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Abstract Historical persecution of grizzly bears in North America reduced the species range by 55%. Today, dedicated recovery efforts and shifting societal perceptions have supported the recovery and expansion of grizzly bear populations in many areas. With increasing overlap between people and bears, conservation actions and scientific inquiry are now shifting efforts toward supporting coexistence with bears. Here, we assessed the demography and behavior of grizzly bears in a coexistence landscape in southeast British Columbia, Canada, where abundant grizzly bear populations occur among busy, human‐settled valleys. Between 2016 and 2022, we captured 76 individual grizzly bears and monitored their conflict behavior, survival, and reproduction for 160 animal‐years. The cause of death for 14 animals with a functioning collar was human–wildlife conflict (n = 6), road or rail collision (n = 6), unknown but human suspected (n = 1), and natural (n = 1). Subadult survival was the lowest recorded in North America, while adult survival was similar to other studies, suggesting an intense demographic filter for young animals. We estimate that human‐caused mortality is underreported in government databases by 65%, or for every recorded mortality, there are ~2 that go unreported. Reporting was especially low for road and rail mortalities. Grizzly bear mortality in the Elk Valley due to collisions and conflicts with people is an order of magnitude greater than elsewhere in British Columbia. Combining DNA‐ and collar‐based estimates of population growth, we show that grizzly bear abundance is stable due to source‐sink dynamics, whereby ~7 immigrant bears per year offset the high mortality rates in the area. Grizzly bears dispersing into the valley are often young and more conflict‐naïve, creating a conflict spiral that can be interrupted by reducing mortality of young animals. Creating a self‐sustaining population of bears in the Elk Valley that is not reliant on immigration will require targeted efforts to reduce or secure attractants on private property and strategies to minimize collisions with trains and vehicles.

Populations of bears in Asia are vulnerable to extinction and effective monitoring is critical to measure and direct conservation efforts. Population abundance (local density) or growth (λ) are the most sensitive metrics to change. We discuss the value in implementing spatially explicit capture-recapture (SCR), the current gold standard for density estimation, and open population SCR (OPSCR) to monitor changes in density over time. We provide guidance for designing studies to provide estimates with sufficient power to detect changes. Because of the wide availability of camera traps and interest in their use, we consider six density estimation methods and their extensions developed for use with camera traps, with specific consideration of assumptions and applications for monitoring Asian bears. We conducted a power analysis to calculate the precision in estimates needed to detect changes in populations with reference to IUCN Red List criteria. We performed a systematic review of empirical studies implementing camera trap abundance estimation methods and considered sample sizes, effort, and model assumptions required to achieve adequate precision for population monitoring. We found SCR and OPSCR, reliant on “marked” individuals, are currently the only methods with enough power to reliably detect even moderate to major (20–80%) declines. Camera trap methods with unmarked individuals rarely achieved precision sufficient to detect even large declines (80–90%), although with some exceptions (e.g., situations with moderate population densities, large number of sampling sites, or inclusion of ancillary local telemetry data. We describe additional estimation options including line transects, direct observations, monitoring age-specific survival and reproductive rates, and hybrid/integrated methodologies that may have potential to work for some Asian bear populations. We conclude monitoring changes in abundance or density is possible for most Asian bear populations but will require collaboration among researchers over broad spatial extents and extensive financial investment to overcome biological and logistical constraints. We strongly encourage practitioners to consider study design and sampling effort required to meet objectives by conducting simulations, power analyses, and assumption checks prior to implementing monitoring efforts, and reporting standardized dispersion measures such as coefficients of variation to allow for assessment of precision. Our guidance is relevant to other low-density and wide-ranging species.

Changes in the distribution of a species can be used to as a metric of conservation status and to identify the loss or gain of isolated populations. This mapping process is a primary tool of the IUCN SSC Red List assessment. Most distribution maps are based on expert opinion or species distribution models based on a combination of species detection records and known habitat and landscape affiliations for that species. However, modeling the distribution for some species is difficult due to low levels of sampling, low detectability, and loose or uncertain associations with landscape attributes. These issues may be particularly prevalent for Asian bears that have generalist habitat requirements and are heavily poached across their range. Presently the range of all bear species in Asia is delineated primarily using expert opinion. Members of the IUCN SSC Bear Specialist Group attempted to create a rule set to combine current detections with known habitat and home range preferences to improve distribution maps for the purpose of conservation metrics and monitoring. The results of applying this rule set for giant pandas (Ailuropoda melanoleuca) closely matched the current IUCN mapped range, but the results for Asiatic black bears (Ursus thibetanus) did not corroborate the IUCN map based solely on expert opinion. We present a list of recommended actions for improving distribution mapping for bears in Asia and possibly any species whose current distribution is heavily influenced by poorly measured metrics such as poaching.