Evaluating survey methods for bat roost detection in ecological impact assessment

International audienceThe disturbance, damage and destruction of roosts are key drivers of bat population declines worldwide. In countries where bats are protected by law, bat roost surveys are often required to inform ecological impact assessments. Yet, evidence-based information on survey methodology to detect bat roosts is crucially lacking, and failing to detect a roost can lead to serious errors during decision-making processes. Here, we assess the efficacy of bat roost surveys in buildings as implemented in the UK. These consist of a daytime inspection of buildings, followed by a series of acoustic surveys at dusk/dawn if during the daytime inspection evidence of bats is found, or if the absence of bats cannot be verified. We reviewed 155 ecological consultants’ reports to (1) compare survey outcome between daytime inspection and acoustic surveys and (2) determine the minimum sampling effort required during acoustic surveys to be confident that no bats are roosting within a building. We focused on two genera of bats most frequently found in buildings in Europe – Pipistrellus (crevice roosting species with high-intensity echolocation calls that can be easily detected by ultrasound detectors) and Plecotus (species that roost in open spaces and which emit faint echolocation calls that are difficult to detect). Daytime inspections were efficient in detecting open-roosting species such as Plecotus species but were likely to miss the presence of crevice-dwelling ones (here Pipistrellus species) which may lead to erroneous conclusions if no acoustic surveys are subsequently prescribed to confirm their absence. A minimum of three and four acoustic surveys are required to be 95% confident that a building does not host a roost of Pipistrellus species and Plecotus species, respectively, thus exceeding current recommendations. Overall, we demonstrated that reports submitted as part of an ecological impact assessment provide suitable data to test and improve survey methods

Accounting for spatial autocorrelation and environment are important to derive robust bat population trends from citizen science data

Monitoring wildlife populations is essential if global targets to reverse biodiversity declines are to be met. Recent analysis of data from the UK’s long-term National Bat Monitoring Programme (NBMP) suggests stable or increasing population trends for many bat species, and these statistics help inform progress towards national biodiversity targets. However, although based on robust citizen science survey designs, it is unknown how sensitive these trends are to spatial and environmental biases. Here we use Bayesian hierarchical modelling with integrated nested Laplace approximation (INLA), to examine the impact of these types of biases on the population trends using relative occupancy of four species monitored by the NBMP Field Survey in Great Britain (GB): Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula and Eptesicus serotinus. Where possible, we also disaggregated trends to national levels using the best model per species to determine if national differences in trends remain once sampling biases are accounted for. Although we found evidence of spatial clustering in the NBMP Field Survey locations, the previously reported GB-wide population trends are broadly robust to spatial autocorrelation. In most species, accounting for spatial autocorrelation and species-environment relationships improved model fit. The nationally disaggregated models highlighted that GB-wide trends mask differences between England and Scotland, consistent with previous analysis of these data, as well as illustrating large gaps in survey effort, especially in Wales. We suggest that although bat population trends were found to be broadly robust to sampling biases present in these data, small differences could propagate over time and this impact is likely to be more severe in less structured citizen science data. Therefore, ensuring trends are robust to sampling biases present in citizen science datasets is critical to effective monitoring of progress towards biodiversity targets, managing populations sustainably, and ultimately a reversal of global declines

Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation

The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate. In order to take advantage of these data, information of varying quantity and quality needs to be integrated. Point process models have been proposed as an elegant way to achieve this for estimates of species distributions. These models can be fitted efficiently using Bayesian methods based on integrated nested Laplace approximations (INLA) with stochastic partial differential equations (SPDEs). This approach uses an efficient way to model spatial autocorrelation using a Gaussian random field and a triangular mesh over the spatial domain. The mesh is constructed by user-defined variables, so effectively represents a free parameter in the model. However, there is a lack of understanding about how to set these mesh parameters, and their effect on model performance. Here, we assess how mesh parameters affect predictions and model fit to estimate the distribution of the serotine bat, Eptesicus serotinus, in Great Britain. A Bayesian INLA model was fitted using five meshes of varying densities to a dataset comprising both structured observations from a national monitoring programme and opportunistic records. We demonstrate that mesh density impacted spatial predictions with a general loss of accuracy with increasing mesh coarseness. However, we also show that the finest mesh was unable to overcome spatial biases in the data. In addition, the magnitude of the covariate effects differed markedly between meshes. This confirms that mesh parameterisation is an important and delicate process with implications for model inference. We discuss how species distribution modellers might adapt their use of INLA in the light of these findings

Applying genomic approaches to identify historic population declines in European forest bats

1. Anthropogenically driven environmental changes over recent centuries have led to severe declines of wildlife populations. Better tools are needed to assess the magnitude and consequences of these declines. Anecdotal evidence suggests European bat populations have suffered substantial declines in the past centuries. However, there is little empirical evidence of these declines that can be used to put more recent population trends into historic context.2. This study is a collaboration between academics and conservation practitioners to develop molecular approaches capable of providing evidence of historic population changes that can inform conservation status assessments and management. We generated a genomic dataset of 46,872 SNPs for the Western barbastelle, Barbastella barbastellus, a regionally Vulnerable bat species, including colonies from across the species' British and Iberian ranges. We used a combination of landscape genetics and model-based inference of demographic history to identify both evidence of population size changes and possible drivers of these changes.3. Levels of genetic diversity increased and inbreeding decreased with increasing broadleaf woodland cover around the colony. Genetic connectivity was impeded by artificial lights and facilitated by rivers and broadleaf woodland cover.4. The demographic history analysis showed that both the northern and southern British barbastelle populations have declined by 99% over the past 330–548 years. These declines may be linked to the loss of large oak trees and native woodlands due to shipbuilding during the early colonial period.5. Synthesis and applications. Genomic approaches can provide a better understanding of the conservation status of threatened species, within historic and contemporary contexts, and inform their conservation management. Our findings of will directly influence the definition of the Favourable Conservation Status of the barbastelle, in turn influencing considerations of the conservation of the species in development plans. Knowledge gained will also help set species recovery targets. Policymakers are interested in using our approach for other species. This study shows how we can bridge the implementation gap between genomic research and direct conservation applications. There is an urgent need to carry out such collaborative studies for other priority species to enable informed species recovery interventions via policy mechanisms and project delivery

Effect of Pembrolizumab Plus Neoadjuvant Chemotherapy on Pathologic Complete Response in Women With Early-Stage Breast Cancer: An Analysis of the Ongoing Phase 2 Adaptively Randomized I-SPY2 Trial.

Importance: Approximately 25% of patients with early-stage breast cancer who receive (neo)adjuvant chemotherapy experience a recurrence within 5 years. Improvements in therapy are greatly needed. Objective: To determine if pembrolizumab plus neoadjuvant chemotherapy (NACT) in early-stage breast cancer is likely to be successful in a 300-patient, confirmatory randomized phase 3 neoadjuvant clinical trial. Design, Setting, and Participants: The I-SPY2 study is an ongoing open-label, multicenter, adaptively randomized phase 2 platform trial for high-risk, stage II/III breast cancer, evaluating multiple investigational arms in parallel. Standard NACT serves as the common control arm; investigational agent(s) are added to this backbone. Patients with ERBB2 (formerly HER2)-negative breast cancer were eligible for randomization to pembrolizumab between November 2015 and November 2016. Interventions: Participants were randomized to receive taxane- and anthracycline-based NACT with or without pembrolizumab, followed by definitive surgery. Main Outcomes and Measures: The primary end point was pathologic complete response (pCR). Secondary end points were residual cancer burden (RCB) and 3-year event-free and distant recurrence-free survival. Investigational arms graduated when demonstrating an 85% predictive probability of success in a hypothetical confirmatory phase 3 trial. Results: Of the 250 women included in the final analysis, 181 were randomized to the standard NACT control group (median [range] age, 47 [24.77] years). Sixty-nine women (median [range] age, 50 [27-71] years) were randomized to 4 cycles of pembrolizumab in combination with weekly paclitaxel followed by AC; 40 hormone receptor (HR)-positive and 29 triple-negative. Pembrolizumab graduated in all 3 biomarker signatures studied. Final estimated pCR rates, evaluated in March 2017, were 44% vs 17%, 30% vs 13%, and 60% vs 22% for pembrolizumab vs control in the ERBB2-negative, HR-positive/ERBB2-negative, and triple-negative cohorts, respectively. Pembrolizumab shifted the RCB distribution to a lower disease burden for each cohort evaluated. Adverse events included immune-related endocrinopathies, notably thyroid abnormalities (13.0%) and adrenal insufficiency (8.7%). Achieving a pCR appeared predictive of long-term outcome, where patients with pCR following pembrolizumab plus chemotherapy had high event-free survival rates (93% at 3 years with 2.8 years\u27 median follow-up). Conclusions and Relevance: When added to standard neoadjuvant chemotherapy, pembrolizumab more than doubled the estimated pCR rates for both HR-positive/ERBB2-negative and triple-negative breast cancer, indicating that checkpoint blockade in women with early-stage, high-risk, ERBB2-negative breast cancer is highly likely to succeed in a phase 3 trial. Pembrolizumab was the first of 10 agents to graduate in the HR-positive/ERBB2-negative signature. Trial Registration: ClinicalTrials.gov Identifier: NCT01042379