Ecological Indicators

Biodiversity monitoring is crucial for ecosystem conservation, yet field data collection is limited by costs, time, and extent. Remote sensing represents a convenient approach providing frequent, near-real-time information over wide areas. According to the Spectral Variation Hypothesis (SVH), spectral diversity (SD) is an effective proxy of environmental heterogeneity, which ultimately relates to plant diversity. So far, studies testing the relationship between SD and biodiversity have reported contradictory findings, calling for a thorough investigation of the key factors (e.g., metrics applied, ecosystem type) and the conditions under which such a relationship holds true. This study investigates the applicability of the SVH for plant diversity monitoring at the landscape scale by comparing the performance of three different types of SD metrics. Species richness and functional diversity were calculated for more than 2000 cells forming a grid covering the Czech Republic. Within each cell, we quantified SD using a Landsat-8 "greenest pixel" composite by applying: i) the standard deviation of NDVI, ii) Raos Q entropy index, and iii) richness of "spectral communities". Habitat type (i.e., land cover) was included in the models describing the relationship between SD and ground biodiversity. Both species richness and functional diversity show positive and significant relationships with each SD metric tested. However, SD alone accounts for a small fraction of the deviance explained by the models. Furthermore, the strength of the relationship depends significantly on habitat type and is highest in natural transitional areas. Our results underline that, despite the stability in the significance of the link between SD and plant diversity at this scale, the applicability of SD for biodiversity monitoring is context-dependent and the factors mediating such a relationship must be carefully considered to avoid drawing misleading conclusions. HighlightsO_LIPlant species richness and functional diversity show significant and positive relationships with spectral diversity C_LIO_LISpectral diversity alone explains a small fraction of the total variability in ground biodiversity C_LIO_LISlight differences among the performances of the spectral diversity metrics tested C_LIO_LIThe relationship between spectral and plant diversity is context-dependent C_LI

The monitoring of environmental policies in Europe has taken place since the 1980s and still remains a challenge for decision- and policy-making. For forests, it is concretized through the publication of a State Of Europes Forests every five years, the last report just been released. However, the process lacks a clear analytical framework and appears limited to orient and truly assess sustainable management of European forests. We classified the 34 quantitative sustainable forest management indicators in the Driver-Pressure-State-Impact-Response (DPSIR) framework to analyse gaps in the process. In addition, we classified biodiversity-related indicators in the simpler Pressure-State-Response (PSR) framework. We showed that most of the sustainable forest management indicators assess the state of European forests, but almost half could be classified in another DPSIR category. For biodiversity, most indicators describe pressures, while direct taxonomic state indicators are very few. Our expert-based classification show that sustainable forest management indicators are unbalanced regarding the DPSIR framework. However, completing this framework with other indicators would help to have a better view and more relevant tools for decision-making. The results for biodiversity were comparable, but we showed that some indicators from other criteria than the one dedicated to biodiversity could also help understanding threats and actions concerning it. Such classification helps in the decision process, but is not sufficient to fully support policy initiative. In particular, the next step would be to better understand the links between DPSIR and PSR categories.

Protected areas are generally designed to conserve biodiversity. However, how well they also contribute to maintaining ecosystem functions that plant diversity supports has rarely been explicitly tested, often due to the lack of historical ecosystem function data. Here, we used a trait-based approach to reconstruct past ecosystem functioning and examine its change over the last 15 years in protected and unprotected coastal dune ecosystems, checking where functions remain stable over time. First, we resurveyed vegetation in quasi-permanent plots and measured in the present several ecosystem functions related to biomass production, carbon, water, nutrient cycling, erosion control, and invasion resistance across six coastal dune sites in Central Italy. Second, using these data, we quantified Biodiversity-Ecosystem Function (BEF) relationships and employed them to hindcast past ecosystem functions based on historical vegetation surveys. Finally, as a case study, we applied this method to assess temporal changes in ecosystem functioning under three protection regimes: national protected areas (i.e. strict protection), Natura 2000 sites (loose protection), and non-protected areas. Biomass production, carbon, and water regulation increased over time in non-protected areas, likely due to an expansion of ruderal and non-native species, that are usually more productive. Within Natura 2000 sites, communities showed a decrease in erosion control and invasion resistance, due to the loss of important dune-building species and the spread of non-natives. Only within national protected areas, ecosystem functions did not undergo significant temporal changes, and invasion resistance even increased. Our results suggest that ecosystem functioning remained stable over time only in areas under strict protection. More broadly, our study demonstrates the potential for using revisitation data in combination with locally estimated BEF relationships to hindcast past ecosystem functioning, providing a valuable tool for monitoring long-term functional changes in response to conservation measures.

Following our failure to fully achieve any of the 20 Aichi biodiversity targets, the future of biodiversity rests in the balance. The Convention on Biological Diversitys Post-2020 Global Biodiversity Framework (GBF) presents us with the opportunity to preserve Natures Contributions to People (NCPs) for current and future generations through conserving biodiversity and averting extinction across the Tree of Life. Here we call attention to our need to conserve the Tree of Life to maintain its benefits into the future as a key mechanism for achieving intergenerational equity. We highlight two indicators available for adoption in the post-2020 GBF to monitor our progress towards safeguarding the Tree of Life. The Phylogenetic Diversity indicator, adopted by IPBES, can be used to monitor biodiversitys capacity to maintain NCPs for future generations. The EDGE (Evolutionarily Distinct and Globally Endangered) Index monitors how well we are performing at averting the greatest losses across the Tree of Life by conserving the most distinctive species. By committing to safeguarding the Tree of Life post-2020, we can reduce biodiversity loss to preserve natures contributions to humanity now and into the future.

Measuring environmental degradation with bioindicators, landscape metrics, and remote sensing helps understand impact on biota. However, data on anthropogenic pressures such as plant exploitation, poaching and invasive species are crucial. We created an Anthropogenic Influence Index (AII) for medium and large mammals at the Atlantic Forest based on local environmental quality indicators and tested its correlation with existing indices, such as the Global Human Influence Index (GHII), landscape metrics and social-economic indicators. We found no correlation between the AII and the GHII, indicating that remote sensing-collected data may not reflect local and specific anthropogenic impacts on the environment. In addition, there was a correlation between the AII and the Human Development Index, drawing attention to the direct relationship between income, education and life expectancy and the incidence of environmental impacts. Thus, the AII appears to better capture local nuances of environmental impacts, particularly those significant for medium and large mammals, compared to other indicators such as GHII, human density, and landscape metrics.

Forest restoration requires monitoring to assess changes in above- and below-ground communities, which is challenging due to practical and resource limitations. With emerging sound recording technologies, ecological acoustic survey methods--also known as ecoacoustics--are increasingly available. These provide a rapid, effective, and non-intrusive means of monitoring biodiversity. Above-ground ecoacoustics is increasingly widespread, but soil ecoacoustics has yet to be utilised in restoration despite its demonstrable effectiveness at detecting meso- and macrofauna acoustic signals. This study applied ecoacoustic tools and indices (Acoustic Complexity Index, Normalised Difference Soundscape Index, and Bioacoustic Index) to measure above- and below-ground biodiversity in a forest restoration chronosequence. We hypothesised that higher acoustic complexity, diversity and high-frequency to low-frequency ratio would be detected in restored forest plots. We collected n = 198 below-ground samples and n = 180 ambient and controlled samples from three recently degraded (within 10 years) and three restored (30-51 years ago) deciduous forest plots across three monthly visits. We used passive acoustic monitoring to record above-ground biological sounds and a below-ground sampling device and sound-attenuation chamber to record soil communities. We found that restored plot acoustic complexity and diversity were higher in the sound-attenuation chamber soil but not in situ or above-ground samples. Moreover, we found that restored plots had a significantly greater high-frequency to low-frequency ratio for soil, but no such association for above-ground samples. Our results suggest that ecoacoustics has the potential to monitor below-ground biodiversity, adding to the restoration ecologists toolkit and supporting global ecosystem recovery. Implications for PracticeO_LIThis is the first known study to assess the sounds of soil biodiversity in a forest restoration context, paving the way for more comprehensive studies and practical applications to support global ecosystem recovery. C_LIO_LISoil ecoacoustics has the potential to support restoration ecology/biodiversity assessments, providing a minimally intrusive, cost-effective and rapid surveying tool. The methods are also relatively simple to learn and apply. C_LIO_LIEcoacoustics can contribute toward overcoming the profound challenge of quantifying the effectiveness (i.e., success) of forest restoration interventions in reinstating target species, functions and so-called services and reducing disturbance. C_LI

Passive acoustic monitoring (PAM) is increasingly applied in biodiversity research, yet its reliability as a proxy for biodiversity remains insufficiently evaluated. In particular, the spatiotemporal autocorrelation inherent in acoustic indices of PAM is rarely quantified, despite its importance for the standardized application of acoustic monitoring. We conducted an integrated study to investigate these issues using a complete grid-based monitoring system covering the entire region (100 grids of 1 km x 1 km) in southern subtropical climatic zones. Acoustic data from 58 valid sites were combined with camera-trapping and vegetation surveys to evaluate six commonly used acoustic indices in PAM. We found that these indices were more strongly associated with relative abundance and community diversity metrics of bird and mammal than with species richness. Spatially, autocorrelation ranges of some acoustic indices extended to approximately 4 km (i.e., the Bioacoustic Index (BIO) and Normalized difference soundscape index (NDSI)). Temporally, all indices exhibited significant autocorrelation over 2-5 days, exceeding the typical short-term turnover of bird and mammal activity (1-2 days). Our results indicate that acoustic indices are not direct proxies for species richness but provide complementary information on soundscape dynamics. By explicitly quantifying spatiotemporal autocorrelation, this study offers practical guidance for sampling design and statistical analysis in passive acoustic monitoring, supporting more reliable and efficient biodiversity assessment.

O_LIForest degradation is posing a growing threat to biodiversity conservation, climate regulation and nature contributions to people worldwide. The EU Nature Restoration Regulation (NRR) recognise the need of healthy forests and has established indicators to assess the condition of forest ecosystems. Forest management practices, especially harvesting may contribute to improve these indicators by, for example, reducing tree density and promoting tree species diversification. C_LIO_LIHere, we assessed temporal trends in forest condition indicators (hereafter, indicators) across Iberian forests since the 80s and evaluated how harvesting occurrence and intensity modulated these trends. Using 46,354 plots from the Spanish Forest Inventory (1986-2022), we analysed trends in indicators depending on stand diversity (monospecific or mixed), protection status (protected or unprotected), origin of the stand (natural or planted), and biogeographical region (Mediterranean or temperate). C_LIO_LIOverall, indicators increased over time. Harvesting occurrence reduced the increases in aboveground carbon stocks, structural diversity, tree species diversity, and standing deadwood; however, it contributed to increase the proportion of native species in specific forest types. Medium to high harvesting intensity negatively impacted aboveground carbon stocks, while medium intensities increased tree species diversity but reduced the structural diversity. C_LIO_LISynthesis and applications. Our results suggest that indicators are increasing as stand develops in absence of disturbances such as harvesting. Tree harvesting cannot be considered as a silver bullet to achieve the objectives of the NRR, but it can contribute under certain conditions - specifically at low intensities for carbon stocks and at medium intensities for species diversity. The naturally positive trend of indicators underlines the need to establish thresholds values and minimum rates of changes that distinguish restoration outcomes from natural dynamics. Finally, our study also highlights the key role of forest inventories in monitoring forest condition over time and across diverse landscapes. C_LI

Patterns of disturbance in Sierra Nevada forests are shifting as a result of changing climate and land uses. These changes have underscored the need for a monitoring system that both detects disturbances and attributes them to different agents. Addressing this need will aid forest management and conservation decision-making, potentially enhancing forests resilience to changing climatic conditions. In addition, it will advance understanding of the patterns, drivers, and consequences of forest disturbance in space and time. This study proposed and evaluated an enhanced method for disturbance agent attribution. Specifically, it tested the extent to which textural information could improve the performance of an ensemble learning method in predicting the agents of disturbance from remote sensing observations. Random Forest (RF) models were developed to attribute disturbance to three primary agents (fire, harvest, and drought) in Stanislaus National Forest, California, U.S.A., between 1999 and 2015. To account for spectral behavior and topographical characteristics that regulate vegetation and disturbance dynamics, the models were trained on predictors derived from both the Landsat record and from a digital elevation model. The predictors included measurements of spectral change acquired through temporal segmentation of Landsat data; measurements of patch geometry; and a series of landscape texture metrics. The texture metrics were generated using the Grey-Level Co-Occurrence Matrix (GLCM). Two models were produced: one with GLCM texture metrics and one without. The per-class and overall accuracies of each model were evaluated with out-of-bag (OOB) observations and compared statistically to quantify the contribution of texture metrics to classification skill. Overall OOB accuracy was 72.0% for the texture-free model and 72.2% for the texture-dependent model, with no significant accuracy difference between them. Spatial patterns in prediction maps cohered with expectations, with most harvest concentrated in mid-elevation forests and fire and stress co-occurring at lower elevations. Altogether, the method yielded adequate identification of disturbance and moderate attribution accuracy for multiple disturbance agents. While textures did not contribute meaningfully to model skill, the study offers a strong foundation for future development, which should focus on improving the efficacy of the model and generalizing it for systems beyond the Central Sierra Nevada.

The Living Planet Index -- a biodiversity indicator that assesses the relative change of aggregate vertebrate abundance data -- is an indicator used in global and national biodiversity monitoring frameworks. In Canada, the LPI has been modified (C-LPI) -- adopting differing methodological choices relative to the global LPI. However, there is no clear consensus on the most appropriate analytical methods, particularly as they pertain to the treatment of zeros, confidence intervals and uncertainty, time series length and number of data points required, modelling of short time series, removal of outliers, weighting species, and the impact of baseline year selection. Our analysis transparently explores multiple methodological options and the consequent C-LPI output for each of these decision points. Our research does not evaluate the superiority of a single approach but rather provides transparency and accountability in C-LPI reporting. We hope that this will further strengthen the utility of the C-LPI and provide decision makers with the necessary information to appropriately interpret patterns, evaluate progress towards biodiversity targets, and inform conservation action.

Ecosystems are under unprecedented pressures, reflected in rapid changes in the regime of disturbances that may cause negative impacts on them. This highlights the importance of characterizing the state of an ecosystem and its response to disturbances, which is known as a notoriously difficult task. The state-of-the-art knowledge has been tested rarely in real ecosystems for a number of reasons such as mismatches between the time scale of ecosystem processes and data accessibility as well as weaknesses in the performance of available methods. This study aims to use remotely sensed spatio-temporal data to identify early warning signals of forest mortality using satellite images. For this purpose, I propose a new approach that measures local spatial autocorrelation (using local Morans I and local Gearys c method) at each time, which proved to produce robust results in multiple different study sites examined in this article. This new approach successfully generates early warning signals from time series of local spatial autocorrelation values in unhealthy study sites 2 years prior to forest mortality occurrence. Furthermore, I develop a new R package, called "stew", that enables users to explore spatio-temporal analysis of ecosystem state changes. This work corroborates the suggestion that spatio-temporal indicators have the potential to diagnose early warning signals to identify upcoming climate-induced forest mortality, up to two years before its occurrence.

Remote sensing is an invaluable tool to objectively illustrate the rapid decline in habitat extents worldwide. The many operational Earth Observation platforms provide options for the generation of land cover maps, each with unique characteristics, as well as considerable semantic differences in the definition of classes. As a result, differences in baseline estimates are inevitable. Here we compare forest cover and surface water estimates over four time periods spanning three decades (1989-2018) for [~]1.3 million km2 encompassing the Xingu river basin, Brazil, from published, freely accessible remotely sensed classifications. While all datasets showed a decrease in forest extent over time, we found a large range in the total area reported by each product for all time periods. The greatest differences ranged from 9% (year 2000) to 17% of the total area (2014-2018 period). We also show the high sensitivity of forest fragmentation metrics (entropy and foreground area density) to data quality and spatial resolution, with cloud cover and sensor artefacts resulting in errors. We further show the importance of choosing surface water datasets carefully because they differ greatly in location and amount of surface water mapped between sources. In several of the datasets illustrating the land cover following operationalization of the Belo Monte dam, the large reservoirs are notably absent. Freshwater ecosystem health is influenced by the land cover surrounding water bodies (e.g. Riparian zones). Understanding differences between the many remotely sensed baselines is fundamentally important to avoid information misuse, and to objectively choose the most appropriate dataset for conservation, taxonomy or policy-making. The differences in forest cover between the datasets examined here are not a failure of the technology, but due to different interpretations of forest and characteristics of the input data (e.g. spatial resolution). Our findings demonstrate the importance of transparency in the generation of remotely sensed datasets and the need for users to familiarize themselves with the characteristics and limitations of each chosen data set.

Native tropical forests hold high levels of diversity, challenging forest restoration of large areas in a global change scenario. For a site-specific restoration is required the understanding of the main influences ruling the assemblages. We aimed to answer three questions. 1) how do environmental variables influence taxonomic, phylogenetic diversities, and the phylogenetic structure in the of Rio Doce Basin (TFRD)? 2) How do environmental variables, phylogenetic structure and the main types of seed dispersal relate to each other? 3) Which information of the TFRD assemblages can be used for ecological restoration and conservation? We used 78 sites with their checklists to calculate taxonomic, and phylogenetic diversities, phylogenetic structures, and dispersal proportions. Then, we related the diversities of the sites to their bioclimatic variables and built GLM models. Species richness was influenced negatively by water excess duration, by water deficit duration, and positively by maximum temperature, and temperature seasonality. Water regime drives diversity and phylogenetic community structure in the TFRD more than other variables. Annual precipitation and maximum temperature presented the clearer influences on diversity and phylogenetic structure. Zoochory was positively, and anemochory, autochory were negatively related to sesMPD. By choosing the lineages with high fitness for each site, the functioning and the stability of ecosystems should increase. The addition of species with anemochory and autochory increases functional and phylogenetic diversity in areas with extreme water excess or water deficit, important in a global change scenario. A high proportion of zoochory allows the communities to function conserving dispersers, biodiversity, and services. Implications for practiceO_LIThe use of objective methods based on community assembly rules enhances the choice of species, and of phylogenetic lineages better fitted to the bioclimatic profiles of the areas to be restored, improving the functioning and stability of the restored forests. C_LIO_LIThe water purification service should be improved through forest restoration as much as possible because ecosystem services and biodiversity conservation are co-benefits of restored forests. C_LIO_LIThe inclusion of species with anemochory, and autochory in forest restoration practices should become usual, as they increase functional, and phylogenetic diversities, and as a consequence, the ecosystem stability. C_LIO_LIA large proportion of species with zoochory in restored forests co-benefits taxonomic diversity, phylogenetic diversity, and ecosystem stability. C_LI

Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificacao Florestal e Agricola (IMAFLORA) near Sao Felix do Xingu, in Para, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORAs shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.

1Tropical 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.

The growing focus on the role of forests in carbon sequestration highlights the importance of accurately and efficiently measuring biophysical traits, such as diameter at breast height (DBH) and tree height. Understanding genetic contributions to trait variation is crucial for enhancing carbon storage through genetic improvement of forest trees. Light detection and ranging (LiDAR) has been used to estimate DBH and tree height; however, few studies have explored the heritability of these traits or assessed the accuracy of biomass increment selections based on these traits. Therefore, this study aimed to leverage LiDAR to measure DBH and tree height, estimate tree heritability, and evaluate the accuracy of timber volume selections based on these traits using 60-year-old larch as the study material. Unmanned aerial vehicle (UAV) and backpack LiDAR were compared against hand-measured values. The accuracy of DBH estimations using backpack LiDAR resulted in a root mean square error (RMSE) of 2.7 cm and a coefficient of determination of 0.67. Conversely, the accuracy achieved with UAV LiDAR was 4.0 cm in RMSE and a 0.24 coefficient of determination. The heritability of DBH was found to be higher for backpack LiDAR than for UAV LiDAR and even exceeded that of hand measurements. Comparisons of the accuracy of timber volume selections based on the measured traits demonstrated comparable performances between the backpack and UAV LiDAR. Overall, these findings underscore the potential of using LiDAR remote sensing to quantitatively measure forest tree biomass and facilitate their genetic improvement of carbon-sequestration ability based on these measurements.

Despite being increasingly threatened by human-induced disturbances, dry forests remain the least studied and protected forest types in the Caribbean region. In contrast to many other forest systems in the world, we have little knowledge of the site-specific variation in vegetation communities within these forests, nor understand how plant species distribution is determined by environmental variables, including among them geological attributes. Here, we assessed the associations between plant communities and habitat types in a semi-deciduous forest of the Dominican Republic. We collected vegetation data from 23 sites within the Ocoa river basin, which we classified into six groups with a Random Forest algorithm, lithology, geomorphology, topography, and last decade history of forest loss as predictor variables. We established three main clusters: one group which encompassed sites with forest over a limestone substrate, four groups of sites with forests over a marlstone substrate with varied degrees of steepness and forest loss history, and one group that gathered all sites with forest over an alluvial substrate. In order to measure the associations of plant communities with groups of sites, we used the indicator value index (IndVal), which indicates whether a plant species is found in one or multiple habitat types, and the phi coefficient of association, which measures species preferences for habitats. We found that 16 species of woody plants are significantly associated with groups of sites by means of their indices. Our findings suggest that the detection of plant species associations with our selection of environmental variables is possible using a combination of indices. We show that there is considerable variation in plant community composition within the semi-deciduous forest studied, and suggest that conservation planning should focus on protection of this variation, while considering the significance and variability of geodiversity as well. In addition, we propose that our indicator groups facilitate vegetation mapping in nearby dry forests, where it is difficult to conduct thorough vegetation or environmental surveys. In short, our analyses hold potential for the development of site-specific management and protection measures for threatened semi-deciduous forests in the Caribbean.

Species richness is the most commonly used metric to quantify biodiversity. However, examining dark diversity, the group of missing species which can potentially inhabit a site, can provide a more thorough understanding of the processes influencing observed biodiversity and help evaluate the restoration potential of local habitats. So far, dark diversity has mainly been studied for specific habitats or largescale landscapes while less attention has been given to variation across broad environmental gradients or as a result of local conditions and biotic interactions. In this study, we investigate the importance of local environmental conditions in determining dark diversity and observed richness in plant communities across broad environmental gradients. We use the ecospace concept to investigate how abiotic gradients (defined as position), availability of biotic resources (defined as expansion), spatiotemporal extent of habitats (defined as continuity), as well as species interactions through competition, relate to these biodiversity measures. Position variables were important for both plant richness and dark diversity, some with quadratic relationships, e.g., plant richness showing a unimodal response to soil fertility corresponding to the intermediate productivity hypothesis. Competition represented by community mean Grime C showed a negative correlation with plant richness. Besides position, organic carbon was the most important variable for dark diversity, indicating that in late succession habitats such as forests and shrubs, dark diversity is generally low. The importance of Grime C indicate that intermediate disturbance, such as grazing, may facilitate higher species richness and lower dark diversity. Comparing various biodiversity metrics and their influencing factors might reveal important drivers of biodiversity changes and result in better conservation decision-making.

Climate change and particularly the frequency and intensity of extreme events is affecting the distribution and abundance of species, with drastic consequences on ecological processes and community structure. Long-term records of environmental parameters are indispensable in climatological studies in order to better understand the processes involved. However, such data is usually unavailable for many geographic areas and certain environments, like Patagonian intertidal shores in the Southwestern Atlantic. The use of reanalysis products can help elucidate the climate of the past when in situ information is missing. In this work, we test the performance of reanalysis datasets in reproducing air temperature patterns and extreme hot events (heatwaves) on rocky intertidal environments of Atlantic Patagonia. Thus, we evaluate the degree of correlation between different reanalysis products and air temperature data from loggers placed on rocky shores. We also test whether those products accurately detect the duration, frequency and number of heatwaves and look for historical trends in their features. Our results showed that reanalysis products perform well for assessing broad-scale changes in air temperature patterns. Products were also capable of detecting heatwaves, with little variation in their features for the period 1960-2024. Additionally, real-time field temperatures to which intertidal organisms are exposed were obtained for the first time in the area; reporting heatwaves events. Thereby, reanalysis products complement local data, providing key information to understand the role that temperature increases and extreme heat can have in events like mussels mass mortalities reported locally. In this sense, our results suggest that heatwaves alone wouldnt be explaining the observed mussel losses. This work provides empiric evidence on the usefulness of reanalysis products of intertidal habitats and encourages similar approaches in order to properly understand climatological patterns that can drive ecological processes on coastal habitats.

Crowdsourced social media data has become popular in the assessment of cultural ecosystem services (CES). Advances in deep learning show great potential for the timely assessment of CES at large scales. Here, we describe a procedure for automating the assessment of image elements pertaining to CES from social media. We focus on a binary (natural, human) and a multiclass (posing, species, nature, landscape, human activities, human structures) classification of those elements using two Convolutional Neural Networks (CNNs; VGG16 and ResNet152) with the weights from two large datasets - Places365 and ImageNet -, and our own dataset. We train those CNNs over Flickr and Wikiloc images from the Peneda-Geres region (Portugal) and evaluate their transferability to wider areas, using Sierra Nevada (Spain) as test. CNNs trained for Peneda-Geres performed well, with results for the binary classification (F1-score > 80%) exceeding those for the multiclass classification (> 60%). CNNs pre-trained with Places365 and ImageNet data performed significantly better than with our data. Model performance decreased when transferred to Sierra Nevada, but their performances were satisfactory (> 60%). The combination of manual annotations, freely available CNNs and pre-trained local datasets thereby show great relevance to support automated CES assessments from social media.