our model, we combined different cost layers to produce a comprehensive cost layer so that we could evaluate of trade‐offs. Our approach directly supports conservation planning, can be applied generally, and is expected to facilitate stakeholder input and community acceptance of conservation.