Abstract: Applied ecology lies at the intersection of human societies and natural systems. Consequently, applied ecologists are constantly challenged as to how best to use ecological knowledge to influence the management of ecosystems (Habel et al. 2013). As Hulme (2011) has pointed out, to do so effectively we must leave our ivory towers and engage with stakeholders. This engagement is especially important and challenging in areas of the world where poverty, weak institutions and poor governance structures conspire to limit the ability of local communities to contribute to biodiversity conservation. These communities often bear disproportionate costs in the form of curtailed access to natural resources, ecosystem services, and developmental
programmes, and also suffer wildlife-caused damage, including injuries or loss of human life, and economic
and psychological impacts (Madhusudan & Mishra 2003). It is well-recognized that conservation efforts in large parts of the world historically have been perceived to be discriminatory by local people (Mishra 2016). The need for engagement with local communities is therefore embedded in the 2020 Aichi biodiversity targets and is widely thought to be critical to the success of conservation efforts. However, although the need for engagement is clear, as ecologists and practitioners we often have little formal training in how we should engage with local communities and how we can recognize the pitfalls and opportunities provided by developing genuine partnerships. The practical challenges of achieving effective engagement are considerable (Agrawal & Gibson 1999; Waylen et al. 2010, 2013), and such forays are fraught with difficulties and ethical considerations (Chan et al. 2007). When they are done badly, conservation interventions
can damage relationships and trust, and lead to serious injustice to local people and setbacks for ecological
outcomes (Duffy 2010). Much has been written on knowledge exchange and participatory research approaches (e.g. Reed et al. 2014 and references therein). This Practitioner’s Perspective
seeks to focus on the next logical step: the elements that practitioners and researchers need to consider when
engaging with communities to effect conservation. Engagement around the management of protected areas
has been discussed and formalized (e.g. Dudley 2008). Considerable literature has also emerged, particularly
from Africa, on the use and co-management of natural resources, commonly referred to as community-based natural resource management or CBNRM (e.g. Fabricius 2004; Roe, Nelson & Sandbrook 2009; Child & Barnes
2010). There have been attempts to draw general principles for CBNRM (e.g. Thakadu 2005; Gruber 2010). In
the related field of community-based conservation, however, while there have been efforts to draw lessons (e.g. Berkes 2004), little exists in terms of frameworks or guidelines for effectively working with local communities to effect biodiversity conservation in multi-use landscapes
(Mishra 2016). The eight principles for community-based conservation outlined here (Fig. 1) build on ideas developed in fields as diverse as applied ecology, conservation and natural
resource management, community health, social psychology, rural development, negotiation theory, and ethics
(see Mishra 2016). They have been developed, challenged and tested through 20 years of community experience andour own research on the endangered snow leopard Panthera uncia and its mountain ecosystems, in South and Central Asia. We suspect that with contextual adaptations, their relevance for applied ecologists and practitioners may be universal.

Abstract: 1. Camera trapping is a widely employed tool in wildlife
research, used to estimate animal abundances, understand animal
movement, assess species richness and under- stand animal behaviour. In
addition to images of wild animals, research cameras often record human
images, inadvertently capturing behaviours ranging from innocuous
actions to potentially serious crimes.
2. With the increasing use of camera traps, there is an urgent need to
reflect on how researchers should deal with human images caught on
cameras. On the one hand, it is important to respect the privacy of
individuals caught on cameras, while, on the other hand, there is a
larger public duty to report illegal activity. This creates ethical
dilemmas for researchers.
3. Here, based on our camera-trap research on snow leopards Panthera
uncia, we outline a general code of conduct to help improve the practice
of camera trap based research and help researchers better navigate the
ethical-legal tightrope of this important research tool.

Abstract: Direct and indirect interactions among predators affect predator fitness, distribution, and overall community structure. Yet, outside of experimental settings, such interactions are difficult to observe and thus poorly understood. Patterns of niche overlap among predators reflect and shape community interactions and may therefore help elucidate the nature and intensity of intraguild interactions. To better understand the coexistence of two apex predators, snow leopards (Panthera uncia) and wolves (Canis lupus), we investigated their spatial, temporal, and dietary niche overlap in summer in the Pamir Mountains of Tajikistan. We estimated population- level space use via spatial capture–recapture models based on noninvasive genetics and camera traps, diel activity patterns based on camera trap detections, and diet composition from prey remains in carnivore scats, from which we estimated coefficients between 0 and 1 for overlap in space, time, and diet, respectively. Snow leopards and wolves displayed moderate spatial partitioning (0.26, 95% confidence interval [CI]: 0.17–37), but overlapping temporal (0.77, 95% CI: 0.64–0.90) and dietary (0.97, 95% CI: 0.80–0.99) niches. Both predators relied on seasonally abundant marmots (Marmota caudata) rather than wild ungulates, their typical primary prey, suggesting that despite patterns of overlap that were superficially conducive to exploitation competition and predator facilitation, prey were likely not a limiting factor. Therefore, prey-mediated interactions, if present, were unlikely to be a major structuring force in the ecosystem. By implication, carnivore conservation planning and monitoring in the mountains of Central Asia should more fully account for the seasonal importance of marmots in the ecosystem.

Abstract: Camera trapping is the most widely used data collection method for estimating snow leopard (Panthera uncia) abundance; however, the accuracy of this method is limited by human observer errors from misclassifying individuals in camera trap images. We evaluated the extent Whiskerbook (www.whiskerbook.org), an artificial intelligence (AI) software, could reduce this error rate and enhance the accuracy of capture-recapture abundance estimates. Using 439 images of 34 captive snow leopard individuals, classification was performed by five observers with prior experience in individual snow leopard ID (“experts”) and five observers with no such experience (“novices”). The “expert” observers classified 35 out of 34 snow leopard individuals, on average erroneously splitting one individual into two, thus resulting in a higher number than true individuals. The success rate of experts was 90 %, with less than a 3 % error in estimating the population size in capture-recapture modeling. However, the “novice” observers successfully matched 71 % of encounters, recognizing 25 out of 34 individuals, underestimating the population by 25 %. It was found that expert observers significantly outperformed novice observers, making statistically fewer errors (Mann Whitney U test P = 0.01) and finding the true number of individuals (P = 0.01). These differences were contrasted with a previous study by Johansson et al. 2020, using the same subset of 16 individuals from European zoos. With the help of AI and the Whiskerbook platform, “experts” were able to match 87 % of encounters and identify 15 out of 16 individuals, with modeled estimates of 16 ± 1 individuals. In contrast, “novices” were 63 % accurate in matching encounters and identified 12 out of 16 individuals, modeling 12 ± 1 individuals that underestimated the population size by 12 %. When comparing the performance of observers using AI and the Whiskerbook platform to observers performing the tasks manually, we found that observers using Whiskerbook made significantly fewer errors in splitting one individual into two (P = 0.04). However, there were also a significantly higher number of combination errors, where two individuals were combined into one (P = 0.01). Specifically, combination errors were found to be made by “novices” (P = 0.04). Although AI benefited both expert and novice observers, expert observers outperformed novices. Our results suggest that AI effectively reduced the misclassification of individual snow leopards in camera trap studies, improving abundance estimates. However, even with AI support, expert observers were needed to obtain the most accurate estimates.

Abstract: 1. Community-based conservation, despite being more inclusive than fortress con- servation, has been criticized for being a top-down implementation of external ideas brought to local communities for conservation's benefit. This is particularly true for Changpas, the pastoral people of Changthang in trans-Himalayan India who live alongside unique wildlife.
2. Our main aim was to co-design conservation interventions through participatory action research. We worked with two Changpa communities, to understand the issues faced by them. Subsequently, we co-designed context-sensitive interventions to facilitate positive human–nature interactions. We did so by integrating the PARTNERS (Presence, Aptness, Respect, Transparency, Empathy, Responsiveness, Strategic Support) principles with the Trinity of Voice (Access, Standing and Influence).
3. In Rupsho, we facilitated focus group discussions (FGDs) led by the community. We found livestock depredation by wildlife was primarily facilitated by the weather. This led to co-designing of a new corral design, which was piloted with seven households, safeguarding 2385 pashmina goats and sheep. Approximating the value of each sheep/goat to be USD125, this intervention amounts to a significant economic protection of USD c. 42,500 for each household. This is along with intangible gains of trust, ownership and improved self-esteem.
4. In Tegazong, a restricted area adjoining the Indo-China border with no previous research records, we worked with 43 Changpa people to co-create research questions of mutual interest. Wildlife presence and reasons for livestock loss were identified as areas of mutual interest. The herders suggested they would record data in a form of their choice, for 6 months, while they live in their winter pastures. This participatory community monitoring revealed nutrition and hypothermia to be a key cause of livestock death. Subsequently, we delimited two previously untested interventions: lamb cribs and provisioning of locally sourced barley as a feed supplement. The wildlife monitoring recorded the first record of Tibetan Gazelle Procapra picticuadata, outside of their known distribution, in Tegazong.
5. We aim to highlight the benefits of co-designing projects with local communities that link research and conservation, while also discussing the challenges faced. Ultimately, such projects are needed to ensure ethical knowledge generation and conservation, which aims to be decolonial and inclusive.