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

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

Bringing It All Together: Multi-species Integrated Population Modelling of a Breeding Community

Integrated population models (IPMs) combine data on different aspects of demography with time-series of population abundance. IPMs are becoming increasingly popular in the study of wildlife populations, but their application has largely been restricted to the analysis of single species. However, species exist within communities: sympatric species are exposed to the same abiotic environment, which may generate synchrony in the fluctuations of their demographic parameters over time. Given that in many environments conditions are changing rapidly, assessing whether species show similar demographic and population responses is fundamental to quantifying interspecific differences in environmental sensitivity and highlighting ecological interactions at risk of disruption. In this paper, we combine statistical approaches to study populations, integrating data along two different dimensions: across species (using a recently proposed framework to quantify multi-species synchrony in demography) and within each species (using IPMs with demographic and abundance data).We analyse data from three seabird species breeding at a nationally important long-term monitoring site. We combine demographic datasets with island-wide population counts to construct the first multi-species Integrated Population Model to consider synchrony. Our extension of the IPM concept allows the simultaneous estimation of demographic parameters, adult abundance and multi-species synchrony in survival and productivity, within a robust statistical framework. The approach is readily applicable to other taxa and habitats

A standard protocol for reporting species distribution models

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready-to-use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically-based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta-analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web-based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community

Population Status of a Cryptic Top Predator: An Island-Wide Assessment of Tigers in Sumatran Rainforests

Large carnivores living in tropical rainforests are under immense pressure from the rapid conversion of their habitat. In response, millions of dollars are spent on conserving these species. However, the cost-effectiveness of such investments is poorly understood and this is largely because the requisite population estimates are difficult to achieve at appropriate spatial scales for these secretive species. Here, we apply a robust detection/non-detection sampling technique to produce the first reliable population metric (occupancy) for a critically endangered large carnivore; the Sumatran tiger (Panthera tigris sumatrae). From 2007–2009, seven landscapes were surveyed through 13,511 km of transects in 394 grid cells (17×17 km). Tiger sign was detected in 206 cells, producing a naive estimate of 0.52. However, after controlling for an unequal detection probability (where p = 0.13±0.017; ±S.E.), the estimated tiger occupancy was 0.72±0.048. Whilst the Sumatra-wide survey results gives cause for optimism, a significant negative correlation between occupancy and recent deforestation was found. For example, the Northern Riau landscape had an average deforestation rate of 9.8%/yr and by far the lowest occupancy (0.33±0.055). Our results highlight the key tiger areas in need of protection and have led to one area (Leuser-Ulu Masen) being upgraded as a ‘global priority’ for wild tiger conservation. However, Sumatra has one of the highest global deforestation rates and the two largest tiger landscapes identified in this study will become highly fragmented if their respective proposed roads networks are approved. Thus, it is vital that the Indonesian government tackles these threats, e.g. through improved land-use planning, if it is to succeed in meeting its ambitious National Tiger Recovery Plan targets of doubling the number of Sumatran tigers by 2022

Model averaging in ecology: a review of Bayesian, information-theoretic and tactical approaches for predictive inference

In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions

From planning to implementation: explaining connections between adaptive management and population models

The management of natural systems often involves periodic interventions that must be decided without a complete understanding of how the system responds to our actions. It is in this situation of recurrent decision-making under uncertainty that adaptive management (AM) has been repeatedly advocated, with each decision round providing an opportunity to improve our knowledge in order to facilitate future decisions: the ‘learning while managing’ tenet of AM. When the subject of management is a wildlife population (that is harvested, is a pest or is threatened with extinction), population models will be at the core of the AM process. We provide an overview of the steps in AM, from the set-up to the iterative phase, highlighting the central role that population models can play at different stages of the process of planning and implementing an AM program, as well as when analyzing the value of acquiring new information. We discuss the contexts in which these models have been applied in natural resource management and biodiversity conservation. We aim to bring this applied discipline to the attention of researchers interested in population dynamics, while stressing the relevance of these models for managers considering an AM approach

Cost-effectiveness of thermal imaging for monitoring a cryptic arboreal mammal

Context: The development of reliable and cost-efficient survey techniques is key to the monitoring of all wildlife. One group of species that presents particular challenges for monitoring is the arboreal mammals. Traditional techniques for detecting these species often yield low detection probabilities (detectability) and are time-consuming, suggesting the potential for novel methods to enhance our understanding of their distribution, abundance and population trajectories. One technique that has been shown to increase detectability in a range of terrestrial species is thermal imaging, although it has rarely been applied to arboreal species. The true conservation status of Lumholtz’s tree-kangaroo (Dendrolagus lumholtzi) is uncertain because of low detectability under typical survey techniques, and a more suitable method is required to enable effective monitoring of the species, making it an ideal candidate for the present study. Aims: We aimed to compare the success and cost-effectiveness of surveys utilising thermal imaging with two traditional methods, namely, spotlighting and daytime surveys, so as to optimise monitoring of D. lumholtzi. Methods: We conducted surveys at 10 sites in Queensland (Australia) where D. lumholtzi was known to occur, by using each method, and modelled both the detectability of D. lumholtzi and the cost-effectiveness of each technique. Key results: Detectability of D. lumholtzi was significantly higher with the use of thermal imaging than it was with the other survey methods, and thermal detection is more cost-effective. In average survey conditions with a trained observer, the single-visit estimated detectability of D. lumholtzi was 0.28 [0.04, 0.79] in a transect through rainforest, by using thermal imaging. Using only spotlights, the detection probability was 0.03 [0, 0.28] under the same conditions. Conclusions: These results show that incorporating thermal technology into monitoring surveys will greatly increase detection probability for D. lumholtzi, a cryptic arboreal mammal. Implications: Our study highlighted the potential utility of thermal detection in monitoring difficult-to-detect species in complex habitats, including species that exist mainly in dense forest canopy

Optimally designing drone‐based surveys for wildlife abundance estimation with N‐mixture models

Abstract Hierarchical N‐mixture models have been suggested for abundance estimation from spatiotemporally replicated drone‐based count surveys, since they allow modeling abundance of unmarked individuals while accounting for detection errors. However, it is still necessary to understand how these models perform in the wide variety of contexts and species in which drone surveys are being used. This knowledge is fundamental to plan study designs with optimal allocation of scarce resources in ecology and conservation. We conduct a simulation study to address N‐mixture model (binomial and multinomial) performance and optimal survey effort allocation in different scenarios of local abundance and detectability of individuals, focusing on their application for drone‐based surveys. We also investigate the benefits of using a double‐observer protocol (either human or algorithm) in image review to decompose the detection process in availability and perception. Finally, we illustrate our simulation‐based survey design considerations by applying them to abundance estimation of marsh deer in the Pantanal wetland (Brazil). Accuracy of abundance estimation with N‐mixture models increases with local abundance in sites and especially with the availability of individuals. The optimal design requires more visits at fewer sites when the availability probability is lower, and the optimal design is more flexible as local abundance increases. Two observers checking images can increase the estimator performance even at very high perception probabilities. We quantified how much the use of a double‐observer protocol in image review can reduce fieldwork effort while achieving the same accuracy. N‐mixture models can deliver accurate abundance estimates from spatiotemporally replicated drone surveys in a wide variety of contexts while accounting for imperfect detection. The improvements achieved by a consciously planned design, rearranging survey efforts among sites and visits, as well as using a second observer in image review, can be crucial to detect trends when monitoring a population or to categorize a species as threatened or not