Temporal insights into ecological community: Advancing waterbird monitoring with dome camera and deep learning

Biodiversity monitoring is critical for conservation and management. However, efficient species monitoring is often hindered by the complexities of ecological dynamics and the constraints of conventional techniques. This study presents an automated observation system by integrating dome camera with cascade neural networks (CNNs) to map the dynamics of waterbird communities across a semi-enclosed wetland in Dianchi Lake, southwestern China, a well-known important bird habitat. The trained model achieved performance with a Top-1 accuracy of 96.83 %, a Top-5 accuracy of 99.55 %, an F1 score of 93.54 %, a recall rate of 93.38 % and precision of 93.44 %, demonstrating its reliability for precise and well-balanced classification performance. Automatic and manual monitoring performed simultaneously showed highly significant correlations for community abundance (R2 = 0.89, n = 68, p < 0.0001), underscoring the value of this system as a tool for waterbird communty monitoring. Analysis of the monitoring results showed significant differences in species richness (n = 595, p < 0.0001) and community abundance (n = 595, p < 0.05) between morning and afternoon sessions, suggesting the need to consider ecological community changes across different time scales when conducting biodiversity surveys. In 2023, the system identified 17 species of birds in 5 orders, 6 families. By analyzing the data obtained from this system, the community composition, diversity changes, the arrival and departure times of waterbirds were revealed. Enabling high-frequency, continuous, and long-term monitoring at a lower cost, this system provides a reliable, alternative tool for developing species conservation and habitat management strategies.

Integrating multiple evidence streams to understand insect biodiversity change

Insects dominate animal species diversity yet face many threats from anthropogenic drivers of change. Many features of insect ecology make them a challenging group, and the fragmented state of knowledge compromises our ability to make general statements about their status. In this Review, we discuss the challenges of assessing insect biodiversity change. We describe how multiple lines of evidence-time series, spatial comparisons, experiments, and expert opinion-can be integrated to provide a synthesis overview of how insect biodiversity responds to drivers. Applying this approach will generate testable predictions of insect biodiversity across space, time, and changing drivers. Given the urgency of accelerating human impacts across the environment, this approach could yield a much-needed rapid assessment of insect biodiversity change.

The influence of COVID-19 on attention-deficit/hyperactivity disorder diagnosis and treatment rates across age, gender, and socioeconomic status: A 20-year national cohort study

Infection and lockdowns resulting from COVID-19 have been suggested to increase the prevalence and treatment rates of Attention Deficit/Hyperactivity Disorder (ADHD). To accurately estimate the pandemic's effects, pre-pandemic data can be used to estimate diagnosis and treatment rates during the COVID-19 years as if the COVID-19 pandemic did not occur. However, accurate predictions require a broad dataset, both in terms of the number of cases and the pre-pandemic timeframe. In the current study, we modeled monthly ADHD diagnosis and treatment rates over the 18 years preceding the COVID-19 pandemic. The dataset included ∼3 million cases for individuals aged 6 to 18 from the Clalit Health Services' electronic database. Using a trained model, we projected monthly rates for post-lockdown and post-infection periods, enabling us to estimate the expected diagnosis and treatment rates without the COVID-19 pandemic. We then compared these predictions to observed data, stratified by age groups, gender, and socioeconomic status. Our findings suggest no influence of the COVID-19 pandemic on ADHD diagnosis or treatment rates. We show that a narrower timeframe for pre-COVID-19 data points can lead to incorrect conclusions that COVID-19 affected ADHD diagnosis rates. Findings are discussed, given the assumed impact of the COVID-19 pandemic on ADHD.

When enough is enough: Optimising monitoring effort for large-scale wolf population size estimation in the Italian Alps

The ongoing expansion of wolf (Canis lupus) populations in Europe has led to a growing demand for up-to-date abundance estimates. Non-invasive genetic sampling (NGS) is now widely used to monitor wolves, as it allows individual identification and abundance estimation without physically capturing individuals. However, NGS is resource-intensive, partly due to the elusive behaviour and wide distribution of wolves, as well as the cost of DNA analyses. Optimisation of sampling strategies is therefore a requirement for the long-term sustainability of wolf monitoring programs. Using data from the 2020-2021 Italian Alpine wolf monitoring, we investigate how (i) reducing the number of samples genotyped, (ii) reducing the number of transects, and (iii) reducing the number of repetitions of each search transect impacted spatial capture-recapture population size estimates. Our study revealed that a 25% reduction in the number of transects or, alternatively, a 50% reduction in the maximum number of repetitions yielded abundance estimates comparable to those obtained using the entire dataset. These modifications would result in a 2046 km reduction in total transect length and 19,628 km reduction in total distance searched. Further reducing the number of transects resulted in up to 15% lower and up to 17% less precise abundance estimates. Reducing only the number of genotyped samples led to higher (5%) and less precise (20%) abundance estimates. Randomly subsampling genotyped samples reduced the number of detections per individual, whereas subsampling search transects resulted in a less pronounced decrease in both the total number of detections and individuals detected. Our work shows how it is possible to optimise wolf monitoring by reducing search effort while maintaining the quality of abundance estimates, by adopting a modelling framework that uses a first survey dataset. We further provide general guidelines on how to optimise sampling effort when using spatial capture-recapture in large-scale monitoring programmes.

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.

Optimal inventorying and monitoring of taxonomic, phylogenetic and functional diversity

Comparable data is essential to understand biodiversity patterns. While assemblage or community inventorying requires comprehensive sampling, monitoring focuses on as few components as possible to detect changes. Quantifying species, their evolutionary history, and the way they interact requires studying changes in taxonomic (TD), phylogenetic (PD) and functional diversity (FD). Here we propose a method for the optimization of sampling protocols for inventorying and monitoring assemblages or communities across these three diversity dimensions taking sampling costs into account. We used Iberian spiders and Amazonian bats as two case-studies. The optimal combination of methods for inventorying and monitoring required optimizing the accumulation curve of α-diversity and minimizing the difference between sampled and estimated β-diversity (bias), respectively. For Iberian spiders, the optimal combination for TD, PD and FD allowed sampling at least 50% of estimated diversity with 24 person-hours of fieldwork. The optimal combination of six person-hours allowed reaching a bias below 8% for all dimensions. For Amazonian bats, surveying all the 12 sites with mist-nets and 0 or 1 acoustic recorders was the optimal combination for almost all diversity types, resulting in >89% of the diversity and <10% bias with roughly a third of the cost. Only for phylogenetic α-diversity, the best solution was less clear and involved surveying both with mist nets and acoustic recorders. The widespread use of optimized and standardized sampling protocols and regular repetition in time will radically improve global inventory and monitoring of biodiversity. We strongly advocate for the global adoption of sampling protocols for both inventory and monitoring of taxonomic, phylogenetic and functional diversity.

Impact of infectious diseases on wild bovidae populations in Thailand: insights from population modelling and disease dynamics

The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times in a model gaur (Bos gaurus) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, haemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.

An increase in management actions has compensated for past climate change effects on desert locust gregarization in western Africa

In response to high population density, the desert locust, Schistocerca gregaria, becomes gregarious and forms swarms that can cause significant damage to crops and pastures, threatening food security of human populations from western Africa to India. This switch from solitary to gregarious populations is highly dependent on favorable weather conditions. Climate change, which has been hypothesized to shift conditions towards increasing risks of gregarization, is therefore likely to have significant impacts on the spatial distribution and likelihood of outbreak events. However, the desert locust is intensely managed at large scales, which possibly counteracts any increased risk of outbreaks due to a more favorable climate. Consequently, understanding the changes in risks in the future involves teasing out the effects of climate change and management actions. Here we studied the dynamics of gregarization at the very early stages of potential outbreaks, in parallel with trends in climate and management, between 1985 and 2018 in western Africa. We used three different spatial scales, with the goal to have a better understanding of the potential effects of climate change per se while controlling for management. Our first approach was to look at a regional scale, where we observed an overall decrease in gregarization events. However, this scale includes very heterogeneous environments and management efforts. To consider this heterogeneity, we divided the area into a grid of 0.5° cells. For each cell, a climate analysis was performed for rainfall and temperature, with trends obtained by a harmonic decomposition model on monthly data. Analyses of gregarization showed only a few significant trends, both positive and negative, mainly found in western Mauritania where management effort has increased. To improve the statistical power, these cells were then grouped into larger homogeneous climatic clusters, i.e. groups of cells with similar climatic conditions and similar climatic trends over the study period. At this scale, gregarization events depend on the intersection between climate conditions and management efforts. The clusters where gregarization increased were also the ones with the highest increase of management. These results highlight the important effect of preventive management, which may counteract the positive effects of climate change on locust proliferation.

Revealing uncertainty in the status of biodiversity change

Biodiversity faces unprecedented threats from rapid global change1. Signals of biodiversity change come from time-series abundance datasets for thousands of species over large geographic and temporal scales. Analyses of these biodiversity datasets have pointed to varied trends in abundance, including increases and decreases. However, these analyses have not fully accounted for spatial, temporal and phylogenetic structures in the data. Here, using a new statistical framework, we show across ten high-profile biodiversity datasets2-11 that increases and decreases under existing approaches vanish once spatial, temporal and phylogenetic structures are accounted for. This is a consequence of existing approaches severely underestimating trend uncertainty and sometimes misestimating the trend direction. Under our revised average abundance trends that appropriately recognize uncertainty, we failed to observe a single increasing or decreasing trend at 95% credible intervals in our ten datasets. This emphasizes how little is known about biodiversity change across vast spatial and taxonomic scales. Despite this uncertainty at vast scales, we reveal improved local-scale prediction accuracy by accounting for spatial, temporal and phylogenetic structures. Improved prediction offers hope of estimating biodiversity change at policy-relevant scales, guiding adaptive conservation responses.

Population status and genetic assessment of mugger (Crocodylus palustris) in a tropical regulated river system in North India

For rewilding the depleted crocodylian populations in India, a targeted 'one-species one area' based conservation approach was adopted in the early-1970s. Suitable habitats were identified and designated as protected areas, specifically targeted to recover a particular crocodylian species. A ~ 610 km stretch of Chambal River in the Ganga River Basin was declared as National Chambal Sanctuary to restore the 'Critically Endangered' gharial (Gavialis gangeticus), where active management of mugger (Crocodylus palustris) was discouraged. In the present study, we examined the population trends, occupancy, and genetic status of mugger by conducting population monitoring and genetic assessment to understand the status of potentially competitive mugger in the Sanctuary. Our finding suggests that the mugger population has notably increased and colonised the Sanctuary. We observed a moderate level of genetic diversity in the mugger, which was relatively higher compared to the gharial in the Sanctuary. The rapid colonization of ecological generalist mugger raises concerns about potential competition with ecological specialist gharial threatening its long-term sustainability. Considering the coexistence dynamics between the species, it is essential to extend adaptive management strategies for mugger to ensure successful recovery of gharial population in the Sanctuary.

Global monitoring for biodiversity: Uncertainty, risk, and power analyses to support trend change detection

Global targets aim to reverse biodiversity declines by 2050 but require knowledge of current trends and future projections under policy intervention. First, given uncertainty in measurement of current trends, we propose a risk framework, considering probability and magnitude of decline. While only 11 of 198 systems analyzed (taxonomic groups by country from the Living Planet Database) showed declining abundance with high certainty, 20% of systems had a 70% chance of strong declines. Society needs to decide acceptable risks of biodiversity loss. Second, we calculated statistical power to detect trend change using ~12,000 populations from 62 systems currently showing strong declines. Current trend uncertainty hinders our ability to assess improvements. Trend change is detectable with high certainty in only 14 systems, even if thousands of populations are sampled, and conservation action reduces net declines to zero immediately, on average. We provide potential solutions to improve monitoring of progress toward biodiversity targets.

Causes of maternal deaths in Sierra Leone from 2016 to 2019: analysis of districts' maternal death surveillance and response data

INTRODUCTION

Sierra Leone is among the top countries with the highest maternal mortality rates. Although progress has been made in reducing maternal mortality, challenges remain, including limited access to skilled care and regional disparities in accessing quality care. This paper presents the first comprehensive analysis of the burden of different causes of maternal deaths reported in the Maternal Death Surveillance and Response (MDSR) system at the district level from 2016 to 2019.

METHODS

The MDSR data are accessed from the Ministry of Health and Sanitation, and the secondary data analysis was done to determine the causes of maternal death in Sierra Leone. The proportions of each leading cause of maternal deaths were estimated by districts. A subgroup analysis of the selected causes of death was also performed.

RESULTS

Overall, obstetric haemorrhage was the leading cause of maternal death (39.4%), followed by hypertensive disorders (15.8%) and pregnancy-related infections (10.1%). Within obstetric haemorrhage, postpartum haemorrhage was the leading cause in each district. The burden of death due to obstetric haemorrhage slightly increased over the study period, while hypertensive disorders showed a slightly decreasing trend. Disparities were found among districts for all causes of maternal death, but no clear geographical pattern emerged. Non-obstetric complications were reported in 11.5% of cases.

CONCLUSION

The MDSR database provides an opportunity for shared learning and can be used to improve the quality of maternal health services. To improve the accuracy and availability of data, under-reporting must be addressed, and frontline community staff must be trained to accurately capture and report death events.

Short-term effects of community-based marine reserves on green abalone, as revealed by population studies

Marine reserves (MRs) are implemented worldwide to protect, restore, and manage marine ecosystems and species. However, it is important to document the positive effects those marine reserves have on slow-growth, temperate invertebrates such as abalone. Abalone, Haliotis spp., are marine gastropods of high economic value extracted worldwide for decades, which has led to fisheries-driven population decreases. In this work, we focused on a case study and assessed the short-term (1-2 years) effects of marine reserves established and managed by a local fishing cooperative at Guadalupe Island, Mexico. We evaluated the population status of green abalone, H. fulgens, by conducting (1) an assessment of the green abalone population around Guadalupe Island through subtidal monitoring and (2) an evaluation of the effect of two recently established marine reserves on population parameters such as the increase in density (individuals·m2), biomass, number of aggregated abalone, egg production, and proportion of individuals bigger than 150 mm (minimum harvest size) compared to fished areas. To assess the population around Guadalupe Island, we surveyed 11,160 m2 during 2020 and 2021. We recorded 2327 green abalones with a mean ± SE shell length of 135.978 ± 0.83 mm and a mean density of 0.21 ± 0.02 individuals·m2. All variables were statistically higher at the MRs except for shell length in 2021. In this work, we report for the first time the green abalone population status at Guadalupe Island and a positive short-term biological response to community-based marine reserves. This study suggests that a network of MRs combined with good management could help abalone populations in the short term in Guadalupe Island, potentially leading to more sustainable fishing practices and social-ecological resilience.

FISHGLOB_data: an integrated dataset of fish biodiversity sampled with scientific bottom-trawl surveys

Scientific bottom-trawl surveys are ecological observation programs conducted along continental shelves and slopes of seas and oceans that sample marine communities associated with the seafloor. These surveys report taxa occurrence, abundance and/or weight in space and time, and contribute to fisheries management as well as population and biodiversity research. Bottom-trawl surveys are conducted all over the world and represent a unique opportunity to understand ocean biogeography, macroecology, and global change. However, combining these data together for cross-ecosystem analyses remains challenging. Here, we present an integrated dataset of 29 publicly available bottom-trawl surveys conducted in national waters of 18 countries that are standardized and pre-processed, covering a total of 2,170 sampled fish taxa and 216,548 hauls collected from 1963 to 2021. We describe the processing steps to create the dataset, flags, and standardization methods that we developed to assist users in conducting spatio-temporal analyses with stable regional survey footprints. The aim of this dataset is to support research, marine conservation, and management in the context of global change.

Forecasting insect dynamics in a changing world

Predicting how insects will respond to stressors through time is difficult because of the diversity of insects, environments, and approaches used to monitor and model. Forecasting models take correlative/statistical, mechanistic models, and integrated forms; in some cases, temporal processes can be inferred from spatial models. Because of heterogeneity associated with broad community measurements, models are often unable to identify mechanistic explanations. Many present efforts to forecast insect dynamics are restricted to single-species models, which can offer precise predictions but limited generalizability. Trait-based approaches may offer a good compromise that limits the masking of the ranges of responses while still offering insight. Regardless of the modeling approach, the data used to parameterize a forecasting model should be carefully evaluated for temporal autocorrelation, minimum data needs, and sampling biases in the data. Forecasting models can be tested using near-term predictions and revised to improve future forecasts.

Bee Assemblage in the Southern Chihuahuan Desert: The Role of Season, Year, and Trap Color in Abundance

Recognizing how populations fluctuate over time is a crucial factor in determining the environmental elements affecting population persistence. However, the limited information on wild bee populations complicates the estimation of the impact of anthropogenic threats leading to changes in population size. To address this, we conducted a study capturing and monitoring nine species of wild bees through monthly samplings over four years. Tray traps were placed in permanent plots, and capture records were used to determine population size (N) and density (D). A generalized linear model (GLM) was employed to determine how the use of traps affected bee species captures. The families Apidae and Halictidae represented the most captures. Apis mellifera, the Lasioglossum (Dialictus spp.) complex, and Macrotera sinaloana exhibited the largest number of captures and highest population density. Most species (77.7%) showed a tendency to remain constant over the years and to have a higher number of captures in the spring months. Moreover, yellow traps were the most effective in capturing bee individuals. We suggest that the availability of essential resources and the reduction in environmental stressors positively affected the capture of wild bee populations.

Passive acoustic monitoring using smartphones reveals an alarming gibbon decline in a protected area in the central Annamite Mountains, Vietnam

Monitoring populations is critical for understanding how they respond to anthropogenic disturbance and for management of protected areas. The use of passive acoustic monitoring can improve monitoring efforts as it allows for collection of data on vocal animals at spatial and temporal scales that are difficult using only human observers. In this study, we used a multiseason occupancy model to monitor occurrence, apparent extinction, and colonization probabilities of a northern yellow-cheeked gibbon, Nomascus annamensis population with acoustic data collected from mobile smartphones in Dakrong Nature Reserve, Vietnam. Forty-five sites were randomly selected for repeated surveys in 2019 and 2022. At each site, a mobile smartphone was attached to a tree and recorded sounds for 4.2 days and 3.89 days on average, in 2019 and 2022, respectively. We manually annotated spectrograms for the presence of gibbon calls, and we detected gibbons at 24 and 12 recording posts in 2019 and 2022, respectively. Estimated local apparent extinction from occupancy models was high with 67% of occupied sites in 2019 becoming unoccupied in 2022. Apparent colonization was low with ~25% of unoccupied sites in 2019 becoming occupied in 2022. As a result, the apparent occurrence probability declined from 0.58 in 2019 to 0.30 in 2022. If the absence of calls indicates that cells are unoccupied this would mean an alarming decline of the gibbon population in the nature reserve. We suggest that in the areas with high hunting pressure, monitoring intervals should be shortened to at least yearly. In addition, urgent actions, such as patrolling, or gun confiscation, should be implemented to conserve the gibbon populations in Dakrong Nature Reserve and other protected areas with the same management context.

A general framework to quantify and compare ecological impacts under temporal dynamics

Biodiversity is diminishing at alarming rates due to multiple anthropogenic drivers. To mitigate these drivers, their impacts must be quantified accurately and comparably across drivers. To enable that, we present a generally applicable framework introducing fundamental principles of ecological impact quantification, including the quantification of interactions between multiple drivers. The framework contrasts biodiversity variables in impacted against those in unimpacted or other reference situations while accounting for their temporal dynamics through modelling. Properly accounting for temporal dynamics reduces biases in impact quantification and comparison. The framework addresses key questions around ecological impacts in global change science, namely, how to compare impacts under temporal dynamics across stressors, how to account for stressor interactions in such comparisons, and how to compare the success of management actions over time.

Trait analysis in a population of the Greater Butterfly-orchid observed through a 16-year period

A large English population of the temperate tuberous Greater Butterfly-orchid, Platanthera chlorantha, was monitored through a 16-year period. Each June the number of flowering plants was counted and 60 flowering plants were measured in situ for four morphological traits, selected for both ease of measurement and their contrasting contributions to the life history of the species. Trait data were tested annually in pairwise combinations for individual plants, before mean values throughout the study period were regressed and cross-correlated against each other and against local data for four meteorological parameters. Labellar spur length proved to be more constrained than either flower number or stem height, and rarely yielded statistically significant correlations with other traits, whereas the three remaining traits reliably showed modest but significant correlations. Mean values and coefficients of variation differed only modestly among years and showed few of any meaningful trends. Spring rainfall and insolation had no detectable effect on traits of plants flowering that June; instead, they impacted on trait expression during the following year, presumably as a result of differential resourcing of replacement tubers formed during the previous year. High spring rainfall in year t-1 increased leaf area and stem height in year t, whereas the widely fluctuating number of flowering plants was highest in years immediately following those characterised by relatively dry and/or sunny springs. The "decision" to flower is taken during the previous summer, though it may be modified through winter/spring abortion of above-ground organs. The proportion of the population electing to flower is the only measured parameter that impacts significantly on annual reproductive output, emphasising the under-rated difficulty of evolving through directional selection. Any attempt to predict the behaviour of plant species in response to climate change must integrate information on demography with that on life history, habitat preference and intimate symbioses.

The recovery of European freshwater biodiversity has come to a halt

Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the most vulnerable to biodiversity loss1. Mitigation measures, including wastewater treatment and hydromorphological restoration, have aimed to improve environmental quality and foster the recovery of freshwater biodiversity2. Here, using 1,816 time series of freshwater invertebrate communities collected across 22 European countries between 1968 and 2020, we quantified temporal trends in taxonomic and functional diversity and their responses to environmental pressures and gradients. We observed overall increases in taxon richness (0.73% per year), functional richness (2.4% per year) and abundance (1.17% per year). However, these increases primarily occurred before the 2010s, and have since plateaued. Freshwater communities downstream of dams, urban areas and cropland were less likely to experience recovery. Communities at sites with faster rates of warming had fewer gains in taxon richness, functional richness and abundance. Although biodiversity gains in the 1990s and 2000s probably reflect the effectiveness of water-quality improvements and restoration projects, the decelerating trajectory in the 2010s suggests that the current measures offer diminishing returns. Given new and persistent pressures on freshwater ecosystems, including emerging pollutants, climate change and the spread of invasive species, we call for additional mitigation to revive the recovery of freshwater biodiversity.

A framework for the detection and attribution of biodiversity change

The causes of biodiversity change are of great scientific interest and central to policy efforts aimed at meeting biodiversity targets. Changes in species diversity and high rates of compositional turnover have been reported worldwide. In many cases, trends in biodiversity are detected, but these trends are rarely causally attributed to possible drivers. A formal framework and guidelines for the detection and attribution of biodiversity change is needed. We propose an inferential framework to guide detection and attribution analyses, which identifies five steps-causal modelling, observation, estimation, detection and attribution-for robust attribution. This workflow provides evidence of biodiversity change in relation to hypothesized impacts of multiple potential drivers and can eliminate putative drivers from contention. The framework encourages a formal and reproducible statement of confidence about the role of drivers after robust methods for trend detection and attribution have been deployed. Confidence in trend attribution requires that data and analyses used in all steps of the framework follow best practices reducing uncertainty at each step. We illustrate these steps with examples. This framework could strengthen the bridge between biodiversity science and policy and support effective actions to halt biodiversity loss and the impacts this has on ecosystems. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Multidecadal data indicate increase of aquatic insects in Central European streams

In recent years, declining insect biodiversity has sparked interest among scientists and drawn the attention of society and politicians. However, our understanding of the extent of this decline is incomplete, particularly for freshwater insects that provide a key trophic link between aquatic and terrestrial ecosystems, but that are also especially vulnerable to climate change. To investigate the response of freshwater insects to climate change, we quantified shifts in insect abundance and diversity across 7264 samples covering Central Europe during 1990-2018 and related these changes to annual data on temperature and precipitation. We observed both increases in richness (10.6 %) and abundance (9.5 %) of freshwater insects over the past three decades. These changes were related to increases in summer temperature and summer precipitation, which had negative effects on species richness, and to increases in winter temperature and precipitation, which had positive effects. Further we found that increased temperature was generally related to increased abundance, whereas increased precipitation was associated with declines, thus highlighting the particularly varying impacts on differing insect orders. Given that freshwater insects have been more severely affected by global change than marine and terrestrial species, the observed increases are a positive sign, but the overall situation of freshwater invertebrates is still critical.

Long-term trends and drivers of biological invasion in Central European streams

Rates of biological invasion continue to accelerate and threaten the structure and function of ecosystems worldwide. High habitat connectivity, multiple pathways, and inadequate monitoring have rendered aquatic ecosystems vulnerable to species introductions. Past riverine invasion dynamics were largely restricted to large rivers, leaving out smaller rivers that commonly harbour high freshwater biodiversity. Moreover, biodiversity time series have rarely been used to investigate invasions across larger spatial-temporal scales, limiting our understanding of aquatic invasion dynamics. Here, we used 6067 benthic invertebrate samples from streams and small rivers from the EU Water Framework Directive monitoring program collected across Central Europe between 2000 and 2018 to assess temporal changes to benthic invertebrate communities as well as non-native species. We assessed invasion rates according to temperature, precipitation, elevation, latitude, longitude, and stream type. Overall, average daily temperatures significantly increased by 0.02 °C per annum (0.34 °C in total) while annual precipitation significantly decreased by 0.01 mm per annum (-67.8 mm over the study period), paralleled with significant increases in overall species richness (12.3 %) and abundance (14.9 %); water quality was relatively stable. Non-native species richness increased 5-fold and abundance 40-fold, indicating an ongoing community shift from native to non-native species. The observed increase in invasions was stronger in low mountain rivers compared to low mountain streams, with the share of non-native species abundance and richness declining with increasing elevation and latitude but increasing with temperature. We found thermophilic non-native species invasion success was greatest in larger sized streams, at lower latitudes, lower elevations and higher temperatures. These results indicate that widespread environmental characteristics (i.e., temperature) could heighten invasion success and confer refuge effects (i.e., elevation and latitude) in higher sites. High altitude and latitude environments should be prioritised for prevention efforts, while biosecurity and management should be improved in lowland areas subject to greater anthropogenic pressure, where non-native introductions are more likely.

Chilean long-term Socio-Ecological Research Network: progresses and challenges towards improving stewardship of unique ecosystems

Ecosystems provide a variety of benefits to human society and humanity’s utilization of ecosystems affects their composition, structure, and functions. Global change drivers demand us to study the interactions between ecological and social systems, and advise strategies to protect the large fraction of Chilean unique ecosystems. Long-term research and monitoring are vital for meaningful understanding of human impacts and socio-ecological feedback, which occur over multiple spatial and time-scales and can be invisible to traditional grant-sponsored short-term studies.

Despite the large fraction of unique ecosystems, Chilean government agencies have not established long-term monitoring programs to inform and guide management decisions for use, conservation, and adaptation to climate change. Responding to this void, the Chilean Long-Term Socio-Ecological Research Network (LTSER-Chile) was created, comprising nine study sites funded by a variety of private and public institutions, that broadly seeks to understand how global change is altering biodiversity and ecosystem functions. The LTSER-Chile is currently in a phase of institutional consolidation to achieve its objectives of alignment with international efforts, fill the need for high-quality, long-term data on social, biological and physical components of Chilean ecosystems, and develop itself as an open research platform for the world. Despite the wide diversity of ecosystems ecncompased by LTSER-Chile sites, several common variables are monitored, especially climatic and hydrographic variables and many ecological indicator variables that consider temporal fluctuations, population and community dynamics.

The main challenges currently facing the LTSER-Chile are to secure funding to maintain existing long-term monitoring programs, to persuade public and private decision-makers about its central role in informing and anticipating socio-ecological problems, and to achieve greater ecosystem representation by integrating new long-term study sites. This will require a more decisive political commitment of the State, to improve the stewardship of our unique terrestrial and marine ecosystems, and the realization that sound ecologically-sustainable policies will never be possible without a national monitoring network. We argue that the State should build on LTSER and several other private and university initiatives to provide the country with a monitoring network. In the absence of this commitment, the LTSER system is subject to discontinuity and frequent interruptions, which jeopardizes the long-term effort to understand the functioning of nature and its biodiversity.

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.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

Spatial and temporal changes in moth assemblages along an altitudinal gradient, Jeju-do island

Montane species on islands attract attention due to their small and isolated populations and limited dispersal potential, making them vulnerable to extinction. We investigated the diversity pattern of moth assemblages over the 12-years-period (2009-2020) at 11 study plots on an island mountain (Mount Hallasan, Jeju-do Island, South Korea) to assess the changes in the moth assemblages in terms of species composition, richness, and abundances. We expected to find a decline in the number of species at these sites, given the reported decline in similar taxa in other temperate regions, such as Europe and North America. In contrast, we found that the numbers of species and individuals of moth populations on the island mountain have not significantly changed, except at the high-elevation sites, where the number of species has increased. Our results also show that the numbers of species and individuals are closely related to energy availability, actual evapotranspiration. Moreover, we found that the species composition during the study period has not been greatly changed, except at the lowermost and uppermost elevations. The mechanism driving this high dissimilarity of moth assemblages differed: the low-elevation site experienced high temporal turnover, and the high elevation sites also experienced high temporal turnover and nestedness resulting from active species replacement due to a recent forest fire and vegetation changes and the geographic and ecological constraints of the high elevations. To date, the moth species diversity of the temperate forests of the island mountain is not showing a drastic change. However, we observed that the moth assemblages had changed the number of species and individuals at low and high elevations. Given the biological and ecological limitations of moths (ectothermic organisms with limited habitat range) and considering the results of our study, we infer that climate change has impacted the diversity and species composition of moths on the island mountain.

Longer study length, standardized sampling techniques, and broader geographic scope leads to higher likelihood of detecting stable abundance patterns in long term black-legged tick studies

Background

Understanding how study design and monitoring strategies shape inference within, and synthesis across, studies is critical across biological disciplines. Many biological and field studies are short term and limited in scope. Monitoring studies are critical for informing public health about potential vectors of concern, such as Ixodes scapularis (black-legged ticks). Black-legged ticks are a taxon of ecological and human health concern due to their status as primary vectors of Borrelia burgdorferi, the bacteria that transmits Lyme disease. However, variation in black-legged tick monitoring, and gaps in data, are currently considered major barriers to understanding population trends and in turn, predicting Lyme disease risk. To understand how variable methodology in black-legged tick studies may influence which population patterns researchers find, we conducted a data synthesis experiment.

Materials and Methods

We searched for publicly available black-legged tick abundance dataset that had at least 9 years of data, using keywords about ticks in internet search engines, literature databases, data repositories and public health websites. Our analysis included 289 datasets from seven surveys from locations in the US, ranging in length from 9 to 24 years. We used a moving window analysis, a non-random resampling approach, to investigate the temporal stability of black-legged tick population trajectories across the US. We then used t-tests to assess differences in stability time across different study parameters.

Results

All of our sampled datasets required 4 or more years to reach stability. We also found several study factors can have an impact on the likelihood of a study reaching stability and of data leading to misleading results if the study does not reach stability. Specifically, datasets collected via dragging reached stability significantly faster than data collected via opportunistic sampling. Datasets that sampled larva reached stability significantly later than those that sampled adults or nymphs. Additionally, datasets collected at the broadest spatial scale (county) reached stability fastest.

Conclusion

We used 289 datasets from seven long term black-legged tick studies to conduct a non-random data resampling experiment, revealing that sampling design does shape inferences in black-legged tick population trajectories and how many years it takes to find stable patterns. Specifically, our results show the importance of study length, sampling technique, life stage, and geographic scope in understanding black-legged tick populations, in the absence of standardized surveillance methods. Current public health efforts based on existing black-legged tick datasets must take monitoring study parameters into account, to better understand if and how to use monitoring data to inform decisioning. We also advocate that potential future forecasting initiatives consider these parameters when projecting future black-legged tick population trends.

Assessing conservation status with extensive but low-resolution data: Application of frequentist and Bayesian models to endangered Athabasca River rainbow trout

Use of extensive but low-resolution abundance data is common in the assessment of species at-risk status based on quantitative decline criteria under International Union for Conservation of Nature (IUCN) and national endangered species legislation. Such data can be problematic for 3 reasons. First, statistical power to reject the null hypothesis of no change is often low because of small sample size and high sampling uncertainty leading to a high frequency of type II errors. Second, range-wide assessments composed of multiple site-specific observations do not effectively weight site-specific trends into global trends. Third, uncertainty in site-specific temporal trends and relative abundance are not propagated at the appropriate spatial scale. A common result is the propensity to underestimate the magnitude of declines and therefore fail to identify the appropriate at-risk status for a species. We used 3 statistical approaches, from simple to more complex, to estimate temporal decline rates for a designatable unit (DU) of rainbow trout in the Athabasca River watershed in western Canada. This DU is considered a native species for purposes of listing because of its genetic composition characterized as >0.95 indigenous origin in the face of continuing introgressive hybridization with introduced populations in the watershed. Analysis of abundance trends from 57 time series with a fixed-effects model identified 33 sites with negative trends, but only 2 were statistically significant. By contrast, a hierarchical linear mixed model weighted by site-specific abundance provided a DU-wide decline estimate of 16.4% per year and a 3-generation decline of 93.2%. A hierarchical Bayesian mixed model yielded a similar 3-generation decline trend of 91.3% and the posterior distribution showed that the estimate had a >99% probability of exceeding thresholds for an endangered listing. We conclude that the Bayesian approach was the most useful because it provided a probabilistic statement of threshold exceedance in support of an at-risk status recommendation.

Monitoring partially marked populations using camera and telemetry data

Long-term monitoring is an important component of effective wildlife conservation. However, many methods for estimating density are too costly or difficult to implement over large spatial and temporal extents. Recently developed spatial mark-resight (SMR) models are increasingly being applied as a cost-effective method to estimate density when data include detections of both marked and unmarked individuals. We developed a generalized SMR model that can accommodate long-term camera data and auxiliary telemetry data for improved spatiotemporal inference in monitoring efforts. The model can be applied in two stages, with detection parameters estimated in the first stage using telemetry data and camera detections of instrumented individuals. Density is estimated in the second stage using camera data, with all individuals treated as unmarked. Serial correlation in detection and density parameters is accounted for using time-series models. The two-stage approach reduces computational demands and facilitates the application to large data sets from long-term monitoring initiatives. We applied the model to 3 years (2015-2017) of white-tailed deer (Odocoileus virginianus) data collected in three study areas of the Big Cypress Basin, Florida, USA. In total, 59 females marked with ear tags and fitted with GPS-telemetry collars were detected along with unmarked females on 180 remote cameras. Most of the temporal variation in density was driven by seasonal fluctuations, but one study area exhibited a slight population decline during the monitoring period. Modern technologies such as camera traps provide novel possibilities for long-term monitoring, but the resulting massive data sets, which are subject to unique sources of observation error, have posed analytical challenges. The two-stage spatial mark-resight framework provides a solution with lower computational demands than joint SMR models, allowing for easier implementation in practice. In addition, after detection parameters have been estimated, the model may be used to estimate density even if no synchronous auxiliary information on marked individuals is available, which is often the case in long-term monitoring.

A simple and viable approach to estimate population trends.. [DOI: 10.1101/2022.03.18.484446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Changes in population abundances over time are of central concern for environmental conservation and the understanding of population dynamics. The standard slope estimator has restricting assumptions and lacks a constant range, which makes its interpretation less intuitive. Herein, more robust measures of trend. It is based on proportional difference and can be used for data on various scale types. If it is applied to ranks it yields the correlation coefficient. Related measures of association are described that assess the relationship between species, including a rank-order correlation that is sensitive to top ranks and a correlation for continuous data that is more robust than the correlation coefficient. All proposed measures have a range between –1 and +1. Furthermore, they can provide a common ground for evaluating trend strength and strength of association for populations undergoing very different dynamics.

The statistical power to detect regional temporal trends in riverine contaminants in the Chesapeake Bay Watershed, USA

Chemical contamination of riverine ecosystems is largely a result of urbanization, industrialization, and agricultural activities occurring on adjacent terrestrial landscapes. Land management activities (e.g., Best Management Practices) are an important tool used to reduce point and non-point sources of pollution. However, the ability to confidently make inferences about the efficacy of land management activities on reducing in-stream chemical concentrations is poorly understood. We estimated regional temporal trends and components of variation for commonly used herbicides (atrazine and metolachlor), total estrogenicity, and riverine sediment concentrations of total PCBs for rivers in the Chesapeake Bay Watershed, USA. We then used the estimated variance components to perform a power analysis and evaluated the statistical power to detect regional temporal trends under different monitoring scenarios. Scenarios included varying the magnitude of the annual contaminant decline, the number of sites sampled each year, the number of years sampled, and sampling frequency. Monitoring for short time periods (e.g., 5 years) was inadequate for detecting regional temporal trends, regardless of the number of sites sampled or the magnitude of the annual declines. Even when monitoring over a 20-year period, sampling a relatively large number of sites each year was required (e.g., >50 sites) to achieve adequate statistical power for smaller trend magnitudes (declines of 5-7%/year). Annual sampling frequency had little impact on power for any monitoring scenario. All sampling scenarios were underpowered for sediment total PCBs. Power was greatest for total estrogenicity, suggesting that this aggregate measure of estrogenic activity may be a useful indicator. This study provides information that can be used to help (1) guide the development of monitoring programs aimed at detecting regional declines in riverine chemical contaminant concentrations in response to land management actions, and (2) set expectations for the ability to detect changes over time.

A quantitative global review of species population monitoring

Species monitoring, defined here as the repeated, systematic collection of data to detect long-term changes in the populations of wild species, is a vital component of conservation practice and policy. We created a database of nearly 1200 schemes, ranging in start date from 1800 to 2018, to review spatial, temporal, taxonomic, and methodological patterns in global species monitoring. We identified monitoring schemes through standardized web searches, an online survey of stakeholders, in-depth national searches in a sample of countries, and a review of global biodiversity databases. We estimated the total global number of monitoring schemes operating at 3300-15,000. Since 2000, there has been a sharp increase in the number of new schemes being initiated in lower- and middle-income countries and in megadiverse countries, but a decrease in high-income countries. The total number of monitoring schemes in a country and its per capita gross domestic product were strongly, positively correlated. Schemes that were active in 2018 had been running for an average of 21 years in high-income countries, compared with 13 years in middle-income countries and 10 years in low-income countries. In high-income countries, over one-half of monitoring schemes received government funding, but this was less than one-quarter in low-income countries. Data collection was undertaken partly or wholly by volunteers in 37% of schemes, and such schemes covered significantly more sites and species than those undertaken by professionals alone. Birds were by far the most widely monitored taxonomic group, accounting for around half of all schemes, but this bias declined over time. Monitoring in most taxonomic groups remains sparse and uncoordinated, and most of the data generated are elusive and unlikely to feed into wider biodiversity conservation processes. These shortcomings could be addressed by, for example, creating an open global meta-database of biodiversity monitoring schemes and enhancing capacity for species monitoring in countries with high biodiversity. Article impact statement: Species population monitoring for conservation purposes remains strongly biased toward a few vertebrate taxa in wealthier countries.

Seasonal dynamics of manganese accumulation in European larch (Larix decidua Mill.), silver birch (Betula pendula Roth), and bilberry (Vaccinium myrtillus L.) over 10 years of monitoring

Leaves of European larch, silver birch, and bilberry were sampled 5-7 times per growing season in 2010-2019 in a locality near the city of Litvínov in the Krušné Hory Mts. (Ore Mts.) near the Czech/German border. The locality is characterised by a large amount of plant-available Mn because of acidic soils in the study area. All three investigated plants at the studied site acquired manganese concentrations close to the definition of hyperaccumulation (ca. 10,000 mg kg^-1). This paper presents the most detailed collection of plant material for the characterisation of seasonal dynamics of Mn concentrations in the foliage of the three studied plants under field conditions and compares this information with that in published studies. Time (day in the year or day in the growing season) and cumulative precipitation anomalies were major and minor variables, respectively, explaining Mn dynamics in leaves, while temperature and insolation anomalies were not significant. The three investigated species showed plant-specific Mn acquisition rates in the growing season and specific effects of precipitation. Seasonal dynamics must be considered if plant leaves are used for environmental monitoring.