Occupancy modelling : study design and models for data collected along transects

Occupancy, defined as the proportion of sites occupied by a species, is a state variable of interest in ecology and conservation. When modelling species occupancy it is crucial to account for the detection process, as most species can remain undetected at sites where present. This is usually achieved by carrying out separate repeat visits to each sampling site but other methods are sometimes used, such as surveying spatial sub-units within each sampling site, or even collecting detection data continuously along a transect, during a single visit. This thesis deals with two aspects of occupancy modelling: (i) we explore issues related to the design of occupancy studies, including the trade-off in survey effort allocation between sites and repeat visits, sample size determination and the impact of sampling with replacement in studies based on spatial replication; and (ii) we develop and evaluate new models to estimate occupancy from species detection data collected along transects, motivated by the analysis of a data set from a Surnatran tiger Panthera tigris sumatrae survey which followed this type of sampling protocol. The models we propose, which describe the detection process as a continuous point process, can account for clustering and/or abundance-induced heterogeneity in the detection process and represent a step forward with respect to current modelling approaches which involve data discretisation and two-stage ad hoc procedures

Valid auto-models for spatially autocorrelated occupancy and abundance data

Auto-logistic and related auto-models, implemented approximately as autocovariate regression, provide simple and direct modelling of spatial dependence. The autologistic model has been widely applied in ecology since Augustin, Mugglestone and Buckland (J. Appl. Ecol., 1996, 33, 339) analysed red deer census data using a hybrid estimation approach, combining maximum pseudo-likelihood estimation with Gibbs sampling of missing data. However Dormann (Ecol. Model., 2007, 207, 234) questioned the validity of auto-logistic regression, giving examples of apparent underestimation of covariate parameters in analysis of simulated "snouter" data. Dormann et al. (Ecography, 2007, 30, 609) extended this analysis to auto-Poisson and auto-normal models, reporting similar anomalies. All the above studies employ neighbourhood weighting schemes inconsistent with conditions (Besag, J. R. Stat. Soc., Ser. B, 1974, 36, 192) required for auto-model validity; furthermore the auto-Poisson analysis fails to exclude cooperative interactions. We show that all "snouter" anomalies are resolved by correct auto-model implementation. Re-analysis of the red deer data shows that invalid neighbourhood weightings generate only small estimation errors for the full dataset, but larger errors occur on geographic subsamples. A substantial fraction of papers applying auto-logistic regression to ecological data use these invalid weightings, which are default options in the widely used "spdep" spatial dependence package for R. Auto-logistic analyses using invalid neighbourhood weightings will be erroneous to an extent that can vary widely. These analyses can easily be corrected by using valid neighbourhood weightings available in "spdep". The hybrid estimation approach for missing data is readily adapted for valid neighbourhood weighting schemes and is implemented here in R for application to sparse presence-absence data.Comment: Typos corrected in Table 1. Note that defaults in R package 'spdep' have changed in response to this paper; some results using defaults are therefore now version-dependen

Cost-efficient effort allocation for camera-trap occupancy surveys of mammals

Camera-traps are increasingly used to survey threatened mammal species and are an important tool for estimating habitat occupancy. To date, cost-efficient occupancy survey effort allocation studies have focused on trade-offs between number of sample units (SUs) and sampling occasions, with simplistic accounts of associated costs which do not reflect camera-trap survey realities. Here we examine camera-trap survey costs as a function of the number of SUs, survey duration and camera-traps per SU, linking costs to precision in occupancy estimation. We evaluate survey effort trade-offs for hypothetical species representing different levels of occupancy (?) and detection (p) probability to identify optimal design strategies. We apply our cost function to three threatened species as worked examples. Additionally, we use an extensive camera-trap data set to evaluate independence between multiple camera traps per SU. The optimal number of sampling occasions that result in minimum cost decrease as detection probability increases, irrespective of whether the species is rare (? 0.5). The most expensive survey scenarios occur for elusive (p 10 km2), where the survey is conducted on foot. Minimum survey costs for elusive species can be achieved with fewer sampling occasions and multiple cameras per SU. Multiple camera-traps set within a single SU can yield independent species detections. We provide managers and researchers with guidance for conducting cost-efficient camera-trap occupancy surveys. Efficient use of survey budgets will ultimately contribute to the conservation of threatened and data deficient mammals

Optical Absorption by Indirect Excitons in a Transition Metal Dichalcogenide Double Layer

We calculate the binding energy, transition energies, oscillator strength, and absorption coefficient of indirect excitons in transition metal dichalcogenide (TMDC) double layers separated by an integer number of hexagonal boron nitride (h-BN) monolayers. The absorption factor, a dimensionless quantity which gives the fraction of incoming photons absorbed by the indirect excitons in the double layer, is evaluated. The aforementioned optical quantities are obtained for transitions from the ground state to the first two excited states. All quantities are studied as a function of the interlayer separation, which may be experimentally controlled by varying the number of h-BN monolayers between the TMDC layers. Calculations are performed by using the exciton wave function and eigenenergies obtained for the Keldysh potential. For each material, we choose a combination of the exciton reduced mass and the dielectric screening length from the existing literature which give the largest and the smallest indirect exciton binding energy. These combinations of material parameters provide upper and lower bounds on all quantities presented. Our findings can be examined experimentally via two-photon spectroscopy.Comment: 13 pages, 3 figure

Monitoring and conservation of the critically endangered Alaotran gentle lemur Hapalemur alaotrensis

The Alaotran gentle lemur Hapalemur alaotrensis is a Critically Endangered lemur, which exclusively inhabits the marshes around Lac Alaotra in northeast Madagascar. In the past decades the population of H. alaotrensis has experienced a dramatic decline due to poaching, habitat destruction and degradation. Surveys have been carried out periodically to follow the status of the population. Here we present the results of a survey carried out between May and June 2008 in the southwestern part of the marshes around Alaotra and discuss the key findings derived from the analysis of the data collected. Our study indicates that the probability of detecting the species in an area where it is present is very low and depends on factors that vary in space and time. These results stress the need to account for imperfect detection when monitoring this species, an issue especially relevant when reporting population trends. Our analyses also show that habitat fragmentation is a key determinant of habitat suitability for H. alaotrensis, with fragmented areas of marsh showing low suitability. Finally, our observations and analysis suggest that the protection provided by the local community to H. alaotrensis in Andreba is contributing to the conservation of this Critically Endangered species. This highlights the need to continue working on engaging the local communities in the conservation of the marshes at Lac Alaotra as a critical element to secure the future of H. alaotrensis

High carbon stock forests provide co-benefits for tropical biodiversity

1. Carbon-based policies provide powerful opportunities to unite tropical forest conservation with climate change mitigation. However, their effectiveness in delivering biodiversity co-benefits is dependent on high levels of biodiversity being found in high carbon areas. Previous studies have focussed solely on the co-benefits associated with Reducing Emissions from Deforestation and forest Degradation (REDD+) over large spatial scales, with few empirically testing carbon-biodiversity correlations at management unit scales appropriate to decision-makers. Yet, in development frontiers, where most biodiversity and carbon loss occurs, carbon-based policies are increasingly driven by commodity certification schemes, which are applied at the concession-level. 2. Working in a typical human-modified landscape in Southeast Asia, we examined the biodiversity value of land prioritised via application of REDD+ or the High Carbon Stock (HCS) Approach, the emerging land-use planning tool for oil palm certification. Carbon stocks were estimated via low- and high-resolution datasets derived from global or local level biomass. Mammalian species richness was predicted using hierarchical Bayesian multi-species occupancy models of camera-trap data from forest and oil palm habitats. 3. At the community level, HCS forest supported comparable mammal diversity to control sites in continuous forest, while lower carbon strata exhibited reduced species occupancy. 4. No association was found between species richness and carbon when the latter was estimated using coarse-resolution data. However, when using high-resolution, field validated biomass data, diversity demonstrated positive relationships with carbon for threatened and disturbance-sensitive species, suggesting sensitivity of co-benefits to carbon data sources and the species considered. 5. Policy implications. Our work confirms the potential for environmental certification and REDD+ to work in tandem with conservation to mitigate agricultural impacts on tropical forest carbon stocks and biodiversity, especially if this directs development to low carbon, low biodiversity areas

Maximizing the value of forest restoration for tropical mammals by detecting three-dimensional habitat associations

Tropical forest ecosystems are facing unprecedented levels of degradation, severely compromising habitat suitability for wildlife. Despite the fundamental role biodiversity plays in forest regeneration, identifying and prioritising degraded forests for restoration or conservation, based on their wildlife value, remains a significant challenge. Efforts to characterize habitat selection are also weakened by simple classifications of human-modified tropical forests as intact versus degraded, which ignore the influence that three-dimensional forest structure may have on species distributions. Here, we develop a framework to identify conservation and restoration opportunities across logged forests in Borneo. We couple high-resolution airborne Light Detection and Ranging (LiDAR) and camera trap data to characterize the response of a tropical mammal community to changes in three-dimensional forest structure across a degradation gradient. Mammals were most responsive to covariates that accounted explicitly for the vertical and horizontal characteristics of the forest, and actively selected structurally-complex environments comprising tall canopies, increased plant area index throughout the vertical column, and the availability of a greater diversity of niches. We show that mammals are sensitive to structural simplification through disturbance, emphasising the importance of maintaining and enhancing structurally-intact forests. By calculating occurrence thresholds of species in response to forest structural change, we identify areas of degraded forest that would provide maximum benefit for multiple high conservation value species if restored. The study demonstrates the advantages of using LiDAR to map forest structure, rather than relying on overly simplistic classifications of human-modified tropical forests, for prioritising regions for restoration

Implications of zero-deforestation commitments: forest quality and hunting pressure limit mammal persistence in fragmented tropical landscapes

Zero-deforestation commitments seek to decouple agricultural production and forest loss to improve prospects for biodiversity. However, the effectiveness of methods designed to meet these commitments is poorly understood. In a highly-fragmented tropical landscape dominated by oil palm, we tested the capacity for the High Carbon Stock (HCS) Approach to prioritise forest remnants that sustain mammal diversity. Patches afforded High Priority by HCS protocols (100 ha core area) provided important refuges for IUCN-threatened species and megafauna. However, patch-scale HCS area recommendations conserved only 35% of the mammal community. At least 3,000 ha would be required to retain intact mammal assemblages, with nearly ten times this area needed if hunting pressure was high. While current HCS protocols will safeguard patches capable of sustaining biodiversity, highly-fragmented tropical landscapes typical of zero-deforestation pledges will require thinking beyond the patch, towards strategically configured forest remnants at the landscape-level and enforcing strict controls on hunting

Data integration for large-scale models of species distributions

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species’ potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise