BIOFRAG - a new database for analyzing BIOdiversity responses to forest FRAGmentation

Peer reviewe

BIOFRAG – a new database for analyzing BIOdiversity responses to forest FRAGmentation

Habitat fragmentation studies have produced complex results that are challenging to synthesize. Inconsistencies among studies may result from variation in the choice of landscape metrics and response variables, which is often compounded by a lack of key statistical or methodological information. Collating primary datasets on biodiversity responses to fragmentation in a consistent and flexible database permits simple data retrieval for subsequent analyses. We present a relational database that links such field data to taxonomic nomenclature, spatial and temporal plot attributes, and environmental characteristics. Field assessments include measurements of the response(s) (e.g., presence, abundance, ground cover) of one or more species linked to plots in fragments within a partially forested landscape. The database currently holds 9830 unique species recorded in plots of 58 unique landscapes in six of eight realms: mammals 315, birds 1286, herptiles 460, insects 4521, spiders 204, other arthropods 85, gastropods 70, annelids 8, platyhelminthes 4, Onychophora 2, vascular plants 2112, nonvascular plants and lichens 320, and fungi 449. Three landscapes were sampled as long-term time series (>10 years). Seven hundred and eleven species are found in two or more landscapes. Consolidating the substantial amount of primary data available on biodiversity responses to fragmentation in the context of land-use change and natural disturbances is an essential part of understanding the effects of increasing anthropogenic pressures on land. The consistent format of this database facilitates testing of generalizations concerning biologic responses to fragmentation across diverse systems and taxa. It also allows the re-examination of existing datasets with alternative landscape metrics and robust statistical methods, for example, helping to address pseudo-replication problems. The database can thus help researchers in producing broad syntheses of the effects of land use. The database is dynamic and inclusive, and contributions from individual and large-scale data-collection efforts are welcome.Keywords: Species turnover, Data sharing, Database, Global change, Landscape metrics, Edge effects, Forest fragmentation, Matrix contrast, Bioinformatic

Trade in wild-sourced African grey parrots: Insights via social media

The rise of social media is changing the global trade of wildlife, presenting new challenges and opportunities for regulating and monitoring trade in threatened species. Parrots are among the most threatened groups of birds with wild populations of many species exploited in large numbers to supply the global pet trade. This trade increasingly occurs online yet the role of social media remains poorly understood. We examined trade in wild-sourced Grey parrots between 2014 and 2017, integrating data gathered via social media with other information sources and expert knowledge to gain insight into the scale and scope of trade. We identified 259 posts featuring trade in wild-sourced Grey parrots showing parrots held in transport containers or holding facilities. At least 70% of posts featured trade likely in breach of national laws or CITES regulations and basic welfare conditions were frequently not met. An examination of the locations of traders together with ancillary information enabled us to describe a number of opportunities for interventions to disrupt illegal trade, including major trade routes. Overall levels of trade activity, measured as numbers of posts, showed surprisingly little variation over time with the exception of a spike in activity in the months immediately proceeding new restrictions on international trade in wild-sourced Grey parrots for commercial purposes. Throughout the study period, the majority of exports originated from the Democratic Republic of Congo, with smaller numbers of posts from traders in Cameroon, Guinea and Ivory Coast. The trade activity of importers was more dynamic with North Africa playing a diminishing role and countries of the Persian Gulf increasing in prominence. The majority of importers were based in western and southern Asia, notably Turkey, Pakistan, Jordan and Iraq most recently. Turkey also played a prominent role as a transit point for air transport between Africa and Asia. There is an urgent need for targeted actions by airlines and enforcement agencies to disrupt illegal trade and by social media companies to improve monitoring and regulation of wildlife trade online. Keywords: Online trade, Social media, Illegal wildlife trade, Caged-bird trade, Parrot conservatio

The Customer Isn't Always Right—Conservation and Animal Welfare Implications of the Increasing Demand for Wildlife Tourism

<div><p>Tourism accounts for 9% of global GDP and comprises 1.1 billion tourist arrivals per annum. Visits to wildlife tourist attractions (WTAs) may account for 20–40% of global tourism, but no studies have audited the diversity of WTAs and their impacts on the conservation status and welfare of subject animals. We scored these impacts for 24 types of WTA, visited by 3.6–6 million tourists per year, and compared our scores to tourists’ feedback on TripAdvisor. Six WTA types (impacting 1,500–13,000 individual animals) had net positive conservation/welfare impacts, but 14 (120,000–340,000 individuals) had negative conservation impacts and 18 (230,000–550,000 individuals) had negative welfare impacts. Despite these figures only 7.8% of all tourist feedback on these WTAs was negative due to conservation/welfare concerns. We demonstrate that WTAs have substantial negative effects that are unrecognised by the majority of tourists, suggesting an urgent need for tourist education and regulation of WTAs worldwide.</p></div

Welfare and conservation scores for the 24 selected WTA types.

<p><b>BD</b> = Bear dancing, <b>BF</b> = Bear bile farms, <b>BP</b> = Bear parks, <b>BS</b> = Bear sanctuary, <b>CC</b> = Civet coffee, <b>CF</b> = Crocodile farms, <b>DC</b> = Captive dolphin interactions, <b>DM</b> = Dancing macaques, <b>DS</b> = Dolphin sanctuary, <b>DW</b> = Wild dolphin interactions, <b>EP</b> = Elephant parks, <b>ES</b> = Elephant sanctuary, <b>GT</b> = Gorilla trekking, <b>GW</b> = Gibbon watching, <b>HM</b> = Hyena men (Nigeria), L<b>E</b> = Lion encounters, <b>LS</b> = Lion sanctuary, <b>OS</b> = Orang-utan sanctuary, <b>PW</b> = Polar bear watching, <b>SC</b> = Snake charming, <b>SD</b> = Shark cage diving, <b>SF</b> = Sea turtle farm, <b>TF</b> = Tiger farms, <b>TI</b> = Tiger interactions.</p

Flow charts detailing the logic underpinning the allocation of a) conservation scores and b) welfare scores to types of wildlife tourist attractions (WTA types).

<p>Final scores range from -3 to +3 and are indicated below the relevant boxes. LC, NT, VU, EN, CR indicate the IUCN Redlist status of the species (Least Concern, Near Threatened, Vulnerable, Endangered, Critically Endangered). Please see accompanying information in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138939#pone.0138939.s001" target="_blank">S1 Appendix</a>.</p

Tourist dissatisfaction scores from TripAdvisor reviews (measured as the percentage of all positive and negative reviews that were negative).

<p>Bars represent the median, boxes the interquartile range, and asterisks outlying points. Numbers above each column, for reference, show the independently awarded conservation and welfare scores, respectively, for each attraction. “C” and “W” denote captive and wild dolphin interactions.</p

Conservation and welfare scores, accessibility (number of visitors per annum and number of animals held) and tourist dissatisfaction score (percentage of reviews on TripAdvisor that were negative for WTAs within a given type) for 24 representative WTA types, selected across five categories of WTA.

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138939#pone.0138939.g001" target="_blank">Fig 1A and 1B</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138939#pone.0138939.s006" target="_blank">S3A Table</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138939#pone.0138939.s006" target="_blank">S3X Table</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138939#pone.0138939.s007" target="_blank">S4A Table</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138939#pone.0138939.s007" target="_blank">S4X Table</a> for score derivation and supporting references, respectively.</p