A quantitative global review of species population monitoring

Climate-Induced Physiological Stress Drives Rainforest Mammal Population Declines

Climate change is a major driver of global biodiversity loss, yet the precise mechanisms linking climate change to population declines remain poorly understood. We developed a novel, broadly applicable framework that integrates biophysical, nutritional, and population modeling to capture fundamental physiological constraints on mammalian herbivores and applied it to investigate the causes of declines in ringtail possums of the Australian Wet Tropics (Pseudochirops archeri and Hemibelideus lemuroides). Our approach bridges the gap between mechanistic ("bottom-up") models, which simulate species' responses based solely on their traits and local microclimates, and the more common ("top-down") statistical models, which infer species' responses from occurrence or abundance data and standard environmental variables. We quantified population dynamics over a 30-year period by generating species-specific estimates of temperature and water stress, foraging limitations, and linking these with annual monitoring and nutritional quality within an open population model. Our findings demonstrate that climate change has impacted populations through physiological stress, but in a species-specific manner. Both species have experienced population collapses at lower elevations and in low-nutritional sites. For P. archeri, we found evidence that population changes were driven by reduced survival due to overheating and dehydration, alongside diminished recruitment from limited foraging. In contrast, our model suggests that H. lemuroides populations were primarily affected by foraging constraints, emphasizing the importance of considering climate-driven limitations on foraging activity in addition to direct physiological stress. These mechanistic insights offer a foundation for targeted conservation strategies to mitigate the impacts of climate pressures on wild populations.

Unrecorded Butterfly Species and Potential Local Extinctions: The Role of Citizen Science and Sampling

Estimating species extinction risk is crucial to reverse biodiversity loss and to adopt proper conservation measures. Different sources may play a pivotal role in prioritising species conservation. Recently, citizen science demonstrated a substantial role, especially when it comes to butterflies. This study examines species records and richness in Aosta Valley, which represents one of the highest mountain areas in Europe. Through 30,351 data points from 1825 to 2022, the impact and efficiency of three groups of data sources were investigated: literature (i.e., publications and collections), sampling (butterfly experts' recording), and citizen science (open-source databases). The study also aims to assess the extinction potential of the butterflies in relation to functional traits. The results showed that even if there were significant differences in the number of records between the three sources, there were no significant differences for species recorded. Moreover, 2.9% of the butterfly community risks extinction, and it is related to some response traits. Indeed, extinction risks increase when the altitudinal range decreases and for multivoltines. In conclusion, citizen science has a strong impact on the amount of data and could be exploited to fill data gaps at low/medium altitudes. However, professional sampling is needed to focus on species no longer reported, and in particular on species that are difficult to identify, have specific distributions or particular traits (e.g., limited altitudinal range). Using different data sources, extinction risk estimation, and trait analysis, it is possible to prioritise studies on some species using different efforts (sampling and/or citizen sciences).

Lost for more than 85 years-rediscovery of Channa amphibeus (McClelland, 1845), the world's most elusive snakehead species (Teleostei, Labyrinthici, Channidae)

The Chel Snakehead or Bora Chung, Channa amphibeus, is a large snakehead species endemic to the Himalayan region of India. Channa amphibeus was last recorded from specimens collected in the years between 1918 and 1933 (Shaw & Shebbeare 1938), leading to speculations that it may have gone extinct. Here, we report the re-discovery of the species after more than 85 years, based on three specimens collected in the year 2024, and additional photographic records. We also provide the first live image of C. amphibeus, along with biometric, and genetic data.

The utility of the Living Planet Index as a policy tool and for measuring nature recovery

The Living Planet Index (LPI) is a leading global biodiversity indicator based on vertebrate population time series. Since it was first developed over 25 years ago, the LPI has been widely used to indicate trends in biodiversity globally, primarily reported every two years in the Living Planet Report. Based on relative abundance, a sensitive metric of biodiversity change, the LPI has also been applied as a tool for informing policy and used in assessments for several multilateral conventions and agreements, including the Convention on Biological Diversity 2010 Biodiversity Target and Aichi targets. Here, we outline all current and some potential uses of the LPI as a policy tool and explore the use of the LPI in policy documents to assess the reach of the LPI geographically and over time. We present limitations to the use of this indicator in policy, primarily relating to the development of the index at the national level, and suggest clear pathways to broaden the utility of the LPI and the underlying database for temporal and spatial predictions of biodiversity change. We also provide evidence that the LPI can detect recoveries in biodiversity and suggest its suitability for measuring progress towards the goal of biodiversity recovery by 2050.This article is part of the discussion meeting issue 'Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future'.

Understanding the effects of weather parameters on the population dynamics of an endangered geophyte supports monitoring efficiency

Due to their complex life cycles geophytes are often neglected in conservation programs, despite they are important elements of early spring communities. Their life cycle is strongly affected by weather parameters, i.e. temperature, precipitation, and light, but the effects of these parameters are often contradictory and show high intra-annual variability even within species. Deeper knowledge about the abiotic factors affecting the population dynamics of geophytes is needed to support the designation of effective conservation plans. We aimed to explore the link between weather parameters and population dynamics of Colchicum bulbocodium, an endangered and strictly protected geophyte. We monitored three life cycle stages (flowering, growing, fruiting) of 1069 individuals in permanent plots for six consecutive years. Our results showed that life cycle of C. bulbocodium was strongly related to the actual weather parameters; the lagged effect of the previous year was weaker. Increasing temperature and lack of cold periods had negative effect on all life stages. We highlighted that population estimation based on the number of flowering individuals in a single year can underestimate population size by 40-83%. Monitoring in years following wet and cold springs and cold winters could increase the accuracy of population estimations of the flowering individuals.

Comparing Genetic Ne Reconstructions Over Time With Long-Time Wolf Monitoring Data in Two Populations

Many methods are now available to calculate N e , but their performance varies depending on assumptions. Although simulated data are useful to discover certain types of bias, real empirical data supported by detailed known population histories allow us to discern how well methods perform with actual messy and complex data. Here, we focus on two genomic data sets of grey wolf populations for which population size changes of the past 40-120 years are well documented. We use this background to explore in what detail we can retrieve the known population history from these populations, in the light of pitfalls relating to population history, sampling design and the change in the spatial scale at which N e is estimated as we go further back in time. The Scandinavian wolf population was founded in the early 1980s from a few individuals and has gradually expanded up to 510 wolves. Although the founder event of the Scandinavian population was detected by GONE, the founding effective population size was strongly overestimated when the most recent samples were used, but less so when older samples were considered. Nevertheless, the present-day N e corresponds to theoretical expectations. The western Great Lakes wolf population of Minnesota is the only population in the contiguous United States that persisted throughout the 20th century, surviving intense persecution. We found a good concordance between the estimated N e and trends in census size data, but the reconstruction of N e clearly highlights the difficulty of interpreting results in spatially structured populations that underwent demographic fluctuations.

Assessing the information-content of messy data to reconstruct population recovery dynamics for the world's rarest primate

Understanding the dynamics of population recovery in threatened species requires robust longitudinal monitoring datasets. However, evidence-based decision-making is often impeded by variable data collection approaches, necessitating critical evaluation of restricted available baselines. The Hainan gibbon, the world's rarest primate, had possibly declined to only seven or eight individuals in 1978 at Bawangling National Nature Reserve but has experienced subsequent population growth. Past population estimates lack detailed reporting of survey effort, and multiple conflicting estimates are available, hindering assessment of gibbon recovery. We investigated all reported estimates of Bawangling gibbon population size from 1978 to 2022, to evaluate the biological signal of population trends and the extent to which noise associated with varying survey effort, reporting and estimation may mask or misrepresent any underlying signal. This longitudinal dataset demonstrates that the Bawangling population experienced a series of bottlenecks and recoveries, with three successive periods of growth interspersed by population crashes (1978-1989, 1989-2000 and 2000-2022). The rate of gibbon population recovery was progressively slower over time in each successive period of growth, and this potential decline in recovery rate following serial bottlenecks suggests that additional management strategies may be required alongside "nature-based solutions" for this species. However, population viability analysis suggests the 1978 founder population is unlikely to have been as low as seven individuals, raising concerns for interpreting reported historical population counts and understanding the dynamics of the species' recovery. We caution against overinterpreting potential signals within "messy" conservation datasets, and we emphasise the crucial importance of standardised replicable survey methods and transparent reporting of data and effort in all future surveys of Hainan gibbons and other highly threatened species.

Harnessing online digital data in biodiversity monitoring

Online digital data from media platforms have the potential to complement biodiversity monitoring efforts. We propose a strategy for integrating these data into current biodiversity datasets in light of the Kunming-Montreal Global Biodiversity Framework.

What factors influence the rediscovery of lost tetrapod species?

We created a database of lost and rediscovered tetrapod species, identified patterns in their distribution and factors influencing rediscovery. Tetrapod species are being lost at a faster rate than they are being rediscovered, due to slowing rates of rediscovery for amphibians, birds and mammals, and rapid rates of loss for reptiles. Finding lost species and preventing future losses should therefore be a conservation priority. By comparing the taxonomic and spatial distribution of lost and rediscovered tetrapod species, we have identified regions and taxa with many lost species in comparison to those that have been rediscovered-our results may help to prioritise search effort to find them. By identifying factors that influence rediscovery, we have improved our ability to broadly distinguish the types of species that are likely to be found from those that are not (because they are likely to be extinct). Some lost species, particularly those that are small and perceived to be uncharismatic, may have been neglected in terms of conservation effort, and other lost species may be hard to find due to their intrinsic characteristics and the characteristics of the environments they occupy (e.g. nocturnal species, fossorial species and species occupying habitats that are more difficult to survey such as wetlands). These lost species may genuinely await rediscovery. However, other lost species that possess characteristics associated with rediscovery (e.g. large species) and that are also associated with factors that negatively influence rediscovery (e.g. those occupying small islands) are more likely to be extinct. Our results may foster pragmatic search protocols that prioritise lost species likely to still exist.

Rice Fields as Important Habitats for Three Anuran Species-Significance and Implications for Conservation

Agriculture can have different effects on wildlife depending on land-use type and extensive/intensive practices. The aim of this study was to establish the significance of rice fields in Central Bulgaria as anuran habitats. We used Audiomoth acoustic loggers to record calling activity at three sites near the city of Plovdiv-one shallow pond and two rice fields-from June to September in 2022 and 2023. We registered the three most widespread species in the region-Bufotes viridis, Hyla orientalis and Pelophylax ridibundus-and created pattern-matching models for them using the free web interface Arbimon, which enabled us to perform presence/absence counts and abundance estimates. At the beginning and the end of the rice vegetation period, water samples were collected and analysed for 62 substances. Substance concentrations were compared between ponds and with LC50 data from the available literature. We registered 19 and 21 substances in 2022 and 2023, respectively, with concentrations within the accepted limits, and although some metals were near risk levels, this was not reflected in the presence counts or the abundance estimates. The results indicated that frog activity was not related to any of the registered substances, but that it was positively correlated with daily rainfall and was higher in the rice fields.

Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity

Data deficiencies among rare or cryptic species preclude assessment of community-level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures. A recent surge in both public science and government-funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well-structured, design-based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis. Hierarchical modelling, including single-species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging 'integrated community modelling' framework that combines both data integration and community modelling to improve inferences on species- and community-level dynamics. We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community-level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula-based interfaces and through development of custom code in JAGS, NIMBLE and Stan. Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community-level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity.

Bayesian areal disaggregation regression to predict wildlife distribution and relative density with low-resolution data

For species of conservation concern and human-wildlife conflict, it is imperative that spatial population data be available to design adaptive-management strategies and be prepared to meet challenges such as land use and climate change, disease outbreaks, and invasive species spread. This can be difficult, perhaps impossible, if spatially explicit wildlife data are not available. Low-resolution areal counts, however, are common in wildlife monitoring, that is, the number of animals reported for a region, usually corresponding to administrative subdivisions, for example, region, province, county, departments, or cantons. Bayesian areal disaggregation regression is a solution to exploit areal counts and provide conservation biologists with high-resolution species distribution predictive models. This method originated in epidemiology but lacks experimentation in ecology. It provides a plethora of applications to change the way we collect and analyze data for wildlife populations. Based on high-resolution environmental rasters, the disaggregation method disaggregates the number of individuals observed in a region and distributes them at the pixel level (e.g., 5 × 5 km or finer resolution), thereby converting low-resolution data into a high-resolution distribution and indices of relative density. In our demonstrative study, we disaggregated areal count data from hunting bag returns to disentangle the changing distribution and population dynamics of three deer species (red, sika, and fallow) in Ireland from 2000 to 2018. We show an application of the Bayesian areal disaggregation regression method and document marked increases in relative population density and extensive range expansion for each of the three deer species across Ireland. We challenged our disaggregated model predictions by correlating them with independent deer surveys carried out in field sites and alternative deer distribution models built using presence-only and presence-absence data. Finding a high correlation with both independent data sets, we highlighted the ability of Bayesian areal disaggregation regression to accurately capture fine-scale spatial patterns of animal distribution. This study uncovers new scenarios for wildlife managers and conservation biologists to reliably use regional count data disregarded so far in species distribution modeling. Thus, it represents a step forward in our ability to monitor wildlife population and meet challenges in our changing world.

Estimating effective survey duration in camera trap distance sampling surveys

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.

Exploring the vegetation of the coastal road in Puerto Cisnes, southern Chile: a vascular plant inventory

Background

In areas of low disturbance, such as the Aysén Region of Chile, the presence of roads can inadvertently facilitate the spread of invasive species. To address this issue, it is imperative to maintain up-to-date biological inventories, as they serve as a primary source of information for the conservation of species and ecosystems. However, the maintenance of systematic inventories of vascular plants in Chile is virtually non-existent, especially outside protected wilderness areas. The data we have come from an inventory of vascular plant species along a stretch of coastal road in Puerto Cisnes (Aysén Region), characterised by a cut slope in the rock. The site is located between mountain ranges, in a region known for its protected wilderness areas and low levels of anthropogenic alteration. The study adopted an observational sampling design, using the road as a transect. For each species identified, the growth substrate, habit and dispersal mode were recorded. A total of 70 species (36 herbs, 23 shrubs and 11 trees) belonging to 42 families were found. The most represented families were Hymenophyllaceae (nine species) and Myrtaceae (four species). We recorded nine introduced species belonging to seven botanical families (Cirsiumvulgare (Savi) Ten., Crocosmiacrocosmiiflora (Lemoine ex Burb. & Dean) N.E.Br., Cytisusscoparius (L.) Link, Digitalispurpurea L., Lotuspedunculatus Cav., Plantagolanceolata L., Polygonumcampanulatum Hook. f., Prunellavulgaris L., Rubusconstrictus Lefèvre & P.J.Müll). Of these nine species, seven are invasive, while the remaining two species have not been assessed for invasive potential (i.e. Crocosmiacrocosmiiflora and Polygonumcampanulatum). In particular, Crocosmiacrocosmiiflora and Rubusconstrictus are new regional records. The majority of species were found growing on the ground (44 species), while a significant proportion were found exclusively on rocky slopes (17 species). According to their seed dispersal mechanism, the most common syndromes were anemochory (32 species) and ornithochory (20 species). Other mechanisms such as mammalochory, ballochory or myrmecochory were less common (less than four species).

New information

This study provides valuable data on the vascular flora of Puerto Cisnes, Chile, a modest human settlement in a minimally altered landscape. The region, dominated by native forests and a burgeoning salmon farming industry, has few inventories, so the database presented here adds significantly to local botanical knowledge. The main novelty of this research is that it is the first inventory carried out on a road in a slightly altered area surrounded by protected wilderness areas (such as Magdalena Island National Park and Queulat National Park). The study systematically categorises species according to substrate, habitat and dispersal mode, dimensions that are rarely combined in a single database.The inventory identifies 70 species (36 herbs, 23 shrubs and 11 trees) in 42 families. The most represented families were Hymenophyllaceae (with nine species) and Myrtaceae (with four species). Additionally, we recorded, two introduced species (Crocosmiacrocosmiiflora and Rubusconstrictus) at least 100 km south of their known distribution.

Past, present, and future of the Living Planet Index

As we enter the next phase of international policy commitments to halt biodiversity loss (e.g., Kunming-Montreal Global Biodiversity Framework), biodiversity indicators will play an important role in forming the robust basis upon which targeted, and time sensitive conservation actions are developed. Population trend indicators are one of the most powerful tools in biodiversity monitoring due to their responsiveness to changes over short timescales and their ability to aggregate species trends from global down to sub-national or even local scale. We consider how the project behind one of the foremost population level indicators - the Living Planet Index - has evolved over the last 25 years, its value to the field of biodiversity monitoring, and how its components have portrayed a compelling account of the changing status of global biodiversity through its application at policy, research and practice levels. We explore ways the project can develop to enhance our understanding of the state of biodiversity and share lessons learned to inform indicator development and mobilise action.

Combining multiple data sources with different biases in state-space models for population dynamics

The resolution at which animal populations can be modeled can be increased when multiple datasets corresponding to different life stages are available, allowing, for example, seasonal instead of annual descriptions of dynamics. However, the abundance estimates used for model fitting can have multiple sources of error, both random and systematic, namely bias. We focus here on the consequences of, and how to address, differing and unknown observation biases when fitting models.State-space models (SSMs) separate process variation and observation error, thus providing a framework to account for different and unknown estimate biases across multiple datasets. Here we study the effects on the inference of including or excluding bias parameters for a sequential life stage population dynamics SSM using a combination of theory, simulation experiments, and an empirical example.When the data, that is, abundance estimates, are unbiased, including bias parameters leads to increased imprecision compared to a model that correctly excludes bias parameters. But when observations are biased and no bias parameters are estimated, recruitment and survival processes are inaccurately estimated and estimates of process variance become biased high. These problems are substantially reduced by including bias parameters and fixing one of them at even an incorrect value. The primary inferential challenge is that models with bias parameters can show properties of being parameter redundant even when they are not in theory.Combining multiple datasets into a single analysis by using bias parameters to rescale data can offer significant improvements to inference and model diagnostics. Because their estimability in practice is dataset specific and will likely require more precise estimates than might be expected from ecological datasets, we outline some strategies for characterizing process uncertainty when it is confounded by bias parameters.

Sampling from commercial vessel routes can capture marine biodiversity distributions effectively

Collecting fine-scale occurrence data for marine species across large spatial scales is logistically challenging but is important to determine species distributions and for conservation planning. Inaccurate descriptions of species ranges could result in designating protected areas with inappropriate locations or boundaries. Optimizing sampling strategies therefore is a priority for scaling up survey approaches using tools such as environmental DNA (eDNA) to capture species distributions. In a marine context, commercial vessels, such as ferries, could provide sampling platforms allowing access to undersampled areas and repeatable sampling over time to track community changes. However, sample collection from commercial vessels could be biased and may not represent biological and environmental variability. Here, we evaluate whether sampling along Mediterranean ferry routes can yield unbiased biodiversity survey outcomes, based on perfect knowledge from a stacked species distribution model (SSDM) of marine megafauna derived from online data repositories. Simulations to allocate sampling point locations were carried out representing different sampling strategies (random vs regular), frames (ferry routes vs unconstrained), and number of sampling points. SSDMs were remade from different sampling simulations and compared with the "perfect knowledge" SSDM to quantify the bias associated with different sampling strategies. Ferry routes detected more species and were able to recover known patterns in species richness at smaller sample sizes better than unconstrained sampling points. However, to minimize potential bias, ferry routes should be chosen to cover the variability in species composition and its environmental predictors in the SSDMs. The workflow presented here can be used to design effective sampling strategies using commercial vessel routes globally for eDNA and other biodiversity survey techniques. This approach has potential to provide a cost-effective method to access remote oceanic areas on a regular basis and can recover meaningful data on spatiotemporal biodiversity patterns.

Defining biologically relevant and hierarchically nested population units to inform wildlife management

Wildlife populations are increasingly affected by natural and anthropogenic changes that negatively alter biotic and abiotic processes at multiple spatiotemporal scales and therefore require increased wildlife management and conservation efforts. However, wildlife management boundaries frequently lack biological context and mechanisms to assess demographic data across the multiple spatiotemporal scales influencing populations. To address these limitations, we developed a novel approach to define biologically relevant subpopulations of hierarchically nested population levels that could facilitate managing and conserving wildlife populations and habitats. Our approach relied on the Spatial "K"luster Analysis by Tree Edge Removal clustering algorithm, which we applied in an agglomerative manner (bottom-to-top). We modified the clustering algorithm using a workflow and population structure tiers from least-cost paths, which captured biological inferences of habitat conditions (functional connectivity), dispersal capabilities (potential connectivity), genetic information, and functional processes affecting movements. The approach uniquely included context of habitat resources (biotic and abiotic) summarized at multiple spatial scales surrounding locations with breeding site fidelity and constraint-based rules (number of sites grouped and population structure tiers). We applied our approach to greater sage-grouse (Centrocercus urophasianus), a species of conservation concern, across their range within the western United States. This case study produced 13 hierarchically nested population levels (akin to cluster levels, each representing a collection of subpopulations of an increasing number of breeding sites). These closely approximated population closure at finer ecological scales (smaller subpopulation extents with fewer breeding sites; cluster levels ≥2), where >92% of individual sage-grouse's time occurred within their home cluster. With available population monitoring data, our approaches can support the investigation of factors affecting population dynamics at multiple scales and assist managers with making informed, targeted, and cost-effective decisions within an adaptive management framework. Importantly, our approach provides the flexibility of including species-relevant context, thereby supporting other wildlife characterized by site fidelity.

Measuring the Impact of Conservation: The Growing Importance of Monitoring Fauna, Flora and Funga

Many stakeholders, from governments to civil society to businesses, lack the data they need to make informed decisions on biodiversity, jeopardising efforts to conserve, restore and sustainably manage nature. Here we review the importance of enhancing biodiversity monitoring, assess the challenges involved and identify potential solutions. Capacity for biodiversity monitoring needs to be enhanced urgently, especially in poorer, high-biodiversity countries where data gaps are disproportionately high. Modern tools and technologies, including remote sensing, bioacoustics and environmental DNA, should be used at larger scales to fill taxonomic and geographic data gaps, especially in the tropics, in marine and freshwater biomes, and for plants, fungi and invertebrates. Stakeholders need to follow best monitoring practices, adopting appropriate indicators and using counterfactual approaches to measure and attribute outcomes and impacts. Data should be made openly and freely available. Companies need to invest in collecting the data required to enhance sustainability in their operations and supply chains. With governments soon to commit to the post-2020 global biodiversity framework, the time is right to make a concerted push on monitoring. However, action at scale is needed now if we are to enhance results-based management adequately to conserve the biodiversity and ecosystem services we all depend on.

Abundance decline in the avifauna of the European Union reveals cross-continental similarities in biodiversity change

Although global assessments provide evidence of biodiversity decline, some have questioned the strength of the evidence, with local assemblage studies often showing a more balanced picture of biodiversity change. The multifaceted nature of biodiversity and imperfect monitoring datasets may partially explain these findings. Here, using an extensive dataset, we find significant biodiversity loss in the native avifauna of the European Union (EU). We estimate a decline of 17-19% in the overall breeding bird abundance since 1980: a loss of 560-620 million individual birds. Both total and proportional declines in bird numbers are high among species associated with agricultural land. The distribution of species' population growth rates (ln) is centered close to zero, with numerical decline driven by substantial losses in abundant species. Our work supports previous assessments indicating substantial recent biodiversity loss and calls to reduce the threat of extinctions and restore species' abundances, for the sake of nature and people.

Bat point counts: A novel sampling method shines light on flying bat communities

Emerging technologies based on the detection of electro-magnetic energy offer promising opportunities for sampling biodiversity. We exploit their potential by showing here how they can be used in bat point counts-a novel method to sample flying bats-to overcome shortcomings of traditional sampling methods, and to maximize sampling coverage and taxonomic resolution of this elusive taxon with minimal sampling bias. We conducted bat point counts with a sampling rig combining a thermal scope to detect bats, an ultrasound recorder to obtain echolocation calls, and a near-infrared camera to capture bat morphology. We identified bats with a dedicated identification key combining acoustic and morphological features, and compared bat point counts with the standard bat sampling methods of mist-netting and automated ultrasound recording in three oil palm plantation sites in Indonesia, over nine survey nights. Based on rarefaction and extrapolation sampling curves, bat point counts were similarly effective but more time-efficient than the established methods for sampling the oil palm species pool in our study. Point counts sampled species that tend to avoid nets and those that are not echolocating, and thus cannot be detected acoustically. We identified some bat sonotypes with near-infrared imagery, and bat point counts revealed strong sampling biases in previous studies using capture-based methods, suggesting similar biases in other regions might exist. Our method should be tested in a wider range of habitats and regions to assess its performance. However, while capture-based methods allow to identify bats with absolute and internal morphometry, and unattended ultrasound recorders can effectively sample echolocating bats, bat point counts are a promising, non-invasive, and potentially competitive new tool for sampling all flying bats without bias and observing their behavior in the wild.

Stepping up from wildlife disease surveillance to integrated wildlife monitoring in Europe

In a context of disease emergence and faced with the ever-growing evidence of the role of wildlife in the epidemiology of transmissible diseases, efforts have been made to develop wildlife disease surveillance (WDS) programs throughout Europe. Disease monitoring is ideally composed of "numerator data" (number of infected individuals) and "denominator data" (size of the target population). Too often however, information is available for only one. Hence, there is a need for developing integrated and harmonized disease and population monitoring tools for wildlife: integrated wildlife monitoring (IWM). IWM should have three components. Passive disease surveillance improves the likelihood of early detection of emerging diseases, while active surveillance and population monitoring are required to assess epidemiological dynamics, freedom of disease, and the outcome of interventions. Here, we review the characteristics of ongoing WDS in Europe, observe how pathogens have been ranked, and note a need for ranking host species, too. Then, we list the challenges for WDS and draw a roadmap for stepping up from WDS to IWM. There is a need to integrate and maintain an equilibrium between the three components of IWM, improve data collection and accessibility, and guarantee the adaptability of these schemes to each epidemiological context and temporal period. Methodological harmonization and centralization of information at a European level would increase efficiency of national programs and improve the follow-up of eventual interventions. The ideal IWM would integrate capacities from different stakeholder; allow to rapidly incorporate relevant new knowledge; and rely on stable capacities and funding.

Connecting Top-Down and Bottom-Up Approaches in Environmental Observing

Effective responses to rapid environmental change rely on observations to inform planning and decision-making. Reviewing literature from 124 programs across the globe and analyzing survey data for 30 Arctic community-based monitoring programs, we compare top-down, large-scale program driven approaches with bottom-up approaches initiated and steered at the community level. Connecting these two approaches and linking to Indigenous and local knowledge yields benefits including improved information products and enhanced observing program efficiency and sustainability. We identify core principles central to such improved links: matching observing program aims, scales, and ability to act on information; matching observing program and community priorities; fostering compatibility in observing methodology and data management; respect of Indigenous intellectual property rights and the implementation of free, prior, and informed consent; creating sufficient organizational support structures; and ensuring sustained community members’ commitment. Interventions to overcome challenges in adhering to these principles are discussed.

Differential reporting of biodiversity in two citizen science platforms during COVID-19 lockdown in Colombia

The COVID-19 pandemic highlighted the potential of using data from long-term citizen science projects to answer questions about the impacts of unexpected events on biodiversity. We evaluate the suitability of data from the citizen science platforms iNaturalist and eBird to describe the effects of the "anthropause" on biodiversity observation in Colombia. We compared record distribution according to human footprint, sampling behaviors, overall and conservation priority species composition during the strictest phase of the COVID-19 lockdown in 2020 to the same periods in 2015-2019. Overall participation in both platforms during the lockdown was high when compared to previous years, but records were concentrated on highly-transformed regions, had lower sampling efforts, and fewer species were recorded. For eBird, species composition was similar to that observed in previous years, and records of species of conservation concern declined in proportion to the decrease in overall species richness across samples. For iNaturalist, the species pool sampled each year remained too dissimilar for comparisons. Once differences in observer behaviors are accounted for, data from these platforms can be used in unplanned comparisons of relatively common species, in regions with high levels of human transformation, and at narrowly defined geographical contexts. To increase the potential of citizen science to monitor rarer species, more natural areas, or be used in large-scale analyses, we need to build and strengthen more diverse networks of observers that can further promote decentralization, democratization, and cost-effectiveness in biodiversity research.