Understanding Sustainability through Traditional Maasai Pastoral Systems in Southern Kenya

In the developed world, we tend to think of sustainability as a newly articulated solution to challenges of environmental resource degradation and issues of social and economic injustice. However, pastoralism, as traditionally practiced by the Maasai of southern Kenya, is intimately tied to the land and responds to climatic variation of the region. As a result of a shift toward privatization of land tenure, traditional pastoral systems are no longer viable. While studying abroad with The School For Field Studies, we became aware of the struggle many Maasai face as they attempt to continue pastoralism in an increasingly hostile environment. Ultimately, development efforts in the region should focus not on implementing exogenous concepts of ‘sustainability’ but rather on supporting and adapting systems that are already in place

Insights and approaches using deep learning to classify wildlife.

The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the methods can be abstruse and the results mystifying. Here, in the context of applying cutting edge methods to classify wildlife species from camera-trap data, we shed light on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications. The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-learning algorithms. We outline these methods and present results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We demonstrate the application of a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that in some cases are similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species. We then interpret the features in the image where the strongest responses occur, and present dataset biases that were revealed by these extracted features. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated the relative unfamiliarity of images that were not part of the training set when these images were one of the 20 species "known" to our CNN in contrast to images of the species that were "unknown" to our CNN

An applied ecology of fear framework: linking theory to conservation practice

Research on the ecology of fear has highlighted the importance of perceived risk from predators and humans in shaping animal behavior and physiology, with potential demographic and ecosystem-wide consequences. Despite recent conceptual advances and potential management implications of the ecology of fear, theory and conservation practices have rarely been linked. Many challenges in animal conservation may be alleviated by actively harnessing or compensating for risk perception and risk avoidance behavior in wild animal populations. Integration of the ecology of fear into conservation and management practice can contribute to the recovery of threatened populations, human–wildlife conflict mitigation, invasive species management, maintenance of sustainable harvest and species reintroduction plans. Here, we present an applied framework that links conservation interventions to desired outcomes by manipulating ecology of fear dynamics. We discuss how to reduce or amplify fear in wild animals by manipulating habitat structure, sensory stimuli, animal experience (previous exposure to risk) and food safety trade-offs to achieve management objectives. Changing the optimal decision-making of individuals in managed populations can then further conservation goals by shaping the spatiotemporal distribution of animals, changing predation rates and altering risk effects that scale up to demographic consequences. We also outline future directions for applied research on fear ecology that will better inform conservation practices. Our framework can help scientists and practitioners anticipate and mitigate unintended consequences of management decisions, and highlight new levers for multi-species conservation strategies that promote human–wildlife coexistence

Why don't we share data and code? Perceived barriers and benefits to public archiving practices

The biological sciences community is increasingly recognizing the value ofopen, reproducible and transparent research practices for science and societyat large. Despite this recognition, many researchers fail to share their dataand code publicly. This pattern may arise from knowledge barriers abouthow to archive data and code, concerns about its reuse, and misalignedcareer incentives. Here, we define, categorize and discuss barriers to dataand code sharing that are relevant to many research fields. We explorehow real and perceived barriers might be overcome or reframed in thelight of the benefits relative to costs. By elucidating these barriers and thecontexts in which they arise, we can take steps to mitigate them and alignour actions with the goals of open science, both as individual scientistsand as a scientific community

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Mammal responses to global changes in human activity vary by trophic group and landscape

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe

Spatial and Temporal Responses of Animals to Landscape Heterogeneity, Predation Risk, and Human Activity

The global expansion of human activity has had profound consequences for wildlife. Research has documented the effects of widespread habitat destruction and defaunation on species and ecosystems, but the more subtle pathways through which humans alter the natural world have largely escaped quantification. Much like apex predators, humans can instill strong fear in wild animals, which may adjust their activity to avoid contact with humans. In this dissertation, my collaborators and I examine pathways through which human disturbance, predation risk, and environmental heterogeneity influence animal behavior and distribution. We review the literature and synthesize theory to develop a novel framework for studying landscapes of fear, and we apply this framework in a global meta-analysis and field studies from California and Mozambique to understand how large mammals perceive and respond to spatial and temporal patterns of risk from humans and carnivores. We consider links between risk and response in complex systems with multiple predators or multiple prey species, and we explore ecology of fear dynamics in the context of seasonality, human disturbance, and restoration. Together, this work integrates disciplines of behavioral ecology, community ecology, and landscape ecology, applying predator-prey theory to understand the role of humans in ecological communities. By elucidating behavioral pathways linking human disturbance to wildlife community dynamics, this research contributes to our understanding of wildlife ecology in human-dominated landscapes and highlights mechanisms for human-wildlife coexistence

Understanding Sustainability through Traditional Maasai Pastoral Systems in Southern Kenya

In the developed world, we tend to think of sustainability as a newly articulated solution to challenges of environmental resource degradation and issues of social and economic injustice. However, pastoralism, as traditionally practiced by the Maasai of southern Kenya, is intimately tied to the land and responds to climatic variation of the region. As a result of a shift toward privatization of land tenure, traditional pastoral systems are no longer viable. While studying abroad with The School For Field Studies, we became aware of the struggle many Maasai face as they attempt to continue pastoralism in an increasingly hostile environment. Ultimately, development efforts in the region should focus not on implementing exogenous concepts of ‘sustainability’ but rather on supporting and adapting systems that are already in place