Tropical forests harbour some of the highest biodiversity on Earth but are undergoing rapid loss and degradation. In the Amazon more than one-third of forests have been altered through human activities, with major implications for wildlife communities. While Earth observation satellites effectively monitor forest cover at scale, it remains unclear how well satellite-derived variables capture variation in bird communities. We tested whether Landsat reflectance and vegetation indices can predict bird species occurrence and community composition in the Peruvian Amazon. We analysed 3,129 point counts and mist-net bird surveys conducted over 16 years in the Tambopata Forest, south-eastern Peru. As predictors we compared the effectiveness of remote sensing derived surface reflectance and vegetation indices (e.g., NDVI and tasselled cap), with traditional land-type and forest cover descriptors. Species occurrence probabilities and community composition of 135 frequently recorded bird species were estimated using multi-species occupancy models that account for imperfect detection. Models using Landsat reflectance and vegetation indices outperformed those based on habitat categories in predicting species occupancy (mean AUC = 0.68 vs 0.58). They also achieved high predictive accuracy (AUC > 0.7) for more species (49 compared with 20). However, low detection rates across surveys limited all models' ability to accurately estimate full community composition and to detect change over time. Our results demonstrate that satellite-derived variables can improve predictions of bird occurrence compared with habitat categories, but their effectiveness depends strongly on survey design and species detectability. Integrating remote sensing with well-structured field surveys provides a scalable approach to monitoring biodiversity trends in tropical forests.

Amphibians are experiencing global population declines, with the Natterjack Toad Epidalea calamita facing significant range contraction and breeding failures across its European range. Effective conservation of this priority species requires robust, long-term monitoring, yet traditional survey methods are often labour-intensive and limited in scope. This study presents the first application of passive acoustic monitoring (PAM) to assess Natterjack Toad breeding activity. Acoustic recorders were deployed at multiple breeding ponds within the Caerlaverock Wetland Centre, Scotland, over three seasons (2022-2024). Acoustic data revealed consistent diel patterns, with peak calling activity around 22:00, and a seasonal peak between late April and mid-May. Interannual variation in calling onset and intensity was observed, likely influenced by temperature and rainfall. Spatial variation in call detections highlighted key breeding sites and local differences in habitat suitability. Due to weather patterns and resourcing limitations, traditional surveys detected minimal evidence of breeding activity, preventing statistical correlation with acoustic data; however, this disparity underscores the value of PAM in detecting presence and breeding behaviour when other methods are constrained. Our findings demonstrate that PAM is a scalable, non-invasive tool capable of capturing fine-scale temporal and spatial patterns in amphibian activity. This approach offers significant potential for long-term monitoring, particularly under changing environmental conditions, and should be integrated into conservation strategies for Natterjack Toad and other threatened amphibians.

Abstract Carbon finance initiatives such as Reducing Deforestation and Forest Degradation (REDD+), designed to mitigate climate change, offer an opportunity to also protect biodiversity. However, managing forests to store and sequester carbon does not necessarily conserve biodiversity. We evaluated the biodiversity co‐benefits of the Gola‐REDD+ initiative in the tropical forests of Sierra Leone, using bioacoustics and DNA metabarcoding under a quasi‐experimental study design. We used soundscape saturation (SS) as a measure of vocalizing diversity, and e‐DNA arthropod community as a complementary measure of biodiversity to examine whether a Gola‐REDD+ financed protected area (Treatment‐PA) conserved biodiversity more than (1) a multiuse community land (Control‐CL) and (2) a PA without REDD+ finance (Control‐PA). We found that REDD+ financing is associated with additional biodiversity co‐benefits in the Treatment‐PA compared to both control areas. Our study makes three key contributions. First, we provide concrete evidence on a carbon finance (REDD+) project's effectiveness in conserving faunal diversity while sequestering carbon. Second, we present a gold‐standard causal inference study design for evaluating biodiversity co‐benefits of conservation strategies. Third, we highlight the role of conservation technologies like bioacoustics and DNA metabarcoding in informing conservation policy.

One mechanism for improving the resilience of freshwater systems affected by climate change is to use environmental water to support refugial habitats which allow species, ecosystems and functions to persist and recover after severe droughts. We applied systematic conservation planning (SCP) to prioritise wetlands and lakes with the aim of informing the delivery of environmental water for the creation and protection of refugia habitat in the Murray-Darling Basin, Australia. SCP uses a complimentary algorithm to generate planning solutions that protect all target ecological assets for the lowest "cost" of the management constraints considered. Here the ecological assets were 294 wetland dependant taxa including species of fish, frogs, dragonflies, crustacea, molluscs, and plants, 42 different ecosystem types and ecosystem productivity. Managements constraints included resistance to drying, condition and connectivity and the ease of environmental water delivery. Conservation inundation targets were aligned with the approximate annual delivery of environmental water by the Commonwealth Environmental Water Holder. We found that prioritisation of sites for enivronmental water was sensitive to the choice of target ecological assets and less so but to some extent the cost of management. We found environmental water delivery in the Basin is reaching refugial wetlands that support the majority of ecosystem and species diversity. However, certain taxonomic groups, such as invertebrates, are comparatively poorly represented. To effectively manage taxa, more data on ecological and life history traits is needed to better identify the spatial and temporal location of their refugia. This case study demonstrates that the SCP approach offers an objective and repeatable process for informing environmental water allocation and delivery, that could be applied to other basins globally.

DNA-based biodiversity surveys involve collecting physical samples from survey sites and assaying the contents in the laboratory to detect species via their diagnostic DNA sequences. DNA-based surveys are increasingly being adopted for biodiversity monitoring. The most commonly employed method is metabarcoding, which combines PCR with high-throughput DNA sequencing to amplify and then read `DNA barcode' sequences. This process generates count data indicating the number of times each DNA barcode was read. However, DNA-based data are noisy and error-prone, with several sources of variation. In this paper, we present a unifying modelling framework for DNA-based data allowing for all key sources of variation and error in the data-generating process. The model can estimate within-species biomass changes across sites and link those changes to environmental covariates, while accounting for species and sites correlation. Inference is performed using MCMC, where we employ Gibbs or Metropolis-Hastings updates with Laplace approximations. We also implement a re-parameterisation scheme, appropriate for crossed-effects models, leading to improved mixing, and an adaptive approach for updating latent variables, reducing computation time. We discuss study design and present theoretical and simulation results to guide decisions on replication at different stages and on the use of quality control methods. We demonstrate the new framework on a dataset of Malaise-trap samples. We quantify the effects of elevation and distance-to-road on each species, infer species correlations, and produce maps identifying areas of high biodiversity, which can be used to rank areas by conservation value. We estimate the level of noise between sites and within sample replicates, and the probabilities of error at the PCR stage, which are close to zero for most species considered, validating the employed laboratory processing.

Technological advances are enabling ecologists to conduct large‐scale and structured community surveys. However, it is unclear how best to extract information from these novel community data. We metabarcoded 48 vertebrate species from their eDNA in 320 ponds across England and applied the ‘internal structure' approach, which uses joint species distribution models (JSDMs) to explain compositions as the result of four metacommunity processes: environmental filtering, dispersal, species interactions, and stochasticity. We confirm that environmental filtering plays an important role in community assembly, and find that species' estimated environmental preferences are consistent with known ecologies. We also detect negative biotic covariances between fish and amphibians after controlling for divergent environmental preferences, consistent with predator–prey interactions (likely mediated by predator avoidance behaviour), and we detect high spatial autocorrelation for the palmate newt, consistent with its hypothesised relict distribution. Promisingly, ecologically and spatially distinctive sites are better explained by their environmental covariates and geographic locations, respectively, revealing sites where environmental filtering and dispersal limitation act more strongly. These results are consistent with the recent proposal that applying JSDMs to species distribution patterns can help reveal the relative importance of environmental filtering, dispersal limitation, and biotic interaction processes for individual sites and species. Our results also highlight the value of the modern interpretation of metacommunity ecology, which embraces the fact that assembly processes differ among species and sites. We discuss how novel community data allow for several study design improvements that will strengthen the inference of metacommunity assembly processes from observational data.

Climate change causes warmer and more variable temperatures globally, impacting physiological rates and function in ectothermic animals. Acclimation of physiological rates can help maintain function. However, it is unresolved how variance in physiological rates changes with temperature despite its potential ecological and evolutionary importance. We developed new effect sizes that capture how both the mean and variation in physiological rates change across temperature (based on the temperature coefficient, ) and used them to test how acclimation and acute thermal responses vary across aquatic and terrestrial ectotherms using meta‐analysis (>1900 effects from 226 species). Comparing both the magnitude of acclimation and changes in variation side‐by‐side provides unique opportunities for evaluating the importance of plasticity and selection under climate change. We show that variance in physiological rates increases at higher temperatures, but that the magnitude of change depends on habitat. Freshwater and marine ectotherms are capable of acclimation and have the greatest increase in variance. In contrast, terrestrial ectotherms have reduced acclimation abilities and smaller increases in physiological rate. Simulations suggest that these patterns may result from differences in among‐individual variation in thermal breadth and optima of performance curves across habitats. Our results highlight the greater vulnerability of terrestrial ectotherms to climate change because of both a lack of acclimation capacity and a limited increase in variance that may provide less raw material for evolutionary adaptation. Considering both acclimation capacity and variance in physiological rates side‐by‐side is therefore important for understanding how climate change will impact populations. Read the free Plain Language Summary for this article on the Journal blog.

Reliable landscape-scale monitoring is key to informing policy and guiding strategic intervention to halt and reverse global biodiversity loss. Time and resources constrain the scale of field surveys, but Earth observation (EO) satellites provide routine wall-to-wall coverage of the Earth. If changes in vegetation composition or abundance are captured in reflectance values, we can combine restricted field monitoring with EO data to improve our inferences at the landscape scale. We investigated whether EO data improved our capacity to predict the spatial distribution and temporal dynamics of vegetation in the Greater Cape Floristic Region (GCFR) of South Africa. Our analysis was based on the 211 most frequently observed plant species recorded in 1440 surveys. The value of EO was assessed based on the improvement to joint species distribution models (JSDMs) that were fitted with standard static environmental data. Topography and temperature were the most influential environmental drivers in both distribution and abundance models. The addition of EO resulted in a marginal increase in the explanatory power of distribution models (i.e., presence/absence) by 3%, while a more substantial enhancement was observed in species abundance models, with an increase of up to 30%. Nevertheless, the proportion of variance explained by EO was much greater, representing between 34% and 64% of the total. The inclusion of measurable EO variables replaced much of the residual variance that was otherwise explained by estimated spatial latent variables, allowing for more accurate predictions of composition across the landscape. We demonstrate that diverse field data combined with EO can substantially enhance the identification of spatial variation in abundance and temporal changes in the composition of highly diverse communities, such as those found in the GCFR.

Over one-third of the Amazon rainforest has been lost or degraded through human activities. While Earth observation satellites effectively monitor forest cover, assessing the impacts of degradation on biodiversity at large scales remains challenging. We analysed 3,129 bird surveys conducted over 16 years in the Tambopata Forest, south-eastern Peru, to evaluate whether remote sensing (RS) variables derived from Landsat imagery can predict variation in bird community composition. We compared the performance of RS-based predictors with traditional habitat descriptors in modelling the occurrence probabilities of 135 commonly recorded bird species. Models using Landsat reflectance outperformed those based on habitat data for predicting species occupancy (mean AUC = 0.68 vs 0.58) and achieved high predictive accuracy (AUC > 0.7) for more species (49 vs 20). However, low detection rates limited the ability of all models to estimate true community composition and to detect temporal change. To address this, we recommend survey designs that prioritise greater replication at fewer sites, thereby improving detection rates and the power to monitor biodiversity trends. Our findings highlight the potential for integrating satellite-based environmental variables with improved survey designs to enhance biodiversity monitoring in tropical forests. ### Competing Interest Statement The authors have declared no competing interest.

Contact with nature can contribute to health and wellbeing, but knowledge gaps persist regarding the environmental characteristics that promote these benefits. Understanding and maximising these benefits is particularly important in urban areas, where opportunities for such contact is limited. At the same time, we are facing climate and ecological crises which require policy and practice to support ecosystem functioning. Policies are increasingly being oriented towards delivering benefits for people and nature simultaneously. However, different disciplinary understandings of environments and environmental quality present challenges to this agenda. This paper highlights key knowledge gaps concerning linkages between nature and health. It then describes two perspectives on environmental quality, based respectively in environmental sciences and social sciences. It argues that understanding the linkages between these perspectives is vital to enable urban environments to be planned, designed and managed for the benefit of both environmental functioning and human health. Finally, it identifies key challenges and priorities for integrating these different disciplinary perspectives.

Climate change is expected to result in warmer and more variable thermal environments globally. Greater thermal variability is expected to result in strong selection pressures leading to genetic adaptation and/or the evolution of adaptive phenotypic plasticity. Such responses depend on genetic and phenotypic variability. However, most work has focused on changes in mean phenotypic responses to climate warming ignoring how temperature may also change phenotypic variability. Phenotypic variability may be particularly important at extreme, high temperatures, which would facilitate selection of resistant individuals or promote plasticity (acclimation) and thereby increase resilience to heat waves. Using newly developed effect size estimates and meta-analysis (>1900 effects from 226 species), we show that across habitats relative variance in physiological rates decreased at higher temperatures. Freshwater ectotherms are capable of acclimating and have the smallest reductions in relative variance. Marine organisms also showed a capacity to acclimate to higher temperatures, but capacity for plasticity traded-off with a reduction in relative variance in physiological rates at higher temperatures. Relative variance reductions were particularly pronounced for terrestrial ectotherms, and this coincided with a lack of capacity for acclimation, highlighting the vulnerability of terrestrial ectotherms to climate change. Neither life-history stage nor past climate explained effect variability. Our results show that beneficial acclimation responses may trade-off with reductions in physiological rate variance. This trade-off could constrain evolutionary responses to climate change and reduce the potential benefits of portfolio effects. These findings have important evolutionary and ecological ramifications that affect our understanding of how climate change will impact populations now and in the future.

Environmental markets are a rapidly emerging tool to mobilize private funding to incentivize landholders to undertake more sustainable land management. How units of biodiversity in these markets are measured and subsequently traded creates key challenges ecologically and economically because it determines whether environmental markets can deliver net gains in biodiversity and efficiently lower the costs of conservation. We developed and tested a metric for such markets based on the well-established principle of irreplaceability from systematic conservation planning. Irreplaceability as a metric avoids the limitations of like-for-like trading and allows one to capture the multidimensional nature of ecosystems (e.g., habitats, species, ecosystem functioning) and simultaneously achieve cost-effective, land-manager-led investments in conservation. Using an integrated ecological modeling approach, we tested whether using irreplaceability as a metric is more ecologically and economically beneficial than the simpler biodiversity offset metrics typically used in net gain and no-net-loss policies. Using irreplaceability ensured no net loss, or even net gain, of biodiversity depending on the targets chosen. Other metrics did not provide the same assurances and, depending on the flexibility with which biodiversity targets can be achieved, and how they overlap with development pressure, were less efficient. Irreplaceability reduced the costs of offsetting to developers and the costs of ecological restoration to society. Integrating economic data and systematic conservation planning approaches would therefore assure land managers they were being fairly rewarded for the opportunity costs of conservation and transparently incentivize the most ecologically and economically efficient investments in nature recovery.

R Code for the study of "Environmental DNA captures signals of the internal structure of a pond metacommunity"

Open-pit and subsurface extraction of oil sands in Alberta, Canada, continue to expand and pose a risk of contamination to the aquatic ecosystems of the region. These operations have raised wider concerns the design of the ongoing environmental monitoring may be insufficient to detect large-scale trends. Our goal was to advance assessment of ecological risk within the region using a combination of approaches, first by quantifying hazards associated with single and multiple contaminants of concern, and second to map expected risk based on spatial and environmental associations, thereby identifying areas of possible impact in the oil sands region that could support adaptive monitoring by assessing and mapping exceedance of guideline values. We assessed 17 dissolved metals of concern at 19 mainstem and tributary monitoring stations on the Lower Athabasca River. Exceedances of guideline values were transient and spatially-restricted, meaning risk was generally low to negligible for many contaminants and sampling stations. The joint or combined risk at least one contaminant would exceed their respective (long-term) guideline values was greatest at Big Creek (32%), but again, typically low for the majority of monitoring stations. However high temporal stochasticity meant statistical associations among monitoring stations were weak, and as a result the association between predicted concentrations and ecological risk could not be expanded beyond these point measurements. Despite this limitation, the synthesis of various lines of evidence indicated that contaminant delivery is measurable and cumulative in proximity to mining developments, and that there is some evidence of ecological effects linked to contaminant exposure. The results of the ERA and the challenges encountered in this work should be used to inform future monitoring and research as part of the adaptive management framework to improve the ERA process to ensure that the monitoring program is effective and viable in the long-term.

Motivation: Aquatic insects comprise 64% of freshwater animal diversity and are widely used as bioindicators to assess water quality impairment and freshwater ecosystem health, as well as to test ecological hypotheses. Despite their importance, a comprehensive, global database of aquatic insect occurrences for mapping freshwater biodiversity in macroecological studies and applied freshwater research is missing. We aim to fill this gap and present the Global EPTO Database, which includes worldwide geo-referenced aquatic insect occurrence records for four major taxa groups: Ephemeroptera, Plecoptera, Trichoptera and Odonata (EPTO).Main type of variables contained: A total of 8,368,467 occurrence records globally, of which 8,319,689 (99%) are publicly available. The records are attributed to the corresponding drainage basin and sub-catchment based on the Hydrography90m dataset and are accompanied by the elevation value, the freshwater ecoregion and the protection status of their location.Spatial location and grain: The database covers the global extent, with 86% of the observation records having coordinates with at least four decimal digits (11.1 m precision at the equator) in the World Geodetic System 1984 (WGS84) coordinate reference system.Time period and grain: Sampling years span from 1951 to 2021. Ninety-nine percent of the records have information on the year of the observation, 95% on the year and month, while 94% have a complete date. In the case of seven sub-datasets, exact dates can be retrieved upon communication with the data contributors.Major taxa and level of measurement: Ephemeroptera, Plecoptera, Trichoptera and Odonata, standardized at the genus taxonomic level. We provide species names for 7,727,980 (93%) records without further taxonomic verification.Software format: The entire tab-separated value (.csv) database can be downloaded and visualized at . Fifty individual datasets are also available at , while six datasets have restricted access. For the latter, we share metadata and the contact details of the authors.

Big Data science has significantly furthered our understanding of complex systems by harnessing large volumes of data, generated at high velocity and in great variety. However, there is a risk that Big Data collection is prioritised to the detriment of 'Small Data' (data with few observations). This poses a particular risk to ecology where Small Data abounds. Machine learning experts are increasingly looking to Small Data to drive the next generation of innovation, leading to development in methods for Small Data such as transfer learning, knowledge graphs, and synthetic data. Meanwhile, meta-analysis and causal reasoning approaches are evolving to provide new insights from Small Data. These advances should add value to high-quality Small Data catalysing future insights for ecology.

New technologies for acquiring biological information such as eDNA, acoustic or optical sensors, make it possible to generate spatial community observations at unprecedented scales. The potential of these novel community data to standardize community observations at high spatial, temporal, and taxonomic resolution and at large spatial scale ('many rows and many columns') has been widely discussed, but so far, there has been little integration of these data with ecological models and theory. Here, we review these developments and highlight emerging solutions, focusing on statistical methods for analyzing novel community data, in particular joint species distribution models; the new ecological questions that can be answered with these data; and the potential implications of these developments for policy and conservation.

The delivery of consistent and accurate fine‐resolution data on biodiversity using metabarcoding promises to improve environmental assessment and research. Whilst this approach is a substantial improvement upon traditional techniques, critics note that metabarcoding data are suitable for establishing taxon occurrence, but not abundance. We propose a novel hierarchical approach to recovering abundance information from metabarcoding, and demonstrate this technique using benthic macroinvertebrates. To sample a range of abundance structures without introducing additional changes in composition, we combined seasonal surveys with fish‐exclusion experiments at Catamaran Brook in northern New Brunswick, Canada. Five monthly surveys collected 31 benthic samples for DNA metabarcoding divided between caged and control treatments. A further six samples per survey were processed using traditional morphological identification for comparison. By estimating the probability of detecting a single individual, multispecies abundance models infer changes in abundance based on changes in detection frequency. Using replicate detections of 184 genera (and 318 species) from metabarcoding samples, our analysis identified changes in abundance arising from both seasonal dynamics and the exclusion of fish predators. Counts obtained from morphological samples were highly variable, a feature that limited the opportunity for more robust comparison, and emphasizing the difficulty standard methods also face to detect changes in abundance. Our approach is the first to demonstrate how quantitative estimates of abundance can be made using metabarcoding, both among species within sites as well as within species among sites. Many samples are required to capture true abundance patterns, particularly in streams where counts are highly variable, but few studies can afford to process entire samples. Our approach allows study of responses across whole communities, and at fine taxonomic resolution. We discuss how ecological studies can use additional sampling to capture changes in abundance at fine resolution, and how this can complement broad‐scale biomonitoring using DNA metabarcoding.

Metabarcoding is capable of delivering consistent and accurate fine-resolution biodiversity data, and offers great promise for improving aspects of environmental assessment and research. Even so, many ecologists are keen to make further inferences about species’ abundances and the number of sequence reads has proven to be a poor proxy for abundance. The conservative interpretation has been to treat metabarcoding data as presence/absence, and although such data are less rich, occurrence and abundance are only different expressions of the same phenomenon. Interestingly if we assume the probability of detecting individuals is constant, it should be possible to use changes in the frequency of detection to infer changes in the underlying abundance. We tested the possibility that changes in the abundance structure of benthic macroinvertebrate communities could be recovered using replicated metabarcoding. We conducted 5 monthly surveys from Jun-Nov 2019 at the Catamaran Brook, a small tributary of the Little Southwest Miramichi River in New Brunswick, Canada. Each survey collected 30 benthic samples divided between control and treatment cages that excluded predatory fish. A further 6 samples were taken for traditional microscopic identification and counting. Analysis of the metabarcoding data demonstrated that we could recover plausible changes in abundance from occurrence data, including significant responses to both seasonal dynamics and the experimental exclusion of predators. The microscopy samples merely confirmed that count data are highly stochastic, and therefore while specific estimates of expected abundance from our model are highly uncertain, they capture those differences we could validate. In summary, while we confirmed that occurrence data are more robust for routine bioassessment, it is possible to recover fine-resolution changes in abundance that can inform ecological studies using metabarcoding.

The complexity and natural variability of ecosystems present a challenge for reliable detection of change due to anthropogenic influences. This issue is exacerbated by necessary trade-offs that reduce the quality and resolution of survey data for assessments at large scales. The Peace-Athabasca Delta (PAD) is a large inland wetland complex in northern Alberta, Canada. Despite its geographic isolation, the PAD is threatened by encroachment of oil sands mining in the Athabasca watershed and hydroelectric dams in the Peace watershed. Methods capable of reliably detecting changes in ecosystem health are needed to evaluate and manage risks. Between 2011 and 2016, aquatic macroinvertebrates were sampled across a gradient of wetland flood frequency, applying both microscope-based morphological identification and DNA metabarcoding. By using multispecies occupancy models, we demonstrate that DNA metabarcoding detected a much broader range of taxa and more taxa per sample compared to traditional morphological identification and was essential to identifying significant responses to flood and thermal regimes. We show that family-level occupancy masks high variation among genera and quantify the bias of barcoding primers on the probability of detection in a natural community. Interestingly, patterns of community assembly were nearly random, suggesting a strong role of stochasticity in the dynamics of the metacommunity. This variability seriously compromises effective monitoring at local scales but also reflects resilience to hydrological and thermal variability. Nevertheless, simulations showed the greater efficiency of metabarcoding, particularly at a finer taxonomic resolution, provided the statistical power needed to detect change at the landscape scale.