Measuring the intensity of conflicts in conservation

Conflicts between the interests of biodiversity conservation and other human activities pose a major threat to natural ecosystems and human well‐being, yet few methods exist to quantify their intensity and model their dynamics. We develop a categorization of conflict intensity based on the curve of conflict, a model originally used to track the escalation and deescalation of armed conflicts. Our categorization assigns six intensity levels reflecting the discourse and actions of stakeholders involved in a given conflict, from coexistence or collaboration to physical violence. Using a range of case studies, we demonstrate the value of our approach in quantifying conflict trends, estimating transition probabilities between conflict stages, and modeling conflict intensity as a function of relevant covariates. By taking an evidence‐based approach to quantifying stakeholder behavior, the proposed framework allows for a better understanding of the drivers of conservation conflict development across a diverse range of socioecological scenarios

Analysing the use of remote sensing & geospatial technology to combat wildlife crime in East and Southern Africa

This thesis analyses the use of remote sensing technologies in efforts that seek to combat wildlife crime in East and Southern Africa. Companies and organisations working on the development of remote sensing technology used for anti-poaching efforts, in the study region, are identified through the creation of a database. The social impacts and risks involved in using these technologies are then outlined by analysing the responses to a research survey from those in the conservation community working with wildlife crime. The species focus is on rhino and elephant poaching, thus, the thesis begins with a background on the legislation surrounding both the hunting and trade of these species. Stockpiling of rhino horn and elephant tusk will be discussed as well as other anti-poaching strategies that do not use remote sensing technology. Three key research questions are then answered: Which remote sensing technologies are in use and what kinds of companies and organisations are mainly working on their development? What are the main risks of using remote sensing technology to specifically target wildlife crime in this region? And can the increased use of remote sensing technologies to combat wildlife crime be regarded as an extension of the militarised approach to conservation? The last question is discussed in relation to existing research on this topic. Considering the findings from this paper, recommendations for further research are then made.

What spatially explicit quantitative evidence exists that shows the effect of land tenure on illegal hunting of endangered terrestrial mammals in sub-Saharan Africa? A systematic map protocol

Abstract Background Over the last two decades there has been an increase in the demand for land in Sub Saharan Africa, particularly from foreign agribusiness investment to provide food for an increasing human population. The majority of land outside of protected areas in sub-Saharan Africa is under customary tenure. Due to poor land administration in the region, communities living in undocumented land areas tend to be at greater risk of eviction from increasing liberalisation of land markets. To prevent local displacement and disturbance to investment caused by land disputes tenure clarification is growing in importance on national and international agendas. Land conversion can fragment wildlife habitat while reducing the suitable range areas of terrestrial mammal populations on the continent. Simultaneously illegal hunting is on the rise for a wide variety of taxa driven by a demand for food and income from the sale of animal products. To enable a better understanding of how land tenure arrangements impact upon spatial variations in illegal hunting, this protocol sets out the parameters for an evidence map which will collate and analyse the spatially explicit quantitative evidence that exists showing the effect of land tenure on illegal hunting of endangered terrestrial mammals in sub-Saharan Africa. Sub-Saharan Africa is the region of focus as it contains the highest number of terrestrial mammals listed as vulnerable, endangered or critically endangered by the International Union for Conservation of Nature. Taking stock of what methods have been used to gather data and where evidence exists can guide future research in this area while informing conservation interventions. Methods This evidence map will compare: (1) data availability on the spatial distribution of illicit hunting of endangered terrestrial mammals across different land tenure regimes in sub-Saharan Africa; (2) research methodologies that have primarily been used to collect quantitative data on illegal hunting and comparability of existing data; (3) preferences in the research body toward particular taxa and geographical areas, (4) the evidence map will provide an analysis on the influence other environmental and anthropogenic determinants that influence the spatial distribution of illicit hunting incidences, e.g., proximity to roads, water bodies, range patrol bases etc. Eight academic databases and numerous organisation repositories will be searched for relevant studies by three authors. Double screening will be carried out on all articles to locate studies that meet the specified inclusion criteria, for inclusion studies must contain spatially explicit quantitative data on illegal hunting of endangered terrestrial mammals as defined by the International Union for the Conservation of Nature. Relevant information from studies will be extracted to a custom-made extraction form. The resulting map will consist of a narrative synthesis, descriptive statistics and a heat map in the form of a matrix. By providing an overview of the evidence base the resulting map can inform future meta-analyses by showing where there is sufficient comparable data while guiding conservation interventions by indicating geographical areas where species are most at risk

The spatial distribution of illegal hunting of terrestrial mammals in Sub-Saharan Africa: a systematic map

Background: There is a rich body of literature addressing the topic of illegal hunting of wild terrestrial mammals. Studies on this topic have risen over the last decade as species are under increasing risk from anthropogenic threats. Sub-Saharan Africa contains the highest number of terrestrial mammals listed as vulnerable, endangered or critically endangered. However, the spatial distribution of illegal hunting incidences is not well documented. To address this knowledge gap, the systematic map presented here aims to answer three research questions: (1) What data are available on the spatial distribution of illegal hunting of terrestrial mammals in Sub-Saharan Africa in relation to environmental and anthropogenic correlates i.e. proximity to roads, water bodies, human settlement areas, different land tenure arrangements and anti-poaching ranger patrol bases? (2) Which research methodologies have primarily been used to collect quantitative data and how comparable are these data? (3) Is there a bias in the research body toward particular taxa and geographical areas? Methods: Systematic searches were carried out across eight bibliographic databases; articles were screened against pre-defined criteria. Only wild terrestrial mammals listed as vulnerable, endangered or critically endangered by the International Union for Conservation of Nature (IUCN) whose geographical range falls in Sub-Saharan Africa and whose threat assessment includes hunting and trapping were included. To meet our criteria, studies were required to include quantitative, spatially explicit data. In total 14,325 articles were screened at the level of title and abstract and 206 articles were screened at full text. Forty-seven of these articles met the pre-defined inclusion criteria. Results: Spatially explicit data on illegal hunting are available for 29 species in 19 of the 46 countries that constitute Sub-Saharan Africa. Data collection methods include GPS and radio tracking, bushmeat household and market surveys, data from anti-poaching patrols, hunting follows and first-hand monitoring of poaching signs via line transects, audio and aerial surveys. Most studies have been conducted in a single protected area exploring spatial patterns in illegal hunting with respect to the surrounding land. Several spatial biases were detected. Conclusions: There is a considerable lack of systematically collected quantitative data showing the distribution of illegal hunting incidences and few comparative studies between different tenure areas. The majority of studies have been conducted in a single protected area looking at hunting on a gradient to surrounding village land. From the studies included in the map it is evident there are spatial patterns regarding environmental and anthropogenic correlates. For example, hunting increases in proximity to transport networks (roads and railway lines), to water sources, to the border of protected areas and to village land. The influence of these spatial features could be further investigated through meta-analysis. There is a diverse range of methods in use to collect data on illicit hunting mainly drawing on pre-existing law enforcement data or researcher led surveys detecting signs of poaching. There are few longitudinal studies with most studies representing just one season of data collection and there is a geographical research bias toward Tanzania and a lack of studies in Central Africa

Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Satellites allow large-scale surveys to be conducted in short time periods with repeat surveys possible at intervals of <24 h. Very-high-resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very-high-resolution satellite imagery and deep learning. In this study, we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView-3 and 4 satellite data –the highest resolution satellite imagery commercially available. We train and test the model on 11 images from 2014 to 2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye-1) captured in Kenya, without any additional training data, to test if the algorithm can generalize to an elephant population outside of the training area. Our results show that the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalize to detect elephants in a different geographical location and from a lower resolution satellite. Our study demonstrates the feasibility of applying state-of-the-art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customized training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with accuracy as high as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalizability of the technique

A satellite perspective on the movement decisions of African elephants in relation to nomadic pastoralists

The African savannah ecosystem is populated by nomadic pastoralists who herd livestock in the day and corral them at night in temporary enclosures, called bomas, to protect them. The number and distribution of bomas on the savannah is important from an ecological perspective and may have a significant impact on wildlife movement. However, no study has yet examined this relationship. Here, using very high-resolution satellite imagery from two time periods, we quanitified changes in boma distribution and density across an area of 3377 km2 in the Laikipia-Samburu ecosystem of northern Kenya between 2011 and 2019. To assess wildlife movement in relation to bomas, we used a GPS data set on African bush elephant Loxodonta africana movement from 27 collared matriarchs representing herds of 9–15, covering 112 467 hourly GPS fixes over 31 months between 2018 and 2020. Our results showed a more than 46% increase in the total number of human-built structures between 2011 and 2019, the majority of which were bomas, representing a 21.9% increase in human-modified land area. Elephants readily adjusted their foraging habits and itineraries in this habitat shared with humans, who were also nomadic in space and time. Assessing the night–day activity ratio, we found elephants move more nocturnally when in closer proximity to bomas, particularly during the dry season. This temporal separation means elephants avoid the times humans are active in and around bomas while still accessing required resources—water and forage. The temporal shift was stronger during the dry season when shared resources are scarce. Using daily travel distance as a metric, we show elephants moved further in closer proximity to bomas which was likely linked to the need to travel between forage patches. Given the rise in human settlements, understanding the consequences of animals' behavioral adjustments is critical to understand the long-term population viability of elephant populations

Determination of optimal flight altitude to minimise acoustic drone disturbance to wildlife using species audiograms

Unmanned aerial vehicles (UAVs) are increasingly important in wildlife data collection but concern over wildlife disturbance has led several countries to ban their use in National Parks. Disturbance is an animal welfare concern and impedes scientific data collection through provoking aberrant behaviour. Dealing with the issue of disturbance will enable wildlife researchers to use UAV technology more effectively and ethically. Here we present a novel method to determine optimal flight altitude for minimising drone disturbance for wildlife using species audiograms. We recorded sound profiles of seven common UAV systems in the horizontal and vertical planes at 5-m increments up to 120 m. To understand how mammals perceive UAV sound, we used audiograms of 20 species to calculate the loudness of each UAV for each species across the measured distances. These calculations filter the UAV noise based on the sensitivity of species’ hearing over the relevant frequency spectrum. We have devised a method to optimise the trade-off between image spatial resolution and flight altitude. We calculated the lowest point at which either the UAV sound level decreases below an acceptable threshold, here chosen as 40 dB, weighted according to species’ hearing sensitivity, or disturbance cannot be significantly further minimised by flying higher. The latter is quantified as the point above which each additional 5 m of flight altitude causes on average less than 0.05 dB decrease in sound pressure level. Reliable data on appropriate flight altitudes can guide policy regulations on flying UAVs over wildlife, thus enabling increased use of this technology for scientific data collection and for wildlife conservation purposes. The methodology is readily applicable to other species and UAV systems for which sound recordings and audiograms are available

Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology