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Biodiversity is changing at unprecedented rates, and it is increasingly important that these changes are quantified through monitoring programmes. Previous recommendations for developing or enhancing these programmes focus either on the end goals, that is the intended use of the data, or on how these goals are achieved, for example through volunteer involvement in citizen science, but not both. These recommendations are rarely prioritized., We used a collaborative approach, involving 52 experts in biodiversity monitoring in the UK, to develop a list of attributes of relevance to any biodiversity monitoring programme and to order these attributes by their priority. We also ranked the attributes according to their importance in monitoring biodiversity in the UK. Experts involved included data users, funders, programme organizers and participants in data collection. They covered expertise in a wide range of taxa., We developed a final list of 25 attributes of biodiversity monitoring schemes, ordered from the most elemental (those essential for monitoring schemes; e.g. articulate the objectives and gain sufficient participants) to the most aspirational (e.g. electronic data capture in the field, reporting change annually). This ordered list is a practical framework which can be used to support the development of monitoring programmes., People's ranking of attributes revealed a difference between those who considered attributes with benefits to end users to be most important (e.g. people from governmental organizations) and those who considered attributes with greatest benefit to participants to be most important (e.g. people involved with volunteer biological recording schemes). This reveals a distinction between focussing on aims and the pragmatism in achieving those aims., Synthesis and applications. The ordered list of attributes developed in this study will assist in prioritizing resources to develop biodiversity monitoring programmes (including citizen science). The potential conflict between end users of data and participants in data collection that we discovered should be addressed by involving the diversity of stakeholders at all stages of programme development. This will maximize the chance of successfully achieving the goals of biodiversity monitoring programmes. [ABSTRACT FROM AUTHOR]

Despite lessons from terrestrial systems, conservation efforts in marine systems continue to focus on identifying priority sites for protection based on high species richness inferred from range maps. Range maps oversimplify spatial variability in animal distributions by assuming uniform distribution within range and de facto giving equal weight to critical and marginal habitats. We used Marxan ver. 2.43 to compare species richness-based systematic reserve network solutions using information about marine mammal range and relative abundance. At a global scale, reserve network solutions were strongly sensitive to model inputs and assumptions. Solutions based on different input data overlapped by a third at most, with agreement as low as 10% in some cases. At a regional scale, species richness was inversely related to density, such that species richness hotspots excluded highest-density areas for all species. Based on these findings, we caution that species-richness estimates derived from range maps and used as input in conservation planning exercises may inadvertently lead to protection of largely marginal habitat. [ABSTRACT FROM AUTHOR]

We coordinated a large-scale evaluation of northern bobwhite ( Colinus virginianus) population response to establishment of 9-m to 37-m linear patches (buffers) of native herbaceous vegetation along row-crop field margins as part of the Conservation Reserve Program practice Habitat Buffers for Upland Birds (CP33). We compared northern bobwhite covey densities on 1,088 paired row-crop fields with and without native herbaceous buffers in 13 states during autumn, 2006-2008. We used a 2-stage random effects modeling approach that incorporates the effective area as an offset in generalized linear mixed models to assess regional relationships among autumn bobwhite covey densities and covariates of field type (i.e., fields with vs. without native herbaceous buffers), ecological region, year, survey week, and contracted vegetative cover (i.e., planting native grasses and forbs vs. establishing through natural regeneration). Covey density was correlated with year and interaction effects of field type and ecological region. The year effect suggested annual variation in covey densities, whereas the field type by ecological region interaction suggested covey response to buffers was dependent on spatial location, likely reflecting differences in buffer establishment, succession, and characteristics of the surrounding landscape among regions. Mean fitted covey density on fields across all survey sites was 0.047 (±0.008 bootstrap standard error [BSE]) and 0.031 coveys/ha (±0.003 BSE) on row-crop fields with and without herbaceous buffers, respectively. Covey density was greater on fields with buffers relative to matched, comparison fields without buffers in the Mississippi Alluvial Valley (241%; P < 0.001) and both the eastern (123%; P < 0.001) and western (60%; P = 0.01) portions of the Southeastern Coastal Plain region. Covey density was an order of magnitude greater in the central Texas region compared to other regions, but exhibited a small response to native herbaceous buffers, as did density of coveys in the Eastern Tallgrass Prairie and Central Hardwoods regions. Disproportionate response to buffers in the Mississippi Alluvial Valley and Southeastern Coastal Plain suggests native herbaceous habitats might be limiting during autumn in these regions, whereas lack of response in the Eastern Tallgrass Prairie, Central Hardwoods, and central Texas regions suggests that herbaceous habitat either was not limiting or buffers failed to provide adequate requirements for bobwhites during autumn. Selection of other habitats to meet security and thermoregulatory needs might have resulted in lack of response in these regions. Native herbaceous cover provided by buffers can provide critical habitat in row-crop agricultural systems in some regions, and can contribute to regional population recovery objectives of the Northern Bobwhite Conservation Initiative (NBCI). However, range-wide NBCI recovery objectives will best be met through multiple conservation practices in row-crop agricultural systems. © 2013 The Wildlife Society. [ABSTRACT FROM AUTHOR]

1. Using novel analytical methods applied to extensive national bird and mammal monitoring data, we examine whether 29 English bird populations may have been depressed by increases in the abundance of two broad categories of predators. The first includes predators of juvenile and adult birds: Eurasian sparrowhawks Accipiter nisus, common kestrels Falco tinnunculus and common buzzards Buteo buteo, and the second comprises five nest predators: carrion crow Corvus corone, black-billed magpie Pica pica, Eurasian jay Garrulus glandarius, great spotted woodpecker Dendrocopos major and grey squirrel Sciurus carolinensis. 2. For 22 avian prey species, there is no evidence that increases in common avian predators and grey squirrels are associated with large-scale depression of prey abundance or population declines. For the remaining seven, some negative correlations are biologically unlikely but we cannot exclude the possibility that some of the negative associations are causally related. For example, a particularly strong negative relationship between sparrowhawk and tree sparrow during the Common Birds Census period (1967–2000) could indicate a causal relationship. In contrast, the negative association between buzzard and goldfinch during the Breeding Bird Survey period (1995–2005) is, on ecological grounds, unlikely to do so. Whilst a correlative study such as this cannot prove causation, it provides a focus for more detailed work on particular species. 3. Unexpected was a large number of positive associations between predators and prey, particularly for native avian nest predators, which largely exonerates these predators as driving declines in passerine numbers. 4. Synthesis and applications. Analyses of large-scale and extensive national monitoring data provides little underlying evidence for large-scale impacts of widespread avian predators and grey squirrels on avian prey populations, although we cannot exclude the possibility that a small number of negative associations between particular predator and prey species reflect causal relationships or that predators affect prey species at smaller spatial scales. [ABSTRACT FROM AUTHOR]

There are two postulated causes for the observed periodic fluctuations (cycles) in red grouse (Lagopus lagopus scoticus). The first involves interaction with the parasitic nematode Trichostrongylus tenuis. The second invokes delayed regulation through the effect of male aggressiveness on territoriality. Empirical evidence exists to support both hypotheses, and each hypothesis has been modeled deterministically. However, little effort has gone into looking at the combined effects of the two mechanisms or formally fitting the corresponding models to field data. Here we present a model for red grouse dynamics that includes both parasites and territoriality. To explore the single and combined hypotheses, we specify three versions of this model and fit them to data using Bayesian state-space modeling, a method that allows statistical inference to be performed on mechanistic models such as ours. Output from the three models is then examined to determine their goodness of fit and the biological plausibility of the parameter values required by each to fit the population data. While all three models are capable of emulating the observed cyclic dynamics, only the model including both aggression and parasites does so under consistently realistic parameter values, providing theoretical support for the idea that both mechanisms shape red grouse cycles. [ABSTRACT FROM AUTHOR]

Inferences based on counts adjusted for detectability represent a marked improvement over unadjusted counts, which provide no information about true population density and rely on untestable and unrealistic assumptions about constant detectability for inferring differences in density over time or space. Distance sampling is a widely used method to estimate detectability and therefore density. In the standard method, we model the probability of detecting a bird as a function of distance alone. Here, we describe methods that allow us to model probability of detection as a function of additional covariates--an approach available in DISTANCE, version 5.0 (Thomas et al. 2005) but still not widely applied. The main use of these methods is to increase the reliability of density estimates made on subsets of the whole data (e.g., estimates for different habitats, treatments, periods, or species), to increase precision of density estimates or to allow inferences about the covariates themselves. We present a case study of the use of multiple covariates in an analysis of a point-transect survey of Hawaii Amakihi (Hemignathus virens). [ABSTRACT FROM AUTHOR]

Methods that allow unbiased estimation of animal abundance are increasingly demanded in management and conservation. The use of these methods should respect their assumptions. The need for accurate distance measurements in distance-sampling surveys is stressed. Here we present 2 alternative methods for measuring distance from a line to an object during helicopter surveys: 1) using a Global Positioning System (GPS) unit, with distances measured using appropriate software; and 2) recording declination angles and altitudes, using basic trigonometry to obtain the appropriate distances. These are compared to distances measured by a laser rangefinder (assumed to be true distances). The effect of the different errors on estimated densities is assessed by simulation. The GPS method appeared to be very accurate, while a potential downward bias in estimated density could be present if the inclinometer method is used. We discuss the implication for wildlife studies of using different measurement methods leading to different errors. [ABSTRACT FROM AUTHOR]

The article focuses on a research that discusses how changes in fire activity are threatening species with extinction around the world. It mentions that the research revealed that interactions with anthropogenic forces such as land use, global climate change, and biotic invasions are transforming fire activity and its impacts on biodiversity.

Among other approaches, camera trap distance sampling (CTDS) is used to estimate animal abundance from unmarked populations. It was formulated for videos and observation distances are measured at predetermined 'snapshot moments'. Surveys recording still images with passive infrared motion sensors suffer from frequent periods where animals are not photographed, either because of technical delays before the camera can be triggered again (i.e. 'camera recovery time') or because they remain stationary and do not immediately retrigger the camera following camera recovery time (i.e. 'retrigger delays'). These effects need to be considered when calculating temporal survey effort to avoid downwardly biased abundance estimates. Here, we extend the CTDS model for passive infrared motion sensor recording of single images or short photo series. We propose estimating 'mean time intervals between triggers' as combined mean camera recovery time and mean retrigger delays from the time interval distribution of pairs of consecutive pictures, using a Gamma and Exponential function, respectively. We apply the approach to survey data on red deer, roe deer and wild boar. Mean time intervals between triggers were very similar when estimated empirically and when derived from the model-based approach. Depending on truncation times (i.e. the time interval between consecutive pictures beyond which data are discarded) and species, we estimated mean time intervals between retriggers between 8.28 and 15.05 s. Using a predefined snapshot interval, not accounting for these intervals, would lead to underestimated density by up to 96% due to overestimated temporal survey effort. The proposed approach is applicable to any taxa surveyed with camera traps. As programming of cameras to record still images is often preferred over video recording due to reduced consumption of energy and memory, we expect this approach to find broad application, also for other camera trap methods than CTDS., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Birds are often obligate to specific habitats which can result in study areas with complex boundaries due to sudden changes in vegetation or other features. This can result in study areas with concave arcs or that include holes of unsuitable habitat such as lakes or agricultural fields. Spatial models used to produce species' distribution and density estimates need to respect such boundaries to make informed decisions for species conservation and management. The soap film smoother is one model for complex study regions which controls the boundary behaviour, ensuring realistic values at the edges of the region. We apply the soap film smoother to account for boundary effects and compare it with thin plate regression spline (TPRS) smooth and design-based conventional distance sampling methods to produce abundance estimates from point-transect distance sampling collected data on Hawai'i 'Ākepa Loxops coccineus in the Hakalau Forest Unit of the Big Island National Wildlife Refuge Complex, Hawai'i Island, USA. The soap film smoother predicted zero or near zero densities in the northern part of the domain and two hotspots (in the southern and central parts of the domain). Along the boundary the soap film model predicted relatively high densities where 'Ākepa occur in the adjacent forest and near zero elsewhere. The design-based and soap film abundance estimates were nearly identical. The width of the soap film confidence interval was 16.5% and 0.8% wider than the width of the TPRS smooth and design-based confidence intervals, respectively. The peaks in predicted densities along the boundary indicates leakage by the TPRS smooth. We provide a discussion of the statistical methods, biological findings and management implications of applying soap film smoothers to estimate forest bird population status., Competing Interests: The authors declare that they have no competing interests.

Background: We assess the analysis of the data resulting from a field experiment conducted by Pilling et al. (PLoS ONE. doi: 10.1371/journal.pone.0077193, 5) on the potential effects of thiamethoxam on honeybees. The experiment had low levels of replication, so Pilling et al. concluded that formal statistical analysis would be misleading. This would be true if such an analysis merely comprised tests of statistical significance and if the investigators concluded that lack of significance meant little or no effect. However, an analysis that includes estimation of the size of any effects-with confidence limits-allows one to reach conclusions that are not misleading and that produce useful insights., Main Body: For the data of Pilling et al., we use straightforward statistical analysis to show that the confidence limits are generally so wide that any effects of thiamethoxam could have been large without being statistically significant. Instead of formal analysis, Pilling et al. simply inspected the data and concluded that they provided no evidence of detrimental effects and from this that thiamethoxam poses a "low risk" to bees., Conclusions: Conclusions derived from the inspection of the data were not just misleading in this case but also are unacceptable in principle, for if data are inadequate for a formal analysis (or only good enough to provide estimates with wide confidence intervals), then they are bound to be inadequate as a basis for reaching any sound conclusions. Given that the data in this case are largely uninformative with respect to the treatment effect, any conclusions reached from such informal approaches can do little more than reflect the prior beliefs of those involved.

The importance of multispecies models for understanding complex ecological processes and interactions is beginning to be realized. Recent developments, such as those by Lahoz-Monfort et al. (2011), have enabled synchrony in demographic parameters across multiple species to be explored. Species in a similar environment would be expected to be subject to similar exogenous factors, although their response to each of these factors may be quite different. The ability to group species together according to how they respond to a particular measured covariate may be of particular interest to ecologists. We fit a multispecies model to two sets of similar species of garden bird monitored under the British Trust for Ornithology's Garden Bird Feeding Survey. Posterior model probabilities were estimated using the reversible jump algorithm to compare posterior support for competing models with different species sharing different subsets of regression coefficients. There was frequently good agreement between species with small asynchronous random-effect components and those with posterior support for models with shared regression coefficients; however, this was not always the case. When groups of species were less correlated, greater uncertainty was found in whether regression coefficients should be shared or not. The methods outlined in this study can test additional hypotheses about the similarities or synchrony across multiple species that share the same environment. Through the use of posterior model probabilities, estimated using the reversible jump algorithm, we can detect multispecies responses in relation to measured covariates across any combination of species and covariates under consideration. The method can account for synchrony across species in relation to measured covariates, as well as unexplained variation accounted for using random effects. For more flexible, multiparameter distributions, the support for species-specific parameters can also be measured.

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

1.Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance.2.We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use.3.Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated.4.A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark-recapture distance sampling, which relaxes the assumption of certain detection at zero distance.5.All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap.6.Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modelling analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software.7.Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of-the-art software that implements these methods is described that makes the methods accessible to practising ecologists.

1. One of the most difficult problems in developing spatially explicit models of population dynamics is the validation and parameterization of the movement process. We show how movement models derived from capture-recapture analysis can be improved by incorporating them into a spatially explicit metapopulation model that is fitted to a time series of abundance data. 2. We applied multisite capture-recapture analysis techniques to photo-identification data collected from female grey seals at the four main breeding colonies in the North Sea between 1999 and 2001. The best-fitting movement models were then incorporated into state-space metapopulation models that explicitly accounted for demographic and observational stochasticity. 3. These metapopulation models were fitted to a 20-year time series of pup production data for each colony using a Bayesian approach. The best-fitting model, based on the Akaike Information Criterion (AIC), had only a single movement parameter, whose confidence interval was 82% less than that obtained from the capture-recapture study, but there was some support for a model that included an effect of distance between colonies. 4. The state-space modelling provided improved estimates of other demographic parameters. 5. The incorporation of movement, and the way in which it was modelled, affected both local and regional dynamics. These differences were most evident as colonies approached their carrying capacities, suggesting that our ability to discriminate between models should improve as the length of the grey seal time series increases.