Social science is becoming increasingly important in conservation, with more studies involving methodologies that collect data from and about people. Conservation science is a normative and applied discipline designed to support and inform management and practice. Poor research practice risks harming participants and, researchers, and can leave negative legacies. Often, those at the forefront of field-based research are early-career researchers, many of whom enter their first research experience ill-prepared for the ethical conundrums they may face. We draw on our own experiences as early-career researchers to illuminate how ethical challenges arise during conservation research that involves human participants. Specifically, we considered ethical review procedures, conflicts of values, and power relations, and devised broad recommendations on how to navigate ethical challenges when they arise during research. In particular, we recommend researchers apply reflexivity (i.e., thinking that allows researchers to recognize the effect researchers have on the research) to help navigate ethical challenges and encourage greater engagement with ethical review processes and the development of ethical guidelines for conservation research that involves human participants. Such guidelines must be accompanied by the integration of rigorous ethical training into conservation education. We believe our experiences are not uncommon and can be avoided and hope to spark discussion to contribute to a more socially just conservation.
1. Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time-consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human.2. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case-study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n = 227 camera stations, n = 23,868 images), and outperforms humans in several respects (e.g. detecting 'invisible' animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patternsThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Summary The COVID-19 pandemic has brought humanity’s strained relationship with nature into sharp focus, with calls for cessation of wild meat trade and consumption, to protect public health and biodiversity. 1 , 2 However, the importance of wild meat for human nutrition, and its tele-couplings to other food production systems, mean that the complete removal of wild meat from diets and markets would represent a shock to global food systems. 3 , 4 , 5 , 6 The negative consequences of this shock deserve consideration in policy responses to COVID-19. We demonstrate that the sudden policy-induced loss of wild meat from food systems could have negative consequences for people and nature. Loss of wild meat from diets could lead to food insecurity, due to reduced protein and nutrition, and/or drive land-use change to replace lost nutrients with animal agriculture, which could increase biodiversity loss and emerging infectious disease risk. We estimate the magnitude of these consequences for 83 countries, and qualitatively explore how prohibitions might play out in 10 case study places. Results indicate that risks are greatest for food-insecure developing nations, where feasible, sustainable, and socially desirable wild meat alternatives are limited. Some developed nations would also face shocks, and while high-capacity food systems could more easily adapt, certain places and people would be disproportionately impacted. We urge decision-makers to consider potential unintended consequences of policy-induced shocks amidst COVID-19; and take holistic approach to wildlife trade interventions, which acknowledge the interconnectivity of global food systems and nature, and include safeguards for vulnerable people.
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