Acoustic behavior, poaching risk, and habitat use in African forest elephants (Loxodonta cyclotis): Insights from passive acoustic monitoring

The African forest elephant (Loxodonta cyclotis) is a critically endangered and cryptic species that inhabits the rainforests of Central Africa. Forest elephant populations are severely threatened by poaching for the ivory trade, and an improved understanding of forest elephant behavior and habitat use, and of the anthropogenic pressures that threaten their existence, is essential for conservation of the species. However, their remote tropical rainforest habitat poses logistical constraints on research and makes forest elephants very difficult to observe and study visually. Limited data collection methods have also inhibited our ability to understand the determinants of poaching activity that is driving forest elephants toward extinction. This dissertation addresses forest elephant behavior, ecology, and conservation questions that span multiple scales by capitalizing on the advantages of passive acoustic monitoring (PAM) to detect elephant vocalizations and gunshots. At the finest scale, Chapter 1 examines forest elephant vocal repertoire use at a forest clearing in the Central African Republic and discusses implications for PAM. The different vocalization types of the repertoire varied in the generality or specificity by which they were used by certain age-sex classes of elephants. An understanding of these patterns is important for PAM of forest elephants, as they determine the population (or subset) that is detected and sampled. At the intermediate scale, Chapter 2 examines forest elephant landscape-scale response to individual poaching events detected in a PAM study system. Elephants within 10 km of gunfire events responded to poacher presence (before gunshots were fired) and to gunshots themselves, exhibiting behavioral changes in either vocal activity, site usage, or both. These results suggest that, in addition to the outright killing of targeted individuals, poaching activity affects the general population of elephants across the landscape. At the broadest scale, Chapters 3 and 4 used detections of elephant vocalizations and gunshots to analyze the distributions of forest elephants and poaching events across a 50-sensor PAM grid spanning 1250 sq. km of rainforest in Republic of Congo, for a period of over 3 years. To elucidate the determinants of these distributions, elephant and gunshot detection data were combined with habitat and landscape variables quantified using satellite remote sensing. In Chapter 3, variation in poaching risk depended primarily on factors related to poacher accessibility, such as distance to major rivers and logging roads. These results can guide the allocation of anti-poaching patrol effort to cover high-risk areas at times of increased vulnerability. Chapter 4 examined the habitat resources and anthropogenic pressures (e.g., poaching and logging) that influence forest elephants’ use of the landscape. Elephant occurrence probabilities decreased over the 3 years of the study and were seasonally dependent, increasing in the wet season. Ongoing logging activity deterred forest elephants from using nearby sites, but previously logged areas provided important habitat resources. By leveraging remote sensing methods to expand the scale and resolution of data collection, this dissertation aimed to advance our understanding of forest elephant behavior and ecology, and confronted questions that will improve conservation efforts to protect the species from extinction

Fine-Scale Tracking of Ambient Temperature and Movement Reveals Shuttling Behavior of Elephants to Water

Movement strategies of animals have been well studied as a function of ecological drivers (e.g., forage selection and avoiding predation) rather than physiological requirements (e.g., thermoregulation). Thermal stress is a major concern for large mammals, especially for savanna elephants (Loxodonta africana), which have amongst the greatest challenge for heat dissipation in hot and arid environments. Therefore, elephants must make decisions about where and how fast to move to reduce thermal stress. We tracked 14 herds of elephant in Kruger National Park (KNP), South Africa, for 2 years, using GPS collars with inbuilt temperature sensors to examine the influence of temperature on movement strategies, particularly when accessing water. We first confirmed that collar-mounted temperature loggers captured hourly variation in relative ambient temperatures across the landscape, and, thus, could be used to predict elephant movement strategies at fine spatio-temporal scales. We found that elephants moved slower in more densely wooded areas, but, unexpectedly, moved faster at higher temperatures, especially in the wet season compared to the dry season. Notably, this speed of movement was highest when elephants were approaching and leaving water sources. Visits to water showed a periodic shuttling pattern, with a peak return rate of 10–30 h, wherein elephants were closest to water during the hotter times of the day, and spent longer at water sources in the dry season compared to the wet season. When elephants left water, they showed low fidelity to the same water source, and traveled farther in the dry season than in the wet season. In KNP, where water is easily accessible, and the risk of poaching is low, we found that elephants use short, high-speed bursts of movement to get to water at hotter times of day. This strategy not only provides the benefit of predation risk avoidance, but also allows them to use water to thermoregulate. We demonstrate that ambient temperature is an important predictor of movement and water use across the landscape, with elephants responding facultatively to a “landscape of thermal stress.

The impacts of bottom-up and top-down drivers in shaping the herbivore community in Pafuri, Kruger National Park, South Africa

Globally, terrestrial mammal populations are facing critical population declines and range contractions owing to habitat fragmentation and destruction, wildlife overexploitation, and climate change driven by expansion of the human population. Mammalian herbivores are integral for maintaining ecosystem structure and functionality. They do this this through herbivory, by acting as prey and cycling soil nutrients. The impacts of herbivores on ecosystems, however, vary with their spatial occupancy which is influenced by interacting bottom-up and top-down factors. Modelling the drivers of herbivore communities is no trivial task given the myriad of potential bottom-up and top-down factors, and the interactions between the two, as well as the species-specific variations in intrinsic functional traits (e.g., foraging strategy, body size, metabolic rate, etc.) influencing herbivore responses (e.g., social structure, space-use, activity patterns, etc.) to these drivers. Consequently, few studies have attempted to model both bottom-up and top-down drivers in structuring herbivore communities, particularly in an African context where predator-prey guilds include multiple species, exposed to high levels of human activity. Therefore, the overarching aim of my research was to quantify the relative effects of both bottom-up and top-down factors driving the herbivore community in the northern Pafuri region of Kruger National Park, South Africa. I utilized a combination of field (i.e., camera trap and vegetation surveys) and analytical (i.e., stable carbon isotopes from faeces and plants) techniques in conjunction with geospatial data to evaluate the impacts of bottom-up (i.e., forage quantity, quality, and water availability) and top-down (i.e., predation and anthropogenic risks) factors on herbivore spatial occupancy and activity patterns. Herbivore responses to bottom-up and top-down factors were species-specific, even among members of the same feeding guild. Specifically, I found that herbivores (varying in body size and foraging strategy) displayed temporal, spatial and in some instances, dietary shifts that reflect species-specific, ecological trade-offs between resource acquisition, and predator and human avoidance. For example, kudu (Tragelaphus strepsiceros) displayed temporal and spatial avoidance of predators and humans, and exhibited previously undocumented levels of seasonal dietary shifts which suggests that the species traded forage acquisition for reduced predation and anthropogenic risks. Further, high levels of human activity appeared to eclipse the risks associated with natural predators resulting in human induced landscapes of fear. For example, warthogs (Phacochoerus africanus) and zebra (Equus quagga) occupied habitats with higher predation risks, but displayed spatial avoidance of nature reserve boundaries which were synonymous with high levels of bushmeat poaching. Most studies focussing on the impacts of bottom-up and top-down drivers neglect to consider the roles that humans play in structuring ecological communities. The results of my thesis, however, emphasize the importance of including anthropogenic drivers when investigating the roles that various bottom-up and top-down factors play in shaping ecological communities.Thesis (PhD) -- Faculty of Science, Zoology and Entomology, 202

Human-Elephant Conflict: A Review of Current Management Strategies and Future Directions

Human-elephant conflict is a major conservation concern in elephant range countries. A variety of management strategies have been developed and are practiced at different scales for preventing and mitigating human-elephant conflict. However, human-elephant conflict remains pervasive as the majority of existing prevention strategies are driven by site-specific factors that only offer short-term solutions, while mitigation strategies frequently transfer conflict risk from one place to another. Here, we review current human-elephant conflict management strategies and describe an interdisciplinary conceptual approach to manage species coexistence over the long-term. Our proposed model identifies shared resource use between humans and elephants at different spatial and temporal scales for development of long-term solutions. The model also highlights the importance of including anthropological and geographical knowledge to find sustainable solutions to managing human-elephant conflict

Reflections from the Workshop on AI-Assisted Decision Making for Conservation

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers.Comment: Co-authored by participants from the October 2022 workshop: https://crcs.seas.harvard.edu/conservation-worksho

UNDERSTANDING STAKEHOLDERS PERCEPTION TOWARDS HUMAN-WILDLIFE INTERACTION AND CONFLICT IN A TIGER LANDSCAPE-COMPLEX OF INDIA

Human-population of the earth exceeding 6 billion and growing at an estimates rate of 1.2% per year (US census Bureau, 2002) will lead to increase in human-wildlife encounters. Attacks on humans are perhaps the least understood of these encounters, but the most interesting and emotionally connected to people (Quigley Howard 2005). The main aim of the study if to understand stakeholders’ perception towards human-wildlife interaction and conflicts in Corbett National park, India. We used a standardized IRB (Institutional Review Board) approved questionnaire to survey 315 household from 15 villages lying within and around Corbett National Park of India using snow-ball technique and stratified random sampling technique.. We also surveyed and analyzed the head of the village, snow-ball technique and stratified random technique survey differently. We used multivariate regression analysis to understand the data obtained from questionnaire survey. Later, we also designed a conceptual model to understand factors influencing human-wildlife interaction; and an empirical model to identify factors affecting human-wildlife conflicts. The results of the study identified that most of the encounters with wildlife occurred while collecting timber or grass from forests. Wild pigs, elephants and cheetal are the species mainly responsible for crop-loss in our study area. Majority of the stakeholders were engaged in timber and grass collection from forested area. Multivariate regression results suggests that stakeholders whose farms were located far from highway, had good fencing and who had better socio-economic status faced least threat from wildlife with respect to crop-loss, livestock loss and human-life loss/injury. The simulation results of dynamic system experiment suggests that habitat loss and poaching play a very significant role in tiger population and its future. The study concludes that a holistic multi-disciplinary conservation approach is needed to address the increasing conflict issues in India. More emphases should be given on community based-conservation strategies and policies. Watch-towers, pits, solar-powered fencing are the best and most effective ways to keep wildlife away from damaging crops and killing livestock. Sustainable development and better higher education is the key to conserving tigers in India

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested