Multi-scale habitat modelling identifies spatial conservation priorities for mainland clouded leopards (Neofelis nebulosa)

Aim Deforestation is rapidly altering Southeast Asian landscapes, resulting in some of the highest rates of habitat loss worldwide. Among the many species facing declines in this region, clouded leopards rank notably for their ambassadorial potential and capacity to act as powerful levers for broader forest conservation programmes. Thus, identifying core habitat and conservation opportunities are critical for curbing further Neofelis declines and extending umbrella protection for diverse forest biota similarly threatened by widespread habitat loss. Furthermore, a recent comprehensive habitat assessment of Sunda clouded leopards (N. diardi) highlights the lack of such information for the mainland species (N. nebulosa) and facilitates a comparative assessment. Location Southeast Asia. Methods Species–habitat relationships are scale‐dependent, yet <5% of all recent habitat modelling papers apply robust approaches to optimize multivariate scale relationships. Using one of the largest camera trap datasets ever collected, we developed scale‐optimized species distribution models for two con‐generic carnivores, and quantitatively compared their habitat niches. Results We identified core habitat, connectivity corridors, and ranked remaining habitat patches for conservation prioritization. Closed‐canopy forest was the strongest predictor, with ~25% lower Neofelis detections when forest cover declined from 100 to 65%. A strong, positive association with increasing precipitation suggests ongoing climate change as a growing threat along drier edges of the species’ range. While deforestation and land use conversion were deleterious for both species, N. nebulosa was uniquely associated with shrublands and grasslands. We identified 800 km2 as a minimum patch size for supporting clouded leopard conservation. Main conclusions We illustrate the utility of multi‐scale modelling for identifying key habitat requirements, optimal scales of use and critical targets for guiding conservation prioritization. Curbing deforestation and development within remaining core habitat and dispersal corridors, particularly in Myanmar, Laos and Malaysia, is critical for supporting evolutionary potential of clouded leopards and conservation of associated forest biodiversity.Dr. Holly Reed Conservation Fund; Langtang National Park; World Animal Protection; Robertson Foundation; Point Defiance Zoo & Aquariu

Predicting biodiversity richness in rapidly changing landscapes: climate, low human pressure or protection as salvation?

Rates of biodiversity loss in Southeast Asia are among the highest in the world, and the Indo-Burma and South-Central China Biodiversity Hotspots rank among the world’s most threatened. Developing robust multi-species conservation models is critical for stemming biodiversity loss both here and globally. We used a large and geographically extensive remote-camera survey and multi-scale, multivariate optimization species distribution modelling to investigate the factors driving biodiversity across these two adjoining biodiversity hotspots. Four major findings emerged from the work. (i) We identified clear spatial patterns of species richness, with two main biodiverse centres in the Thai-Malay Peninsula and in the mountainous region of Southwest China. (ii) Carnivores in particular, and large ungulates to a lesser degree, were the strongest indicators of species richness. (iii) Climate had the largest effect on biodiversity, followed by protected status and human footprint. (iv) Gap analysis between the biodiversity model and the current system of protected areas revealed that the majority of areas supporting the highest predicted biodiversity are not protected. Our results highlighted several key locations that should be prioritized for expanding the protected area network to maximize conservation effectiveness. We demonstrated the importance of switching from single-species to multi-species approaches to highlight areas of high priority for biodiversity conservation. In addition, since these areas mostly occur over multiple countries, we also advocate for a paradigmatic focus on transboundary conservation planning.The majority of the team, as well as the data, were part of the core WildCRU effort supported principally by a Robertson Foundation grant to DWM

Pasif kızılötesi hareket algılayıcılı kameralar yardımıyla büyük memeli türlerinin Yenice Ormanları'nda incelenmesi

Widely applicable, quantitative field methods are needed to gather wildlife data for conservation and management initiatives in Turkey. In order to evaluate the use of camera traps in forest habitats of Turkey, we conducted a 5 phase camera trap survey by using 16 passive infrared-triggered cameras with a total sampling effort of 1200 camera trap days in Yaylacık Research Forest, a 50 km2 forest patch of Yenice Forest in Karabük during January-May 2006. The camera trap survey confirmed the presence of grey wolf (Canis lupus), brown bear (Ursus arctos), wildcat (Felis silvestris), red fox (Vulpes vulpes), badger (Meles meles), pine marten (Martes martes), roe deer (Capreolus capreolus) and wild boar (Sus scrofa) in the study area. The camera trap survey also revealed the presence of jackal (Canis aureus) and brown hare (Lepus europaeus), whose presence were not known by people living and working in the area. Contrary to the local belief, neither camera trapping survey nor ground survey confirmed the presence of lynx (Lynx lynx) in Yaylacık Research Forest. The wolf was observed to be crepuscular and the wildcat showed a diurnal activity pattern. Wildcat seemed to avoid other carnivores spatially and temporally. Simulation studies suggested that camera trap surveys should last 14 days for wolf, 13 days for wildcat, 10 days for pine marten, and 11 days for roe deer, while it is advisable to conduct longer surveys, probably 15-20 days, for wild boar, red fox and brown bears. The estimated population size for wildcat was 9 (SE=2.28227) with 95% confidence interval of 9 to 25 in the study area. A minimum of 6 brown bears were present in the study area. Our study indicated that the local knowledge about the presence of wildlife should be considered by researchers, but it cannot replace scientific surveys conducted by field biologists. This study was the first attempt to assess the presence, relative abundance, activity patterns and diversity of multiple mammal species by the use of camera trapping methodology in Turkey. The results suggest that camera trap surveys have the potential for gathering wildlife data at larger scales in Turkey, where information gap on large mammals is an obstacle for effective management and conservation of mammals.Ph.D. - Doctoral Progra

Leopard conservation in the Caucasus

The leopard Panthera pardus is a Critically Endangered flagship species of the Caucasus. In 2007, conservation experts and institutions from all six Caucasian countries joined to develop a Strategy for the Conservation of the Leopard in the Caucasus Ecoregion, based on a review of the status of the leopard population and its prey (Cat News Special Issue 2, 2007). Now, three years later, the IUCN/SSC Cat Specialist Group, WWF and NACRES organised a discussion group at the annual conference of the International Bear Association IBA in Tbilisi, Georgia. The meeting was part of the symposium “Large Carnivores in the Caucasus”, organised and supported by the Secretariat of the Convention on the Conservation of European Wildlife and Natural Habitats (Bern Convention). The leopard is listed as a strictly protected species in Appendix II of the Bern Convention. The aim of the meeting was to discuss the status of the leopard, the implementation of the Strategy and next steps with wildlife conservationists from the Caucasian countries

Tigers are 181 kg on average; their lifespan is up to 15 years in the wild and their densities range from 0.7 to 15.84 per 100 km² [15, 16] (Photograph by Özgün Emre Can).

<p>Tigers are 181 kg on average; their lifespan is up to 15 years in the wild and their densities range from 0.7 to 15.84 per 100 km² [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001001#pbio.2001001.ref015" target="_blank">15</a>, <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001001#pbio.2001001.ref016" target="_blank">16</a>] (Photograph by Özgün Emre Can).</p

Scientific crowdsourcing in wildlife research and conservation: Tigers (<i>Panthera tigris</i>) as a case study - Fig 4

<p>What the Internet “knows” about the terms (A) “crowdsourcing” and (B) “citizen science” based on a cluster search and Lingo clustering algorithm using Carrot² software via 17 Internet search engines [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001001#pbio.2001001.ref030" target="_blank">30</a>, <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001001#pbio.2001001.ref031" target="_blank">31</a>]. Each cell is a theme created by the algorithm, and the sizes of cells are proportional to the amount of information available in the clustered search results.</p

Project’s global outreach.

<p>Icons in red indicate the countries from where the project website was visited. Countries and the number of sessions (given in parenthesis) as they are reported by Google Analytics are as follows: Argentina (2), Armenia (1), Australia (32), Azerbaijan (1), Bangladesh (5), Belarus (1), Belgium (8), Belize (1), Bolivia (1), Brazil (97), Bulgaria (4), Cambodia (3), Canada (144), Chile (8), China (59), Colombia (5), Costa Rica (2), Croatia (1), Cyprus (2), Czechia (3), Denmark (18), Ecuador (3), Egypt (1), Estonia (1), Finland (10), France (18), Germany (17), Ghana (1), Gibraltar (1), Greece (11), India (131), Indonesia (42), Iran (2), Ireland (8), Israel (1), Italy (23), Japan (6), Kazakhstan (8), Kenya (2), Lithuania (1), Luxembourg (2), Malaysia (15), Mexico (11), Myanmar (2), Nepal (9), Netherlands (35), New Zealand (26), Norway (4), Peru (6), Poland (2), Portugal (7), Republic of Korea (3), Russian Federation (197), Saudi Arabia (1), Singapore (3), Slovakia (1), Slovenia (1), South Africa (10), Spain (24), Sri Lanka (2), Sweden (5), Switzerland (8), Thailand (4), Turkey (48), Ukraine (7), United Arab Emirates (1), United Kingdom (541), United States of America (350), Zimbabwe (4), and unknown (56).</p