Targeted management buffers negative impacts of climate change on the hihi, a threatened New Zealand passerine

In order to buffer the risks climate change poses to biodiversity, managers need to develop new strategies to cope with an increasingly dynamic environment. Supplementary Feeding (SF) is a commonly-used form of conservation management that may help buffer the impacts of climate change. However, the role of SF as an adaptation tool is yet to be fully understood. Here we used the program MARK to quantify the relationship between weather (average temperature and total precipitation) and vital rates (survival and recruitment) of an island bird population, the hihi Notiomystis cincta, for which long term demographic data are available under periods of little and ad libitum SF. We then used predictive population modelling to project this population’s dynamics under each management strategy and several climate change scenarios in accordance with the Intergovernmental Panel on Climate Change predictions. Our stochastic population projections revealed that ad libitum SF likely buffer the population against heavier rainfall and more stochastic precipitation patterns; no buffering effect on temperature was detected. While the current SF approach is unlikely to prevent local extinction of the population under increasing temperatures, SF still presents itself as a valuable climate change adaptation tool by delaying extinction. To the best of our knowledge, this is the first study to quantify the interaction between climate and SF intensity of a threatened population. We call for on-going critical evaluation of management measures, and suggest that novel adaptation solutions that combine current approaches are required for conserving species with limited opportunity for dispersal

Assessing the camera trap methodologies used to estimate density of unmarked populations

1. Population density estimations are essential for wildlife management and conservation. Camera traps have become a promising cost-effective tool, for which several methods have been described to estimate population density when individuals are unrecognizable (i.e. unmarked populations). However, comparative tests of their applicability and performance are scarce. 2. Here, we have compared three methods based on camera traps to estimate population density without individual recognition: Random Encounter Model (REM), Random Encounter and Staying Time (REST) and Distance Sampling with camera traps (CT-DS). Comparisons were carried out in terms of consistency with one another, precision and cost-effectiveness. We considered six natural populations with a wide range of densities, and three species with different behavioural traits (red deer Cervus elaphus, wild boar Sus scrofa and red fox Vulpes vulpes). In three of these populations, we obtained independent density estimates as a reference. 3. The densities estimated ranged from 0.23 individuals/km2 (fox) to 34.87 individuals/km2 (red deer). We did not find significant differences in terms of density values estimated by the three methods in five out of six populations, but REM has a tendency to generate higher average density values than REST and CT-DS. Regarding the independents’ densities, REM results were not significantly different in any population, and REST and CT-DS were significantly different in one population. The precision obtained was not significantly different between methods, with average coefficients of variation of 0.28 (REST), 0.36 (REM) and 0.42 (CT-DS). The REST method required the lowest human effort. 4. Synthesis and applications. Our results show that all of the methods examined can work well, with each having particular strengths and weaknesses. Broadly, Random Encounter and Staying Time (REST) could be recommended in scenarios of high abundance, Distance Sampling with camera traps (CT-DS) in those of low abundance while Random Encounter Model (REM) can be recommended when camera trap performance is not optimal, as it can be applied with less risk of bias. This broadens the applicability of camera trapping for estimating densities of unmarked populations using information exclusively obtained from camera traps. This strengthens the case for scientifically based camera trapping as a cost-effective method to provide reference estimates for wildlife managers, including within multi-species monitoring programmes

Remote sensing and the UN Ocean Decade: high expectations, big opportunities

This year officially marks the beginning of the United Nations Decade of Ocean Science for Sustainable Development (2021–2030)—the Ocean Decade. A primary objective of this coordination framework is to support scientific research and technological developments that can contribute to the conservation and sustainable management of the world’s oceans. One of the seven Decade Outcomes is to secure healthy and resilient oceans where marine biodiversity is mapped and protected; however, fulfilling this goal will require data, knowledge, and technology. The use of remote sensing is now established in marine research and management and is crucial in developing our understanding of ocean patterns and processes at multiple spatial and temporal scales (e.g., Jawak et al., 2015). As such, remote sensing technology is expected to play a critical role in achieving the vision set by the Ocean Decade

Density responses of lesser-studied carnivores to habitat and management strategies in southern Tanzania's Ruaha-Rungwa landscape.

Compared to emblematic large carnivores, most species of the order Carnivora receive little conservation attention despite increasing anthropogenic pressure and poor understanding of their status across much of their range. We employed systematic camera trapping and spatially explicit capture-recapture modelling to estimate variation in population density of serval, striped hyaena and aardwolf across the mixed-use Ruaha-Rungwa landscape in southern Tanzania. We selected three sites representative of different habitat types, management strategies, and levels of anthropogenic pressure: Ruaha National Park’s core tourist area, dominated by Acacia-Commiphora bushlands and thickets; the Park’s miombo woodland; and the neighbouring community-run MBOMIPA Wildlife Management Area, also covered in Acacia-Commiphora. The Park’s miombo woodlands supported a higher serval density (5.56 [Standard Error = ±2.45] individuals per 100 km2) than either the core tourist area (3.45 [±1.04] individuals per 100 km2) or the Wildlife Management Area (2.08 [±0.74] individuals per 100 km2). Taken together, precipitation, the abundance of apex predators, and the level of anthropogenic pressure likely drive such variation. Striped hyaena were detected only in the Wildlife Management Area and at low density (1.36 [±0.50] individuals per 100 km2), potentially due to the location of the surveyed sites at the edge of the species’ global range, high densities of sympatric competitors, and anthropogenic edge effects. Finally, aardwolf were captured in both the Park’s core tourist area and the Wildlife Management Area, with a higher density in the Wildlife Management Area (13.25 [±2.48] versus 9.19 [±1.66] individuals per 100 km2), possibly as a result of lower intraguild predation and late fire outbreaks in the area surveyed. By shedding light on three understudied African carnivore species, this study highlights the importance of miombo woodland conservation and community-managed conservation, as well as the value of by-catch camera trap data to improve ecological knowledge of lesser-studied carnivores

Reframing the concept of alternative livelihoods

Alternative livelihood project (ALP) is a widely used term for interventions that aim to reduce the prevalence of activities deemed to be environmentally damaging by substituting them with lower impact livelihood activities that provide at least equivalent benefits. ALPs are widely implemented in conservation, but in 2012, an International Union for Conservation of Nature resolution called for a critical review of such projects based on concern that their effectiveness was unproven. We focused on the conceptual design of ALPs by considering their underlying assumptions. We placed ALPs within a broad category of livelihood-focused interventions to better understand their role in conservation and their intended impacts. We dissected 3 flawed assumptions about ALPs based on the notions of substitution, the homogenous community, and impact scalability. Interventions based on flawed assumptions about people's needs, aspirations, and the factors that influence livelihood choice are unlikely to achieve conservation objectives. We therefore recommend use of a sustainable livelihoods approach to understand the role and function of environmentally damaging behaviors within livelihood strategies; differentiate between households in a community that have the greatest environmental impact and those most vulnerable to resource access restrictions to improve intervention targeting; and learn more about the social–ecological system within which household livelihood strategies are embedded. Rather than using livelihood-focused interventions as a direct behavior-change tool, it may be more appropriate to focus on either enhancing the existing livelihood strategies of those most vulnerable to conservation-imposed resource access restrictions or on use of livelihood-focused interventions that establish a clear link to conservation as a means of building good community relations. However, we recommend that the term ALP be replaced by the broader term livelihood-focused intervention. This avoids the implicit assumption that alternatives can fully substitute for natural resource-based livelihood activities

Does polymorphism make Asiatic golden cat the most adaptable predator in Eastern Himalayas?

Some wild felines have a diverse range of coat colors while others do not. Jaguars and leopards, for instance, come in spotted and melanistic forms but tigers are always striped and lions always beige. Smaller cats like clouded leopards, marbled cats, and ocelots are almost always patterned in the same way while jaguarundis, oncillas, and golden cats occur in several different colors and patterns. This article is protected by copyright. All rights reserved

A generalised random encounter model for estimating animal density with remote sensor data

Summary: Wildlife monitoring technology is advancing rapidly and the use of remote sensors such as camera traps and acoustic detectors is becoming common in both the terrestrial and marine environments. Current methods to estimate abundance or density require individual recognition of animals or knowing the distance of the animal from the sensor, which is often difficult. A method without these requirements, the random encounter model (REM), has been successfully applied to estimate animal densities from count data generated from camera traps. However, count data from acoustic detectors do not fit the assumptions of the REM due to the directionality of animal signals. We developed a generalised REM (gREM), to estimate absolute animal density from count data from both camera traps and acoustic detectors. We derived the gREM for different combinations of sensor detection widths and animal signal widths (a measure of directionality). We tested the accuracy and precision of this model using simulations of different combinations of sensor detection widths and animal signal widths, number of captures and models of animal movement. We find that the gREM produces accurate estimates of absolute animal density for all combinations of sensor detection widths and animal signal widths. However, larger sensor detection and animal signal widths were found to be more precise. While the model is accurate for all capture efforts tested, the precision of the estimate increases with the number of captures. We found no effect of different animal movement models on the accuracy and precision of the gREM. We conclude that the gREM provides an effective method to estimate absolute animal densities from remote sensor count data over a range of sensor and animal signal widths. The gREM is applicable for count data obtained in both marine and terrestrial environments, visually or acoustically (e.g. big cats, sharks, birds, echolocating bats and cetaceans). As sensors such as camera traps and acoustic detectors become more ubiquitous, the gREM will be increasingly useful for monitoring unmarked animal populations across broad spatial, temporal and taxonomic scales

Can CNN-based species classification generalise across variation in habitat within a camera trap survey?

Camera trap surveys are a popular ecological monitoring tool that produce vast numbers of images making their annotation extremely time-consuming. Advances in machine learning, in the form of convolutional neural networks, have demonstrated potential for automated image classification, reducing processing time. These networks often have a poor ability to generalise, however, which could impact assessments of species in habitats undergoing change. Here, we (i) compare the performance of three network architectures in identifying species in camera trap images taken from tropical forest of varying disturbance intensities; (ii) explore the impacts of training dataset configuration; (iii) use habitat disturbance categories to investigate network generalisability and (iv) test whether classification performance and generalisability improve when using images cropped to bounding boxes. Overall accuracy (72.8%) was improved by excluding the rarest species and by adding extra training images (76.3% and 82.8%, respectively). Generalisability to new camera locations within a disturbance level was poor (mean F1-score: 0.32). Performance across unseen habitat disturbance levels was worse (mean F1-score: 0.27). Training the network on multiple disturbance levels improved generalisability (mean F1-score on unseen disturbance levels: 0.41). Cropping images to bounding boxes improved overall performance (F1-score: 0.77 vs. 0.47) and generalisability (mean F1-score on unseen disturbance levels: 0.73), but at a cost of losing images that contained animals which the detector failed to detect. These results suggest researchers should consider using an object detector before passing images to a classifier, and an improvement in classification might be seen if labelled images from other studies are added to their training data. Composition of training data was shown to be influential, but including rarer classes did not compromise performance on common classes, providing support for the inclusion of rare species to inform conservation efforts. These findings have important implications for use of these methods for long-term monitoring of habitats undergoing change, as they highlight the potential for misclassifications due to poor generalisability to impact subsequent ecological analyses. These methods therefore need to be considered as dynamic, in that changes to the study site would need to be reflected in the updated training of the network

WiseEye: next generation expandable and programmable camera trap platform for wildlife research

Funding: The work was supported by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1. The work of S. Newey and RJI was part funded by the Scottish Government's Rural and Environment Science and Analytical Services (RESAS). Details published as an Open Source Toolkit, PLOS Journals at: http://dx.doi.org/10.1371/journal.pone.0169758Peer reviewedPublisher PD