Abstract: Spatial capture–recapture (SCR) methods use the location of
detectors (camera traps, hair snares and live-capture traps) and the
locations at which animals were detected (their spatial capture
histories) to estimate animal density. Despite the often large expense
and effort involved in placing detectors in a landscape, there has been
relatively little work on how detectors should be located. A natural
criterion is to place traps so as to maximize the precision of density
estimators, but the lack of a closed-form expression for precision has
made optimizing this criterion computationally demanding. 2. Recent
results by Efford and Boulanger (2019) show that precision can be well
approximated by a function of the expected number of detected
individuals and expected number of recapture events, both of which can
be evaluated at low computational cost. We use these results to develop
a method for obtaining survey designs that optimize this approximate
precision for SCR studies using count or binary proximity detectors, or
multi-catch traps. 3. We show how the basic design protocol can be
extended to incorporate spatially varying distributions of activity
centres and animal detectability. We illustrate our approach by
simulating from a camera trap study of snow leopards in Mongolia and
comparing estimates from our designs to those generated by regular or
optimized grid designs. Optimizing detector placement increased the
number of detected individuals and recaptures, but this did not always
lead to more precise density estimators due to less precise estimation
of the effective sampling area. In most cases, the precision of density
estimators was comparable to that obtained with grid designs, with
improvement in some scenarios where approximate CV(¬D) < 20% and density
varied spatially. 4. Designs generated using our approach are
transparent and statistically grounded. They can be produced for survey
regions of any shape, adapt to known information about animal density
and detectability, and are potentially easier and less costly to
implement. We recommend their use as good, flexible candidate designs
for SCR surveys when reasonable knowledge of model parameters exists. We
provide software for researchers to construct their own designs, in the
form of updates to design functions in the r package oSCR.

Abstract: The high altitudes of Arunachal Pradesh,India, located in the Eastern Himalaya biodiversity hotspot, remain zoologically unexplored and unprotected. We report results of recent mammal surveys in the high altitude habitats of western Arunachal Pradesh. A total of 35 mammal species (including 12 carnivores, 10 ungulates and 5 primates) were recorded, of which 13 are categorized as Endangered or Vulnerable on the IUCN Red List. One species of primate, the Arunachal macaque Macaca munzala, is new to science and the Chinese goral Nemorhaedus caudatus is a new addition to the ungulate fauna of the Indian subcontinent. We documented peoples' dependence on natural resources for grazing and extraction of timber and medicinal plants. The region's mammals are threatened by widespread hunting. The snow leopard Uncia uncia and dhole Cuon alpinus are also persecuted in retaliation for livestock depredation. The tiger Panthera tigris, earlier reported from the lower valleys, is now apparently extinct there, and range reductions over the last two decades are reported for bharal Pseudois nayaur and musk deer Moschus sp.. Based on mammal species richness, extent of high altitude habitat, and levels of anthropogenic disturbance, we identified a potential site for the creation of Arunachal's first high altitude wildlife reserve (815 km2). Community-based efforts that provide incentives for conservation-friendly practices could work in this area, and conservation awareness programmes are required, not just amongst the local communities and schools but for politicians, bureaucrats and the army.