Camera trap distance sampling for terrestrial mammal population monitoring: lessons learnt from a UK case study

Accurate and precise density estimates are crucial for effective species management and conservation. However, efficient monitoring of mammal densities over large spatial and temporal scales is challenging. In the United Kingdom, published density estimates for many mammals, including species considered to be common, are imprecise. Camera trap distance sampling (CTDS) can estimate densities of multiple species at a time and has been used successfully in a small number of studies. However, CTDS has typically been used over relatively homogeneous landscapes, often over large time scales, making monitoring changes (by repeating surveys) difficult. In this study, we deployed camera traps at 109 sites across an area of 2725 km2 of varied habitat in North-East England, United Kingdom. The 4-month survey generated 51 447 photos of wild mammal species. Data were sufficient for us to use CTDS to estimate the densities of eight mammal species across the whole-survey area and within four specific habitats. Both survey-wide and habitat-specific density estimates largely fell within previously published density ranges and our estimates were amongst the most precise produced for these species to date. Lower precision for some species was typically due to animals being missed by the camera at certain distances, highlighting the need for careful consideration of practical and methodological decisions, such as how high to set cameras and where to left-truncate data. Although CTDS is a promising methodology for determining densities of multiple species from one survey, species-specific decisions are still required and these cannot always be generalized across species types and locations. Taking the United Kingdom as a case study, our study highlights the potential for CTDS to be used on a national scale, although the scale of the task suggests that it would need to be integrated with a citizen science approach

A place-based approach to payments for ecosystem services

Payment for Ecosystem Services (PES) schemes are proliferating but are challenged by insufficient attention to spatial and temporal inter-dependencies, interactions between different ecosystems and their services, and the need for multi-level governance. To address these challenges, this paper develops a place-based approach to the development and implementation of PES schemes that incorporates multi-level governance, bundling or layering of services across multiple scales, and shared values for ecosystem services. The approach is evaluated and illustrated using case study research to develop an explicitly place-based PES scheme, the Peatland Code, owned and managed by the International Union for the Conservation of Nature’s UK Peatland Programme and designed to pay for restoration of peatland habitats. Buyers preferred bundled schemes with premium pricing of a primary service, contrasting with sellers’ preferences for quantifying and marketing services separately in a layered scheme. There was limited awareness among key business sectors of dependencies on ecosystem services, or the risks and opportunities arising from their management. Companies with financial links to peatlands or a strong environmental sustainability focus were interested in the scheme, particularly in relation to climate regulation, water quality, biodiversity and flood risk mitigation benefits. Visitors were most interested in donating to projects that benefited wildlife and were willing to donate around £2 on-site during a visit. Sellers agreed a deliberated fair price per tonne of CO2 equivalent from £11.18 to £15.65 across four sites in Scotland, with this range primarily driven by spatial variation in habitat degradation. In the Peak District, perceived declines in sheep and grouse productivity arising from ditch blocking led to substantially higher prices, but in other regions ditch blocking was viewed more positively. The Peatland Code was developed in close collaboration with stakeholders at catchment, landscape and national scales, enabling multi-level governance of the management and delivery of ecosystem services across these scales. Place-based PES schemes can mitigate negative trade-offs between ecosystem services, more effectively include cultural ecosystem services and engage with and empower diverse stakeholders in scheme design and governance

Habitat requirements of breeding golden plover Pluvialis apricaria

SIGLEAvailable from British Library Document Supply Centre-DSC:DX192959 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework

Behavioural ecologists often study complex systems in which multiple hypotheses could be proposed to explain observed phenomena. For some systems, simple controlled experiments can be employed to reveal part of the complexity; often, however, observational studies that incorporate a multitude of causal factors may be the only (or preferred) avenue of study. We assess the value of recently advocated approaches to inference in both contexts. Specifically, we examine the use of information theoretic (IT) model selection using Akaike’s information criterion (AIC). We find that, for simple analyses, the advantages of switching to an IT-AIC approach are likely to be slight, especially given recent emphasis on biological rather than statistical significance. By contrast, the model selection approach embodied by IT approaches offers significant advantages when applied to problems of more complex causality. Model averaging is an intuitively appealing extension to model selection. However, we were unable to demonstrate consistent improvements in prediction accuracy when using model averaging with IT-AIC; our equivocal results suggest that more research is needed on its utility. We illustrate our arguments with worked examples from behavioural experiments

Farmer attitudes to cross-holding agri-environment schemes and their implications for Countryside Stewardship

A literature review and on-line consultation (of 122 respondents from across the UK) revealed farmers’ perspectives of cross-holding agri-environment schemes (AES). The main concerns raised included; a culture of independent working, lack of existing farmer networks, the validity of farmer-farmer contracts, inadequate financial compensation, the need for third party support, farmers’ lack of knowledge of the environmental benefits of AES, and the scheme’s ‘‘small print’’. The consultation added the following concerns; the need to offer ‘‘collaborative’’ and ‘‘coordinated’’ environmental management options, the belief that neighbours would not make willing or suitable collaborators, and possible facilitation of the spread of pest and diseases, including non-native invasive species. It uses these research findings to identify which of these concerns have been taken into account in the design of Countryside Stewardship (CS) the recently introduce replacement in England of the Environmental Stewardship Scheme. Suggested changes that may increase CS’s effectiveness in enhancing ecological networks include; provision of up-front financial support to farmer-group applications, allowing existing AES agreements to end before their due dates, and removing restrictions on the use of the Capital Grants element. Offering additional resourcebased incentives to farmer-group applicants, such as reducing the area of land entered into ‘‘greening’’, can be justified if the expected environmental benefits from cross-holding collective action do materialise

Time in captivity, individual differences and foraging behaviour in wild-caught chaffinches

Wild-caught animals are often given a settling in period before experimental trials are initiated. We used wild-caught chaffinches (Fringilla coelebs) to investigate (a) the effect of settling in period duration on the likelihood that chaffinches foraged during experimental trials and (b) whether settling in period duration influenced measures of foraging and vigilance behaviour recorded from those experiments. The probability of collecting foraging data from an individual's first trial fell below 50% if it had been in captivity for more than 12 days prior to that trial, whereas the probability was >75% if trials were completed within two days of capture. The successful collection of foraging data from subsequent trials was also dependent on the number of days an individual spent in captivity prior to its first trial and on whether that individual foraged in its first trial, suggesting that some individuals were more inclined to forage in captivity. Individuals that foraged in their first trial had a 94% higher success rate in subsequent trials than those that did not. However, settling in period duration did not significantly influence the peck rate, mean search period or mean vigilance period of individuals that did forage. Our results show that allowing a settling in period actually reduced the likelihood of collecting foraging data from chaffinches and that commencing experiments shortly after capture increased data collection efficiency. We discuss the possibility that the inability to collect data from certain birds following a settling in period could lead to potentially important biases in results, particularly if propensity to forage is linked to an individual's coping strategy or personality. We conclude that it may not always be beneficial to allow wild-caught animals to habituate to captivity before commencing experiments. In some cases, testing animals soon after capture may increase the likelihood of data collection, reducing both the number of study animals required and the length of time they spend in captivity

Economical crowdsourcing for camera trap image classification

Camera trapping is widely used to monitor mammalian wildlife but creates large image datasets that must be classified. In response, there is a trend towards crowdsourcing image classification. For high‐profile studies of charismatic faunas, many classifications can be obtained per image, enabling consensus assessments of the image contents. For more local‐scale or less charismatic communities, however, demand may outstrip the supply of crowdsourced classifications. Here, we consider MammalWeb, a local‐scale project in North East England, which involves citizen scientists in both the capture and classification of sequences of camera trap images. We show that, for our global pool of image sequences, the probability of correct classification exceeds 99% with about nine concordant crowdsourced classifications per sequence. However, there is high variation among species. For highly recognizable species, species‐specific consensus algorithms could be even more efficient; for difficult to spot or easily confused taxa, expert classifications might be preferable. We show that two types of incorrect classifications – misidentification of species and overlooking the presence of animals – have different impacts on the confidence of consensus classifications, depending on the true species pictured. Our results have implications for data capture and classification in increasingly numerous, local‐scale citizen science projects. The species‐specific nature of our findings suggests that the performance of crowdsourcing projects is likely to be highly sensitive to the local fauna and context. The generality of consensus algorithms will, thus, be an important consideration for ecologists interested in harnessing the power of the crowd to assist with camera trapping studies