Investigating the impacts of humans and dogs on the spatial and temporal activity of wildlife in urban woodlands

Humans can derive enormous benefit from the natural environment and the wildlife they see there, but increasing human use of natural environments may negatively impact wildlife, particularly in urban green spaces. Few studies have focused on the trade-offs between intensive human use and wildlife use of shared green spaces in urban areas. In this paper, we investigate the impacts of humans and their dogs on wildlife within an urban green space using camera trap data from Hampstead Heath, London. Spatial and temporal activity of common woodland bird and mammal species were compared between sites with low and high frequency of visits by humans and dogs. There was no significant difference in the spatial or temporal activity of wildlife species between sites with lower and higher visitation rates of humans and dogs, except with European hedgehogs (Erinaceus europaeus) which showed extended activity in the mornings and early evenings in sites with lower visitation rates. This may have implications for the survival and reproductive success of European hedgehogs. Our results suggest that adaptation to human and dog activity deserves greater study in urban green spaces, as would a broader approach to measuring possible anthropogenic effects

Monitoring wild animal communities with arrays of motion sensitive camera traps

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans

Using water-landing, fixed-wing UAVs and computer vision to assess seabird nutrient subsidy effects on sharks and rays

Bird colonies on islands sustain elevated productivity and biomass on adjacent reefs, through nutrient subsidies. However, the implications of this localized enhancement on higher and often more mobile trophic levels (such as sharks and rays) are unclear, as spatial trends in mobile fauna are often poorly captured by traditional underwater visual surveys. Here, we explore whether the presence of seabird colonies is associated with enhanced abundances of sharks and rays on adjacent coral reefs. We used a novel long-range water-landing fixed-wing unoccupied aerial vehicle (UAV) to survey the distribution and density of sharks, rays and any additional megafauna, on and around tropical coral islands (n = 14) in the Chagos Archipelago Marine Protected Area. We developed a computer-vision algorithm to distinguish greenery (trees and shrubs), sand and sea glitter from visible ocean to yield accurate marine megafauna density estimation. We detected elevated seabird densities over rat-free islands, with the commonest species, sooty tern, reaching densities of 932 ± 199 per km−2 while none were observed over former coconut plantation islands. Elasmobranch density around rat-free islands with seabird colonies was 6.7 times higher than around islands without seabird colonies (1.3 ± 0.63 vs. 0.2 ± SE 0.1 per km2). Our results are evidence that shark and ray distribution is sensitive to natural and localized nutrient subsidies. Correcting for non-sampled regions of images increased estimated elasmobranch density by 14%, and our openly accessible computer vision algorithm makes this correction easy to implement to generate shark and ray and other wildlife densities from any aerial imagery. The water-landing fixed-wing long-range UAV technology used in this study may provide cost effective monitoring opportunities in remote ocean locations

Camera traps enable the estimation of herbaceous aboveground net primary production (ANPP) in an African savanna at high temporal resolution

Determining the drivers of aboveground net primary production (ANPP), a key ecosystem process, is an important goal of ecosystem ecology. However, accurate estimation of ANPP across larger areas remains challenging, especially for savanna ecosystems that are characterized by large spatiotemporal heterogeneity in ANPP. Satellite remote sensing methods are frequently used to estimate productivity at the landscape scale but generally lack the spatial and temporal resolution to capture the determinants of productivity variation. Here, we developed and tested methods for estimating herbaceous productivity as an alternative to labour-intensive repeated biomass clipping and caging of small plots. We compared measures of three spectral greenness indices, normalized difference vegetation index derived from Sentinel-2 (NDVIs) and a handheld radiometer (NDVIg), and green chromatic coordinate derived from digital repeat cameras (GCC) and tested their relationship to biweekly field-measured herbaceous ANPP using movable exclosures. We found that a satellite-based model including average NDVIs and its rate of change (ΔNDVIs) over the biweekly productivity measurement interval predicted herbaceous ANPP reasonably well (Jackknife R2 = 0.26). However, the predictive accuracy doubled (Jackknife R2 = 0.52) when including the sum of day to day increases in camera trap-derived vegetation greenness (tGCC). This result can be considered promising, given the current lack of productivity estimation methods at comparable spatiotemporal resolution. We furthermore found that the fine (daily) temporal resolution of GCC time series captured fast vegetation responses to rainfall events that were missed when using a coarser temporal resolution (>2 days). These findings demonstrate the importance of measuring at a fine temporal resolution for predicting herbaceous ANPP in savanna ecosystems. We conclude that camera traps are promising in offering a reliable and cost-effective method to estimate productivity in savannas and contribute to a better understanding of ecosystem functioning and its drivers

How do foragers decide when to leave a patch? A test of alternative models under natural and experimental conditions.

This is the peer reviewed version of the article which has been published in final form at DOI: http://dx.doi.org/10.1111/1365-2656.12089. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.A forager's optimal patch-departure time can be predicted by the prescient marginal value theorem (pMVT), which assumes they have perfect knowledge of the environment, or by approaches such as Bayesian updating and learning rules, which avoid this assumption by allowing foragers to use recent experiences to inform their decisions. In understanding and predicting broader scale ecological patterns, individual-level mechanisms, such as patch-departure decisions, need to be fully elucidated. Unfortunately, there are few empirical studies that compare the performance of patch-departure models that assume perfect knowledge with those that do not, resulting in a limited understanding of how foragers decide when to leave a patch. We tested the patch-departure rules predicted by fixed rule, pMVT, Bayesian updating and learning models against one another, using patch residency times (PRTs) recorded from 54 chacma baboons (Papio ursinus) across two groups in natural (n = 6175 patch visits) and field experimental (n = 8569) conditions. We found greater support in the experiment for the model based on Bayesian updating rules, but greater support for the model based on the pMVT in natural foraging conditions. This suggests that foragers may place more importance on recent experiences in predictable environments, like our experiment, where these experiences provide more reliable information about future opportunities. Furthermore, the effect of a single recent foraging experience on PRTs was uniformly weak across both conditions. This suggests that foragers' perception of their environment may incorporate many previous experiences, thus approximating the perfect knowledge assumed by the pMVT. Foragers may, therefore, optimize their patch-departure decisions in line with the pMVT through the adoption of rules similar to those predicted by Bayesian updating.Natural Environment Research Council (NERC)Fenner School of Environment and SocietyLeakey FoundationAnimal Behavior Society (USA)International Primatological SocietyExplorers Club Exploration Fun

The emergence of leaders and followers in foraging pairs when the qualities of individuals differ.

BACKGROUND: Foraging in groups offers animals a number of advantages, such as increasing their likelihood of finding food or detecting and avoiding predators. In order for a group to remain together, there has to be some degree of coordination of behaviour and movement between its members (which may in some cases be initiated by a decision-making leader, and in other cases may emerge as an underlying property of the group). For example, behavioural synchronisation is a phenomenon where animals within a group initiate and then continue to conduct identical behaviours, and has been characterised for a wide range of species. We examine how a pair of animals should behave using a state-dependent approach, and ask what conditions are likely to lead to behavioural synchronisation occurring, and whether one of the individuals is more likely to act as a leader. RESULTS: The model we describe considers how the energetic gain, metabolic requirements and predation risks faced by the individuals affect measures of their energetic state and behaviour (such as the degree of behavioural synchronisation seen within the pair, and the value to an individual of knowing the energetic state of its colleague). We explore how predictable changes in these measures are in response to changes in physiological requirements and predation risk. We also consider how these measures should change when the members of the pair are not identical in their metabolic requirements or their susceptibility to predation. We find that many of the changes seen in these measures are complex, especially when asymmetries exist between the members of the pair. CONCLUSION: Analyses are presented that demonstrate that, although these general patterns are robust, care needs to be taken when considering the effects of individual differences, as the relationship between individual differences and the resulting qualitative changes in behaviour may be complex. We discuss how these results are related to experimental observations, and how the model and its predictions could be extended.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Data-driven counterfactual evaluation of management outcomes to improve emergency conservation decisions

Monitoring is needed to assess conservation success and improve management, but naïve or simplistic interpretation of monitoring data can lead to poor decisions. We illustrate how to counter this risk by combining decision-support tools and quantitative counterfactual analysis. We analyzed 20 years of egg rescue for tara iti (Sternula nereis davisae) in Aotearoa New Zealand. Survival is lower for rescued eggs; however, only eggs perceived as imminently threatened by predators or weather are rescued, so concluding that rescue is ineffective would be biased. Equally, simply assuming all rescued eggswould have died if left in situ is likely to be simplistic. Instead, we used the monitoring data itself to estimate statistical support for a wide space of uncertain counterfactuals about decisions and fate of rescued eggs. Results suggest under past management, rescuing and leaving eggs would have led to approximately the same overall fledging rate, because of likely imperfect threat assessment and low survival of rescued eggs to fledging. Managers are currently working to improve both parameters. Our approach avoids both naïve interpretation of observed outcomes and simplistic assumptions thatmanagement is always justified, using the same data to obtain unbiased quantitative estimates of counterfactual support

Sherlock - A flexible, low-resource tool for processing camera-trapping images

1. The use of camera traps to study wildlife has increased markedly in the last two decades. Camera surveys typically produce large data sets which require processing to isolate images containing the species of interest. This is time consuming and costly, particularly if there are many empty images that can result from false triggers. Computer vision technology can assist with data processing, but existing artificial intelligence algorithms are limited by the requirement of a training data set, which itself can be challenging to acquire. Furthermore, deep-learning methods often require powerful hardware and proficient coding skills. 2. We present Sherlock, a novel algorithm that can reduce the time required to process camera trap data by removing a large number of unwanted images. The code is adaptable, simple to use and requires minimal processing power.3. We tested Sherlock on 240,596 camera trap images collected from 46 cameras placed in a range of habitats on farms in Cornwall, United Kingdom, and set the parameters to find European badgers (Meles meles). The algorithm correctly classified 91.9% of badger images and removed 49.3% of the unwanted ‘empty’ images. When testing model parameters, we found that faster processing times were achieved by reducing both the number of sampled pixels and ‘bouncing’ attempts (the number of paths explored to identify a disturbance), with minimal implications for model sensitivity and specificity. When Sherlock was tested on two sites which contained no livestock in their images, its performance greatly improved and it removed 92.3% of the empty images. 4. Although further refinements may improve its performance, Sherlock is currently an accessible, simple and useful tool for processing camera trap data

Land-use change alters the mechanisms assembling rainforest mammal communities in Borneo

1. The assembly of species communities at local scales is thought to be driven by environmental filtering, species interactions and spatial processes such as dispersal limitation. Little is known about how the relative balance of these drivers of community assembly changes along environmental gradients, especially manmade environmental gradients associated with land-use change. 2. Using concurrent camera- and live-trapping, we investigated the local-scale assembly of mammal communities along a gradient of land-use intensity (old-growth forest, logged forest and oil palm plantations) in Borneo. We hypothesised that increasing land-use intensity would lead to an increasing dominance of environmental control over spatial processes in community assembly. Additionally, we hypothesised that competitive interactions among species might reduce in concert with declines in α-diversity (previously documented) along the land-use gradient. 3. To test our first hypothesis, we partitioned community variance into the fractions explained by environmental and spatial variables. To test our second hypothesis, we used probabilistic models of expected species co-occurrence patterns, in particular focussing on the prevalence of spatial avoidance between species. Spatial avoidance might indicate competition, but might also be due to divergent habitat preferences. 4. We found patterns that are consistent with a shift in the fundamental mechanics governing local community assembly. In support of our first hypothesis, the importance of spatial processes (dispersal limitation and fine-scale patterns of home-ranging) appeared to decrease from low to high intensity land-uses, whilst environmental control increased in importance (in particular due to fine-scale habitat structure). Support for our second hypothesis was weak: whilst we found that the prevalence of spatial avoidance decreased along the land-use gradient, in particular between congeneric species pairs most likely to be in competition, few instances of spatial avoidance were detected in any land-use, and most were likely due to divergent habitat preferences. 5. The widespread changes in land-use occurring in the tropics might be altering not just the biodiversity found in landscapes, but also the fundamental mechanics governing the local assembly of communities. A better understanding of these mechanics, for a range of taxa, could underpin more effective conservation and management of threatened tropical landscapes