Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability

Higher efficiency in large-scale and long-term biodiversity monitoring can be obtained through the use of Essential Biodiversity Variables, among which species population sizes provide key data for conservation programs. Relevant estimations and assessment of actual population sizes are critical for species conservation, especially in the current context of global biodiversity erosion. However, knowledge on population size varies greatly, depending on species conservation status and ranges. While the most threatened or restricted-range species generally benefit from exhaustive counts and surveys, monitoring common and widespread species population size tends to be neglected or is simply more challenging to achieve. In such a context, citizen science (CS) is a powerful tool for the long-term monitoring of common species through the engagement of various volunteers, permitting data acquisition on the long term and over large spatial scales. Despite this substantially increased sampling effort, detectability issues imply that even common species may remain unnoticed at suitable sites. The use of structured CS schemes, including repeated visits, enables to model the detection process, permitting reliable inferences of population size estimates. Here, we relied on a large French structured CS scheme (EPOC-ODF) comprising 27,156 complete checklists over 3,873 sites collected during the 2021-2023 breeding seasons to estimate the population size of 63 common bird species using hierarchical distance sampling (HDS). These population size estimates were compared to the previous expert-based French breeding bird atlas estimations, which did not account for detectability issues. We found that population size estimates from the former French breeding bird atlas were lower than those estimated using HDS for 65% of species. Such a prevalence of lower estimations is likely due to more conservative estimates inferred from semi-quantitative expert-based assessments used for the previous atlas. We also found that species with long-range songs such as the Common Cuckoo (Cuculus canorus), Eurasian Hoopoe (Upupa epops) or the Eurasian Blackbird (Turdus merula) had, in contrast, higher estimated population sizes in the previous atlas than in our HDS models. Our study highlights the need to rely on sound statistical methodology to ensure reliable ecological inferences with adequate uncertainty estimation and advocates for a higher reliance on structured CS in support of long-term biodiversity monitoring.

Detection Rate of Bird Species and What It Depends on: Tips for Field Surveys

Bird counting inevitably suffers from imperfect detection, which varies across species, habitats, period of the day, and seasons. Although various modeling techniques have recently been developed to account for this phenomenon, the biological basis of natural variation in detection remains insufficiently known. This study examined the bird species’ detection rate throughout the day, considering their body mass and diet type, concerning the environment and weather characteristics. Species detection rates were significantly affected by the number of individuals of that species but were unrelated to body mass. Overall, species with the highest detection rate were Corn bunting, Blackbird, European robin, House sparrow and Common chiffchaff. Granivores-insectivores and insectivores showed significant differences in detection rates throughout the day among habitats, with higher detection rates in grasslands during the afternoon. Insectivores showed higher detection rates in farmland during midday (warmest time of the day). Granivores, omnivores and scavengers did not show changes in detection rates in different day periods. Such patterns in daily detection rates were significant even when considering abundance and total species richness in each community. Finally, cloudiness was unrelated to the overall detection rate of birds, while temperature and wind affected detection rates in some guilds. Our findings provide some advice for choosing a suitable ornithological sampling method by considering the avian communities composition in combination with the type of environment, the diet of bird species, and the period of the day.

A Framework of Observer-Based Biases in Citizen Science Biodiversity Monitoring: Semi-Structuring Unstructured Biodiversity Monitoring Protocols

Citizen science, whereby ordinary citizens participate in scientific endeavors, is widely used for biodiversity monitoring, most commonly by relying on unstructured monitoring approaches. Notwithstanding the potential of unstructured citizen science to engage the public and collect large amounts of biodiversity data, observers’ considerations regarding what, where and when to monitor result in biases in the aggregate database, thus impeding the ability to draw conclusions about trends in species’ spatio-temporal distribution. Hence, the goal of this study is to enhance our understanding of observer-based biases in citizen science for biodiversity monitoring. Toward this goals we: (a) develop a conceptual framework of observers’ decision-making process along the steps of monitor – > record and share, identifying the considerations that take place at each step, specifically highlighting the factors that influence the decisions of whether to record an observation (b) propose an approach for operationalizing the framework using a targeted and focused questionnaire, which gauges observers’ preferences and behavior throughout the decision-making steps, and (c) illustrate the questionnaire’s ability to capture the factors driving observer-based biases by employing data from a local project on the iNaturalist platform. Our discussion highlights the paper’s theoretical contributions and proposes ways in which our approach for semi-structuring unstructured citizen science data could be used to mitigate observer-based biases, potentially making the collected biodiversity data usable for scientific and regulatory purposes.

Phylogenetic uncertainty and the inference of patterns in community ecology and comparative studies

Progress in phylogenetic community ecology is often limited by the availability of phylogenetic information and the lack of appropriate methods and solutions to deal with this problem. We estimate the effect of the lack of phylogenetic information on the relations among taxa measured by commonly used phylogenetic metrics in comparative studies and community ecology, namely: Blomberg's K phylogenetic signal, Faith's Phylogenetic Diversity (PD), Mean Phylogenetic Distance (MPD) and Mean Nearest Taxon Distance (MNTD). To overcome this problem, we tested two possible solutions: Polytomic trees and Operational trees. Our results show that the effects on K values strongly depended on the level of phylogenetic signal. In the case of the community metrics, the effects were insensitive to the patterns of species distribution in the communities. Community metrics tended to be overestimated with both Polytomic and Operational trees, but the overestimation was higher with Polytomic trees. PD and MPD metrics were less biased than MNTD metric. We show that the lack of phylogenetic resolution is not necessarily problematic for all analyses and that its effect will depend on the chosen metric and on the solutions used to deal with the problem. Based on our results, we suggest that ecologists should prefer the Operational tree solution to remove polytomies in the phylogenetic tree and take careful consideration while designing experiments, and analyzing and interpreting the results of phylogenetic metrics.

Combining spawn egg counts, individual photo-ID and genetic fingerprinting to estimate the population size and sex ratio of an endangered amphibian

According to the International Union for Conservation of Nature Red List, 41% of the world's amphibian species are threatened with extinction, making them more threatened than any other vertebrate group nowadays. Given the global amphibian crisis, comprehensive understanding of demographics and population trends of declining and threatened species is essential for effective management and conservation strategies. Counting egg spawns is widely used to assess population abundance in pond breeding anurans. However, it is unknown how such counts translate into robust population size estimations. We monitored the breeding activity of the Natterjack toad (Epidalea calamita), combining egg string counts and individual photo-identification with Capture-Mark-Recapture population size and operational sex ratio estimation. Male Natterjack toads were identified by the pattern of natural markings with repeated ID of the same individual confirmed for 10% of the samples using genetic fingerprinting. We identified 647 unique individuals within a closed study population at Caherdaniel, Co Kerry. Population estimates derived from egg string counts estimated a breeding population of 368 females (95% CI 353-384) and Capture-Mark-Recapture estimated a breeding population of 1698 males (95% CI 1000-2397). The female:male sex ratio was conservatively estimated at 1:5 (95% CI 1:3-1:6) where 62% ± 6% of females were assumed to spawn. These substantially departed from any priori assumption of 1:1 which could have underestimated the breeding population by up to 72%. Where amphibian absolute population size estimation is necessary, methods should include empirical survey data on operational sex ratios and not rely on assumptions or those derived from the literature which may be highly population and/or context-dependent.

The importance of common and the irrelevance of rare species for partition the variation of community matrix: implications for sampling and conservation

In community ecology, it is important to understand the distribution of communities along environmental and spatial gradients. However, it is common for the residuals of models investigating those relationships to be very high (> 50%). It is believed that species’ intrinsic characteristics such as rarity can contribute to large residuals. The objective of this study is to test the relationship among communities and environmental and spatial predictors by evaluating the relative contribution of common and rare species to the explanatory power of models. Our hypothesis is that the residual of partition the variation of community matrix (varpart) models will decrease as rare species get removed. We used several environmental variables and spatial filters as varpart model predictors of fish and Zygoptera (Insecta: Odonata) communities in 109 and 141 Amazonian streams, respectively. We built a repetition structure, in which we gradually removed common and rare species independently. After the repetitions and removal of species, our hypothesis was not corroborated. In all scenarios, removing up to 50% of rare species did not reduce model residuals. Common species are important and rare species are irrelevant for understanding the relationships among communities and environmental and spatial gradients using varpart. Therefore, our findings suggest that studies using varpart with single sampling events that do not detect rare species can efficiently assess general distributional patterns of communities along environmental and spatial gradients. However, when the objectives concern conservation of biodiversity and functional diversity, rare species must be carefully assessed by other complementary methods, since they are not well represented in varpart models.

On the right track: placement of camera traps on roads improves detection of predators and shows non-target impacts of feral cat baiting

Abstract ContextTo understand the ecological consequences of predator management, reliable and accurate methods are needed to survey and detect predators and the species with which they interact. Recently, poison baits have been developed specifically for lethal and broad-scale control of feral cats in Australia. However, the potential non-target effects of these baits on other predators, including native apex predators (dingoes), and, in turn, cascading effects on lower trophic levels (large herbivores), are poorly understood. AimsWe examined the effect that variation in camera trapping-survey design has on detecting dingoes, feral cats and macropodids, and how different habitat types affect species occurrences. We then examined how a feral cat poison baiting event influences the occupancy of these sympatric species. MethodsWe deployed 80 remotely triggered camera traps over the 2410-km2 Matuwa Indigenous Protected Area, in the semiarid rangelands of Western Australia, and used single-season site-occupancy models to calculate detection probabilities and occupancy for our target species before and after baiting. Key resultsCameras placed on roads were ~60 times more likely to detect dingoes and feral cats than were off-road cameras, whereas audio lures designed to attract feral cats had only a slight positive effect on detection for all target species. Habitat was a significant factor affecting the occupancy of dingoes and macropodids, but not feral cats, with both species being positively associated with open woodlands. Poison baiting to control feral cats did not significantly reduce their occupancy but did so for dingoes, whereas macropodid occupancy increased following baiting and reduced dingo occupancy. ConclusionsCamera traps on roads greatly increase the detection probabilities for predators, whereas audio lures appear to add little or no value to increasing detection for any of the species we targeted. Poison baiting of an invasive mesopredator appeared to negatively affect a non-target, native apex predator, and, in turn, may have resulted in increased activity of large herbivores. ImplicationsManagement and monitoring of predators must pay careful attention to survey design, and lethal control of invasive mesopredators should be approached cautiously so as to avoid potential unintended negative ecological consequences (apex-predator suppression and herbivore release).

Outbreak of Peste des Petits Ruminants among Critically Endangered Mongolian Saiga and Other Wild Ungulates, Mongolia, 2016-2017

The 2016-2017 introduction of peste des petits ruminants virus (PPRV) into livestock in Mongolia was followed by mass mortality of the critically endangered Mongolian saiga antelope and other rare wild ungulates. To assess the nature and population effects of this outbreak among wild ungulates, we collected clinical, histopathologic, epidemiologic, and ecological evidence. Molecular characterization confirmed that the causative agent was PPRV lineage IV. The spatiotemporal patterns of cases among wildlife were similar to those among livestock affected by the PPRV outbreak, suggesting spillover of virus from livestock at multiple locations and time points and subsequent spread among wild ungulates. Estimates of saiga abundance suggested a population decline of 80%, raising substantial concerns for the species' survival. Consideration of the entire ungulate community (wild and domestic) is essential for elucidating the epidemiology of PPRV in Mongolia, addressing the threats to wild ungulate conservation, and achieving global PPRV eradication.

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.

Using Semistructured Surveys to Improve Citizen Science Data for Monitoring Biodiversity

Biodiversity is being lost at an unprecedented rate, and monitoring is crucial for understanding the causal drivers and assessing solutions. Most biodiversity monitoring data are collected by volunteers through citizen science projects, and often crucial information is lacking to account for the inevitable biases that observers introduce during data collection. We contend that citizen science projects intended to support biodiversity monitoring must gather information about the observation process as well as species occurrence. We illustrate this using eBird, a global citizen science project that collects information on bird occurrences as well as vital contextual information on the observation process while maintaining broad participation. Our fundamental argument is that regardless of what species are being monitored, when citizen science projects collect a small set of basic information about how participants make their observations, the scientific value of the data collected will be dramatically improved.

Transcending data gaps: a framework to reduce inferential errors in ecological analyses

The analysis of functional diversity (FD) has gained increasing importance due to its generality and utility in ecology. In particular, patterns in the spatial distribution and temporal change of FD are being used to predict locations and functional groups that are immediately vulnerable to global changes. A major impediment to the accurate measurement of FD is the pervasiveness of missing data in trait datasets. While such prevalent data gaps can engender misleading inferences in FD analyses, we currently lack any practical guide to handle missing data in trait datasets. Here, we identify significant mismatches between true FD and values derived from datasets that contain missing data. We demonstrate that imputing missing data with a phylogeny-informed approach reduces the risk of misinterpretation of FD patterns, and provides baseline information against which central questions in ecology can be evaluated.

Is the detection of aquatic environmental DNA influenced by substrate type?

The use of environmental DNA (eDNA) to assess the presence-absence of rare, cryptic or invasive species is hindered by a poor understanding of the factors that can remove DNA from the system. In aquatic systems, eDNA can be transported out either horizontally in water flows or vertically by incorporation into the sediment. Equally, eDNA may be broken down by various biotic and abiotic processes if the target organism leaves the system. We use occupancy modelling and a replicated mesocosm experiment to examine how detection probability of eDNA changes once the target species is no longer present. We hypothesise that detection probability falls faster with a sediment which has a large number of DNA binding sites such as topsoil or clay, over lower DNA binding capacity substrates such as sand. Water removed from ponds containing the target species (the great crested newt) initially showed high detection probabilities, but these fell to between 40% and 60% over the first 10 days and to between 10% and 22% by day 15: eDNA remained detectable at very low levels until day 22. Very little difference in detection was observed between the control group (no substrate) and the sand substrate. A small reduction in detection probability was observed between the control and clay substrates, but this was not significant. However, a highly significant reduction in detection probability was observed with a topsoil substrate. This result is likely to have stemmed from increased levels of PCR inhibition, suggesting that incorporation of DNA into the sentiment is of only limited importance. Surveys of aquatic species using eDNA clearly need to take account of substrate type as well as other environmental factors when collecting samples, analysing data and interpreting the results.

Sampling effort determination in bird surveys: do current norms meet best-practice recommendations?

A critical design component of studies measuring diversity is sampling effort. Allocation of sampling effort dictates how many sites can be sampled within a particular time-frame or budget, as well as sample duration, frequency and intensity, thereby determining the resolution and reliability of emergent inferences. Conventional survey techniques use fixed-effort methods that assume invariant detectabilities among sites and species. Several approaches have been developed in the past decade that account for variable detectability by using alternative sampling methods or by adjusting standard counts before analysis, but it is unclear how widely adopted these techniques have been or how current bird surveying norms compare with best-practice recommendations. I conducted a systematic search of the primary literature to ascertain how sampling effort is determined, how much effort is devoted to sampling each site and how variation in detectability is dealt with. Of 225 empirical studies of bird diversity published between 2004 and 2016, five used results-based stopping rules (each derived independently), 54 used proportional sampling, and 159 (71%) used implicit effort-based stopping rules (fixed effort). Effort varied widely, but 61% of studies used samples of 10min or less and 62% of studies expended total effort per datum of 2h or less, with 78% providing no justification for sampling efforts used and just 15% explicitly accounting for estimated detectability. Given known variation in detectability, relying on short-duration fixed-effort approaches without validation or post hoc correction means that most bird diversity studies necessarily under-sample some sites and/or species. Having identified current bird surveying norms and highlighted their shortcomings, I provide five practical solutions to improve sampling effort determination, urging contributors and consumers of empirical ecological literature to consider survey data in terms of sample completeness.

Citizen science monitoring reveals a significant, ongoing decline of the Endangered Carnaby's black-cockatoo Calyptorhynchus latirostris

Citizen science monitoring programmes are making increasingly important contributions to wildlife conservation, often at spatial and temporal scales unachievable by individual or teams of researchers. They are particularly valuable in estimating population trends and management impacts, and thus informing effective conservation decisions for declining species. The quality and potential biases of citizen science data are of concern, however, and appropriate experimental design and analysis are needed to ensure that the maximum scientific value is extracted. We investigated these issues in a citizen science survey of the Endangered Carnaby's black-cockatoo Calyptorhynchus latirostris. Since 2010, citizen scientists have conducted synchronized annual counts of Carnaby's black-cockatoo at roost sites to estimate the population trend. Survey effort was substantial, with c. 150 sites surveyed by > 260 volunteers each year. Relatively few sites were occupied, however, and only 42 (16%) of the 265 sites surveyed in total accounted for 95% of all observations. Many sites were empty and survey effort was often inconsistent. Taking these issues into account, analysis showed a statistically significant decline in roost occupancy rate and a non-significant decline in the mean size of roosting flocks, with an estimated overall trend of 14% decline per annum in the number of roosting birds. We highlight three important issues for citizen science monitoring programmes: the need to maintain regular surveys of sample sites to avoid patchy data, use an appropriate model that accounts for variable survey effort, high frequency of zero counts, and sampling site turnover, and incorporate information on site characteristics to help explain variation.

Multi-Season Regional Analysis of Multi-Species Occupancy: Implications for Bird Conservation in Agricultural Lands in East-Central Argentina

Rapid expansion and intensification of agriculture create challenges for the conservation of biodiversity and associated ecosystem services. In Argentina, the total row crop planted area has increased in recent decades with the expansion of soybean cultivation, homogenizing the landscape. In 2003 we started the first long-term, large-scale bird monitoring program in agroecosystems of central Argentina, in portions of the Pampas and Espinal ecoregions. Using data from this program, we evaluated the effect of land use and cover extent on birds between 2003-2012, accounting for imperfect detection probabilities using a Bayesian hierarchical, multi-species and multi-season occupancy model. We tested predictions that species diversity is positively related to habitat heterogeneity, which in intensified agroecosystems is thought to be mediated by food availability; thus the extent of land use and cover is predicted to affect foraging guilds differently. We also infer about ecosystem services provisioning and inform management recommendations for conservation of birds. Overall our results support the predictions. Although many species within each guild responded differently to land use and native forest cover, we identified generalities for most trophic guilds. For example, granivorous gleaners, ground insectivores and omnivores responded negatively to high proportions of soybean, while insectivore gleaners and aerial foragers seemed more tolerant. Habitat heterogeneity would likely benefit most species in an intensified agroecosystem, and can be achieved with a diversity of crops, pastures, and natural areas within the landscape. Although most studied species are insectivores, potentially beneficial for pest control, some guilds such as ground insectivores are poorly represented, suggesting that agricultural intensification reduces ecological functions, which may be recovered through management. Continuation of the bird monitoring program will allow us to continue to inform for conservation of birds in agroecosystems, identify research needed to reduce key uncertainties, and anticipate the effects of changes in agriculture in central Argentina.

Modeling Systematic Change in Stopover Duration Does Not Improve Bias in Trends Estimated from Migration Counts

The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration) to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling) to reduce the probability that individuals will be detected on more than one occasion.

Recommended survey designs for occupancy modelling using motion-activated cameras: insights from empirical wildlife data

Motion-activated cameras are a versatile tool that wildlife biologists can use for sampling wild animal populations to estimate species occurrence. Occupancy modelling provides a flexible framework for the analysis of these data; explicitly recognizing that given a species occupies an area the probability of detecting it is often less than one. Despite the number of studies using camera data in an occupancy framework, there is only limited guidance from the scientific literature about survey design trade-offs when using motion-activated cameras. A fuller understanding of these trade-offs will allow researchers to maximise available resources and determine whether the objectives of a monitoring program or research study are achievable. We use an empirical dataset collected from 40 cameras deployed across 160 km(2) of the Western Slope of Colorado, USA to explore how survey effort (number of cameras deployed and the length of sampling period) affects the accuracy and precision (i.e., error) of the occupancy estimate for ten mammal and three virtual species. We do this using a simulation approach where species occupancy and detection parameters were informed by empirical data from motion-activated cameras. A total of 54 survey designs were considered by varying combinations of sites (10-120 cameras) and occasions (20-120 survey days). Our findings demonstrate that increasing total sampling effort generally decreases error associated with the occupancy estimate, but changing the number of sites or sampling duration can have very different results, depending on whether a species is spatially common or rare (occupancy = ψ) and easy or hard to detect when available (detection probability = p). For rare species with a low probability of detection (i.e., raccoon and spotted skunk) the required survey effort includes maximizing the number of sites and the number of survey days, often to a level that may be logistically unrealistic for many studies. For common species with low detection (i.e., bobcat and coyote) the most efficient sampling approach was to increase the number of occasions (survey days). However, for common species that are moderately detectable (i.e., cottontail rabbit and mule deer), occupancy could reliably be estimated with comparatively low numbers of cameras over a short sampling period. We provide general guidelines for reliably estimating occupancy across a range of terrestrial species (rare to common: ψ = 0.175-0.970, and low to moderate detectability: p = 0.003-0.200) using motion-activated cameras. Wildlife researchers/managers with limited knowledge of the relative abundance and likelihood of detection of a particular species can apply these guidelines regardless of location. We emphasize the importance of prior biological knowledge, defined objectives and detailed planning (e.g., simulating different study-design scenarios) for designing effective monitoring programs and research studies.

The influence of vegetation height heterogeneity on forest and woodland bird species richness across the United States

Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity. Traditionally, studies have relied primarily on two-dimensional habitat structure to model broad scale species richness. Vegetation vertical structure is increasingly used at local scales. However, the spatial arrangement of vegetation height has never been taken into consideration. Our goal was to examine the efficacies of three-dimensional forest structure, particularly the spatial heterogeneity of vegetation height in improving avian richness models across forested ecoregions in the U.S. We developed novel habitat metrics to characterize the spatial arrangement of vegetation height using the National Biomass and Carbon Dataset for the year 2000 (NBCD). The height-structured metrics were compared with other habitat metrics for statistical association with richness of three forest breeding bird guilds across Breeding Bird Survey (BBS) routes: a broadly grouped woodland guild, and two forest breeding guilds with preferences for forest edge and for interior forest. Parametric and non-parametric models were built to examine the improvement of predictability. Height-structured metrics had the strongest associations with species richness, yielding improved predictive ability for the woodland guild richness models (r(2) = ∼ 0.53 for the parametric models, 0.63 the non-parametric models) and the forest edge guild models (r(2) = ∼ 0.34 for the parametric models, 0.47 the non-parametric models). All but one of the linear models incorporating height-structured metrics showed significantly higher adjusted-r2 values than their counterparts without additional metrics. The interior forest guild richness showed a consistent low association with height-structured metrics. Our results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of forest bird species. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness.

Assessing the utility of statistical adjustments for imperfect detection in tropical conservation science

In recent years, there has been a fast development of models that adjust for imperfect detection. These models have revolutionized the analysis of field data, and their use has repeatedly demonstrated the importance of sampling design and data quality. There are, however, several practical limitations associated with the use of detectability models which restrict their relevance to tropical conservation science.

We outline the main advantages of detectability models, before examining their limitations associated with their applicability to the analysis of tropical communities, rare species and large‐scale data sets. Finally, we discuss whether detection probability needs to be controlled before and/or after data collection.

Models that adjust for imperfect detection allow ecologists to assess data quality by estimating uncertainty and to obtain adjusted ecological estimates of populations and communities. Importantly, these models have allowed informed decisions to be made about the conservation and management of target species.

Data requirements for obtaining unadjusted estimates are substantially lower than for detectability‐adjusted estimates, which require relatively high detection/recapture probabilities and a number of repeated surveys at each location. These requirements can be difficult to meet in large‐scale environmental studies where high levels of spatial replication are needed, or in the tropics where communities are composed of many naturally rare species. However, while imperfect detection can only be adjusted statistically, covariates of detection probability can also be controlled through study design. Using three study cases where we controlled for covariates of detection probability through sampling design, we show that the variation in unadjusted ecological estimates from nearly 100 species was qualitatively the same as that obtained from adjusted estimates. Finally, we discuss that the decision as to whether one should control for covariates of detection probability through study design or statistical analyses should be dependent on study objectives.

Synthesis and applications. Models that adjust for imperfect detection are an important part of an ecologist's toolkit, but they should not be uniformly adopted in all studies. Ecologists should never let the constraints of models dictate which questions should be pursued or how the data should be analysed, and detectability models are no exception. We argue for pluralism in scientific methods, particularly where cost‐effective applied ecological science is needed to inform conservation policy at a range of different scales and in many different systems.

Models that adjust for imperfect detection are an important part of an ecologist's toolkit, but they should not be uniformly adopted in all studies. Ecologists should never let the constraints of models dictate which questions should be pursued or how the data should be analysed, and detectability models are no exception. We argue for pluralism in scientific methods, particularly where cost‐effective applied ecological science is needed to inform conservation policy at a range of different scales and in many different systems.

Diversity of birds in eastern North America shifts north with global warming

The distribution of diversity along latitudinal and elevation gradients, and the coupling of this phenomenon with climate, is a pattern long recognized in ecology. Hypothesizing that climate change may have altered this pattern over time, we investigated whether the aggregate of reported northward shifts of bird ranges in North America is now detectable in community-level indices such as richness and diversity. Here, we report that bird diversity in North America increased and shifted northward between 1966 and 2010. This change in the relationship of diversity to the latitudinal gradient is primarily influenced by range expansions of species that winter in the eastern United States as opposed to species which migrate to this area from wintering grounds in the tropics. This increase in diversity and its northward expansion is best explained by an increase in regional prebreeding season temperature over the past 44 years.

Biodiversity monitoring at the Tonle Sap Lake of Cambodia: a comparative assessment of local methods

This paper assesses local biodiversity monitoring methods practiced in the Tonle Sap Lake of Cambodia. For the assessment we used the following criteria: methodological rigor, perceived cost, ease of use (user friendliness), compatibility with existing activities, and effectiveness of intervention. Constraints and opportunities for execution of the methods were also considered. Information was collected by use of: (1) key informant interview, (2) focus group discussion, and (3) researcher's observation. The monitoring methods for fish, birds, reptiles, mammals and vegetation practiced in the research area have their unique characteristics of generating data on biodiversity and biological resources. Most of the methods, however, serve the purpose of monitoring biological resources rather than biodiversity. There is potential that the information gained through local monitoring methods can provide input for long-term management and strategic planning. In order to realize this potential, the local monitoring methods should be better integrated with each other, adjusted to existing norms and regulations, and institutionalized within community-based organization structures.

Population size influences amphibian detection probability: implications for biodiversity monitoring programs

Monitoring is an integral part of species conservation. Monitoring programs must take imperfect detection of species into account in order to be reliable. Theory suggests that detection probability may be determined by population size but this relationship has not yet been assessed empirically. Population size is particularly important because it may induce heterogeneity in detection probability and thereby cause bias in estimates of biodiversity. We used a site occupancy model to analyse data from a volunteer-based amphibian monitoring program to assess how well different variables explain variation in detection probability. An index to population size best explained detection probabilities for four out of six species (to avoid circular reasoning, we used the count of individuals at a previous site visit as an index to current population size). The relationship between the population index and detection probability was positive. Commonly used weather variables best explained detection probabilities for two out of six species. Estimates of site occupancy probabilities differed depending on whether the population index was or was not used to model detection probability. The relationship between the population index and detectability has implications for the design of monitoring and species conservation. Most importantly, because many small populations are likely to be overlooked, monitoring programs should be designed in such a way that small populations are not overlooked. The results also imply that methods cannot be standardized in such a way that detection probabilities are constant. As we have shown here, one can easily account for variation in population size in the analysis of data from long-term monitoring programs by using counts of individuals from surveys at the same site in previous years. Accounting for variation in population size is important because it can affect the results of long-term monitoring programs and ultimately the conservation of imperiled species.