Distribution, range connectivity, and trends of bear populations in Southeast Asia

University of Minnesota Ph.D. dissertation. June 2017. Major: Conservation Biology. Advisor: Francesca Cuthbert. 1 computer file (PDF); ix, 123 pages.Sun bears and Asiatic black bears co-occur in Southeast Asia with wide areas of overlapping range. Both species are in decline, and are vulnerable to extinction due mainly to habitat loss and illegal hunting. Efforts to conserve bears in Southeast Asia are hampered by a lack of basic knowledge of distribution, population trends and habitat configuration. To advance the scientific understanding of sun bears and Asiatic back bears in this region I investigated fine and broad scale patterns of distribution. In Lao PDR, I gathered data on bear occurrence using bear sign transects walked in multiple forest blocks throughout the country. To model the country-wide relative abundance of bears and habitat quality, I related bear sign to environmental factors associated with bear occurrence. Within global sun bear range, I gathered camera trap records of sun bear detections from seven sun bear range countries. To generate quantitative measures of sun bear population trends, I related sun bear detection rates to tree cover and estimated related changes in country and global-level sun bear populations based on tree cover loss. To evaluate the global extent of sun bear range connectivity, I used the modelled relationship between sun bears and tree cover to create a habitat suitability index, and I identified areas of fractured range that have created unnatural subpopulations that are at risk from isolation. In Lao PDR, bears selected for areas of high elevation, rugged terrain, and areas of high tree density far from roads. My model-based estimates of sun bear global population trends predicted that over a 30-year period, sun bear populations in mainland southeast Asia have potentially declined by close to 20%, and insular sun bear populations have declined by ~50%. I identified seven potential sun bear subpopulations; two that are fully isolated with no potential for inter-subpopulation movement, and in the other five, inter and intra-subpopulation habitat fragmentation occurs in a continuum of severity. My findings advance the understanding of patterns in bear distribution and trends in southeast Asia, identify research priorities, and lay a framework for future monitoring efforts at country and region-level scales. I conclude with recommendations on how to better manage camera trap data for secondary research and sharing

Reversing “Empty Forest Syndrome” in Southeast Asia

[Extract] The diverse tropical forests of Southeast Asia are home to some of the most mysterious and beautiful wildlife species in the world, some of which have only been discovered in the last few decades. Home to species such as the antelope-like Saola (the Asian “unicorn”), which was only discovered in 1992 and that no biologist has seen in the wild, capturing the imagination of scientists, reporters and the public alike. Home to an extensive community of animals small and large, from civets to muntjacs, striped rabbits to Doucs, porcupines to pigs, tortoises to wild cattle. However, Southeast Asia also holds a higher proportion of globally threatened vascular plant, reptile, bird and mammal species than any other region on the planet. Today, these irreplaceable forests are often harboring the ghosts of these amazing species, victims of a barbaric and widespread hunting technique—the use of homemade and cheap wire snares that catch animals, leaving them trapped, often to suffer for days, before death. This hunting technique makes no distinction between common and Endangered species and is indiscriminately laying waste to any wildlife species regardless of their size and shape: Saola, Grey-shanked Douc, Southeast Asian Porcupine, Sambar Deer, Marbled Cat, Hog Badger, and the list goes on and on. Imagine walking into the Adirondacks in the northeastern United States and not seeing a single squirrel or raccoon. This idea appropriately has an ominous name: “empty forest syndrome.

Best practices and software for themanagement and sharing of camera trap data for small and large scales studies

Camera traps typically generate large amounts of bycatch data of non-target species that are secondary to the study’s objectives. Bycatch data pooled from multiple studies can answer secondary research questions; however, variation in field and data management techniques creates problems when pooling data from multiple sources. Multi-collaborator projects that use standardized methods to answer broad-scale research questions are rare and limited in geographical scope. Many small, fixed-term independent camera trap studies operate in poorly represented regions, often using field and data management methods tailored to their own objectives. Inconsistent data management practices lead to loss of bycatch data, or an inability to share it easily. As a case study to illustrate common problems that limit use of bycatch data, we discuss our experiences processing bycatch data obtained by multiple research groups during a range-wide assessment of sun bears Helarctos malayanus in Southeast Asia. We found that the most significant barrier to using bycatch data for secondary research was the time required, by the owners of the data and by the secondary researchers (us), to retrieve, interpret and process data into a form suitable for secondary analyses. Furthermore, large quantities of data were lost due to incompleteness and ambiguities in data entry. From our experiences, and from a review of the published literature and online resources, we generated nine recommendations on data management best practices for field site metadata, camera trap deployment metadata, image classification data and derived data products. We cover simple techniques that can be employed without training, special software and Internet access, as well as options for more advanced users, including a review of data management software and platforms. From the range of solutions provided here, researchers can employ those that best suit their needs and capacity. Doing so will enhance the usefulness of their camera trap bycatch data by improving the ease of data sharing, enabling collaborations and expanding the scope of research

Camera trap sun bear detection data, collected between 2000–2015, were combined from 31 field sites in 7 out of 11 sun bear range countries.

<p>Camera trap sun bear detection data, collected between 2000–2015, were combined from 31 field sites in 7 out of 11 sun bear range countries.</p

Projecting range-wide sun bear population trends using tree cover and camera-trap bycatch data

<div><p>Monitoring population trends of threatened species requires standardized techniques that can be applied over broad areas and repeated through time. Sun bears <i>Helarctos malayanus</i> are a forest dependent tropical bear found throughout most of Southeast Asia. Previous estimates of global population trends have relied on expert opinion and cannot be systematically replicated. We combined data from 1,463 camera traps within 31 field sites across sun bear range to model the relationship between photo catch rates of sun bears and tree cover. Sun bears were detected in all levels of tree cover above 20%, and the probability of presence was positively associated with the amount of tree cover within a 6-km² buffer of the camera traps. We used the relationship between catch rates and tree cover across space to infer temporal trends in sun bear abundance in response to tree cover loss at country and global-scales. Our model-based projections based on this “space for time” substitution suggested that sun bear population declines associated with tree cover loss between 2000–2014 in mainland southeast Asia were ~9%, with declines highest in Cambodia and lowest in Myanmar. During the same period, sun bear populations in insular southeast Asia (Malaysia, Indonesia and Brunei) were projected to have declined at a much higher rate (22%). Cast forward over 30-years, from the year 2000, by assuming a constant rate of change in tree cover, we projected population declines in the insular region that surpassed 50%, meeting the IUCN criteria for endangered if sun bears were listed on the population level. Although this approach requires several assumptions, most notably that trends in abundance across space can be used to infer temporal trends, population projections using remotely sensed tree cover data may serve as a useful alternative (or supplement) to expert opinion. The advantages of this approach is that it is objective, data-driven, repeatable, and it requires that all assumptions be clearly stated.</p></div

Model-based projections of sun bear population change across southeast Asia between 2000–2014.

<p>Bars, with 95% confidence intervals, show estimates generated by binary regression models (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185336#pone.0185336.e002" target="_blank">Eq 2</a>) fit to the pooled mainland and insular data. Models assumed log catch rate of sun bears at camera traps was a linear function of % tree cover averaged over a 6 km² circular area around camera traps. Country-level declines were predicted to be the most severe in Malaysia and Indonesia, which form the bulk of the insular region. On the mainland, declines roughly follow a longitudinal gradient, being highest in eastern countries (Cambodia, Lao PRD, Vietnam) and lowest in the west (India, Thailand, Myanmar).</p

Projected country-level sun bear population declines between 2000–2014, based on the modelled relationship between sun bear catch rate at camera traps and % tree cover.

<p>Models assumed log catch rate of sun bears at camera traps was a linear function of % tree cover averaged over a 6-km² circular area around camera traps. Country-level decline estimates are also summarized in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185336#pone.0185336.g004" target="_blank">Fig 4</a>.</p

Sun bear range limits and distribution of camera trap field sites from which sun bear detection data were collected between 2000–2015.

<p>Historic (within 500 years) sun bear range extends southwards, from southeast Bangladesh, northeast India and southern China, throughout most of mainland southeast Asia, and all of Malaysia and Indonesia [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185336#pone.0185336.ref023" target="_blank">23</a>]. Camera trap data, collected between 2000–2015, were combined from 7 out of 11 sun bear range countries to project range-wide population trends using changes in tree cover between 2000–2014.</p

Estimated regression coefficients and 95% bootstrap confidence intervals for <i>β</i>_<i>1</i>, relating the log expected catch rate to % tree cover.

<p>Regional models were fit to data from either the insular and mainland countries, allowing the response of sun bears to tree cover to vary by region. The insular data, catch rate per camera trap, were modelled using log-linear regression (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185336#pone.0185336.e001" target="_blank">Eq 1</a>) and the mainland data, detection/non-detection per camera trap within a trapping period, were modelled using a binary regression model with complementary log-log link (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185336#pone.0185336.e002" target="_blank">Eq 2</a>). The global model pooled all data, assuming bears responded similarly to tree cover throughout the range, and modelled detection/non-detection per camera trap within a trapping period using the binary regression model (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185336#pone.0185336.e002" target="_blank">Eq 2</a>). Models assumed log catch rate of sun bears at camera traps was a linear function of tree cover averaged over a 6-km² circular area surrounding the camera traps. Data were filtered to reduce variability in sampling intensity, by removing cameras active for < 7 days and > 90 days, and study sites with < 10 cameras.</p

The log-linear relationship between sun bear detection rates at camera traps and % tree cover.

<p>We pooled data from camera traps active between 2000–2015 into % tree cover categories (<20%, 21–30, 31–40, …91–100), and calculated the detection rate of sun bears within each category by dividing the total number cameras that detected a sun bear at least once within a trapping period by the total number of traps nights cameras were active. We log-transformed detection rates and increased by 1 to avoid infinite values. Camera traps were active within all levels of tree cover, and were more active in areas of high tree cover (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185336#pone.0185336.s003" target="_blank">S2 Fig</a>). Tree cover (0–100%) at camera traps, taken from rasters of tree cover closest in time to when cameras were active, were averaged over a 6-km² area around camera traps to represent tree cover at the scale of a core sun bear range. In a simple linear regression, sun bear detections (log) rates were positively related with % tree cover (ln([Y]/Trap Nights+1) = -8.16 + 0.03*Tree Cover, R² = 0.7).</p