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Citizen science data has rapidly gained influence in urban ecology and conservation planning, but with limited understanding of how such data reflects social, economic, and political conditions and legacies. Understanding patterns of sampling bias across socioeconomic gradients is critical to accurately map and understand biodiversity patterns, and to generating representative and just environmental knowledge. In this study we explore how historic racially- explicit zoning policies (redlining) relate to biodiversity data collection across and within 195 metropolitan areas in the United States covering >30 million people across 38 states. We specifically look at birds, as they are the most widely studied group of animals, and hundreds of thousands of citizen scientists collect biodiversity data each year. We consistently find uneven bird observation sampling density across redlined areas, with so-called ‘desirable’ areas (i.e. historically white areas) having more than twice the density than areas redlined as ‘hazardous’. We further estimate the degree to which historically redlined areas are surveyed sufficiently and identify regions across all metro areas in need of enhanced bird surveying. After accounting for differences in present day vegetation, open space, population density and climate, we find significantly lower sampling density and sampling completeness in these redlined neighborhoods. Our results shed light on the importance of considering socio-political conditions in the interpretation of urban bird biodiversity estimates. We conclude by discussing specific policy implementations– such as the Justice 40 Initiative and propose a new EPA indicator – and opportunities in collaborating with bottom-up community and social justice organizations for a more representative understanding of biodiversity in urban areas.

Observations and collections of organisms form the basis of our understanding of Earth’s biodiversity and are an indispensable resource for global change studies. Geographic information is key, serving as the link between organisms and the environments they reside in. However, the geographic information associated with these records is often inaccurate, thus limiting their efficacy for research. Along these lines multiple solutions for identifying erroneous coordinate data and records georeferenced to centroids or landmarks have been developed. Another prominent, but less discussed and documented source of inaccuracies arises due to the use of gridded survey systems in many regions of the world. Here we present GridDER, a tool for identifying biodiversity records that have been designated locations on widely used grid systems. Our tool also estimates the degree of environmental heterogeneity associated with grid systems, allowing users to make informed decisions about how to use such occurrence data in global change studies. We show that a significant proportion (~13.5%; 261 million) of records on GBIF, largest aggregator of natural history collection data, are potentially gridded data, and demonstrate that our tool can reliably identify such records and quantify the associated uncertainties. GridDER can serve as a tool to not only screen for gridded points, but to quantify the geographic and environmental uncertainties associated with these records, which can be used to inform models and analyses that utilize these data, including those pertaining to global change.

Many migratory species are in decline across their geographical ranges. Single-population studies can provide important insights into drivers at a local scale, but effective conservation requires multi-population perspectives. This is challenging because relevant data are often hard to consolidate, and state-of-the-art analytical tools are typically tailored to specific datasets. We capitalized on a recent data harmonization initiative (SPI-Birds) and linked it to a generalized modeling framework to identify the demographic and environmental drivers of large-scale population decline in migratory pied flycatchers (Ficedula hypoleuca) breeding across Britain. We implemented a generalized integrated population model (IPM) to estimate age-specific vital rates, including their dependency on environmental conditions, and total and breeding population size of pied flycatchers using long-term (34-64 years) monitoring data from seven locations representative of the British breeding range. We then quantified the relative contributions of different vital rates and population structure to changes in short- and long-term population growth rate using transient life table response experiments (LTREs). Substantial covariation in population sizes across breeding locations suggested that change was the result of large-scale drivers. This was supported by LTRE analyses, which attributed past changes in short-term population growth rates and long-term population trends primarily to variation in annual survival and dispersal dynamics, which largely act during migration and/or non-breeding season. Contributions of variation in local reproductive parameters were small in comparison, despite sensitivity to local temperature and rainfall within the breeding period. We show that both short- and longer-term population changes of British-breeding pied flycatchers are likely linked to factors acting during migration and in non-breeding areas, where future research should be prioritized. We illustrate the potential of multi-population analyses for informing management at (inter)national scales and highlight the importance of data standardization, generalized and accessible analytical tools, and reproducible workflows to achieve them.

1. Estimates of temporal change of biodiversity, and its components loss and gain, are needed at local and geographical scales. However, we lack them because of data in-completeness, heterogeneity, and lack of temporal replication. Hence, we need a tool to integrate heterogeneous data and to account for their incompleteness. 2. We introduce spatiotemporal machine learning interpolation that can estimate cross-scale biodiversity change and its components. The approach naturally captures the expected and complex interactions between scale (grain), geography, data types, and drivers of change. As such it can integrate inventory data from reserves or countries with data from atlases and local survey plots. We present two flavors, both blending tree-based machine learning (random forests, boosted trees) with advances in ecolog-ical scaling: The first combines machine learning with species-area relationships (SAR method), the second with occupancy-area relationships (OAR method). 3. Using simulated data and an empirical example of global mammals and European plants, we show that tree-based machine learning effectively captures temporal biodi-versity change, loss, and gain across a continuum of spatial grains. This can be done despite the lack of time series data (i.e., it does not require temporal replication at sites), temporal biases in the amount of data, and highly uneven sampling area. These estimates can be mapped at any desired spatial resolution. 4. In all, this is a user-friendly and computationally fast approach with minimal require-ments on data format. It can integrate heterogeneous biodiversity data to obtain esti-mates of temporal biodiversity change, loss, and gain, that would otherwise be invisi-ble in the raw data alone.

Fossil crinoids are exceptionally suited to deep-time studies of community paleoecology and niche partitioning. By merging ecomorphological trait and phylogenetic data, this study summarizes niche occupation and community paleoecology of crinoids from the Bromide fauna of Oklahoma (Sandbian, Upper Ordovician). Further, patterns of community structure and niche evolution are evaluated over a ~5 million-year period through comparison with the Brechin Lagerstätte (Katian, Upper Ordovician). We establish filtration fan density, food size selectivity, and body size as major axes defining niche differentiation, and niche occupation is strongly controlled by phylogeny. Ecological strategies (i.e., adaptive zones) were relatively static over the study interval at high taxonomic scales, but niche differentiation and specialization increased in most subclades. Changes in disparity and species richness indicate the transition between the early-middle Paleozoic Crinoid Evolutionary Faunas was already underway by the Katian due to ecological drivers and was not triggered by the Late Ordovician mass extinction.

Ecological communities change due to both natural and human factors. Distinguishing between the two is critical to ecology and conservation science. One of the most common approaches for modelling species composition changes is calculating Beta diversity indices and then relating index changes to covariates changes. The main difficulty with these analyses is that Beta diversity indices are paired comparisons, which means indices calculated with the same community are not independent. Mantel tests and Generalised Dissimilarity Modeling are the most commonly used statistical procedures for analysing such data, dealing with the datas dependence using randomisation tests. Here we introduce a model-based approach called BetaBayes that requires first modelling the process that results in Beta diversity indices and then quantifying which model configurations are consistent with the observed data. This approach is based on Bradley-Terry model and consists of fitting a regression model containing two varying intercepts that capture the dependence in the data. We demonstrate the use of this approach by analysing a famous dataset collected in Panama that contains information on multiple 1-ha plots from the rain forests of Panama. Several studies have analysed this dataset using Mantel tests and Generalised Dissimilarity Modeling. We calculated the Bray-Curtis index between all pairs of plots and analysed the relationship between the index and two covariates, geographic distance and elevation. We compared the results of BetaBayes with those from the methods mentioned above. We show that BetaBayes provides a step towards consistently modelling community composition changes and discussing possible extensions and future directions.

Evolutionary and organismal biology, similar to other fields in biology, have become inundated with data. At the same rate, we are experiencing a surge in broader evolutionary and ecological syntheses for which tree-thinking is the staple for a variety of post-tree analyses. To fully take advantage of this wealth of data to discover and understand large-scale evolutionary and ecological patterns, computational data integration, i.e. the use of machines to link data at large scale by shared entities, is crucial. The most common shared entity by which evolutionary and ecological data need to be linked is the taxon to which they belong. In this paper, we propose a set of requirements that a system for defining such taxa should meet for computational data science: taxon definitions should maintain conceptual consistency, be reproducible via a known algorithm, be computationally automatable, and be applicable across the tree of life. We argue that Linnaean names based in Linnaean taxonomy, by far the most prevalent means of linking data to taxa, fail to meet these requirements due to fundamental theoretical and practical shortfalls. We argue that for the purposes of data-integration we should instead use phylogenetic clade definitions transformed into formal logic expressions. We call such expressions phyloreferences, and argue that, unlike Linnaean names, they meet all requirements for effective data-integration.

International audience; Identifying the impacts of anthropogenic fires on biodiversity is imperative for human-influenced tropical rainforests because: i) these ecosystems have been transformed by human-induced fires for millennia; and ii) their effective management is essential for protecting the world's terrestrial biodiversity in the face of global environmental change. While several short-term studies elucidate the impacts of fires on local plant diversity, how plant diversity responds to fire regimes over long timescales (>100 years) is a significant knowledge gap, posing substantial impediment to evidence-based management of tropical social-ecological systems. Using wet evergreen forests of the Western Ghats of India as a model system, we discuss the synergistic effects of anthropogenic fires and enhanced aridity on tropical plant diversity over the past 4000 years by examining fossil pollen-based diversity indices (e.g., pollen richness and evenness, and temporal β-diversity), past fire management, the intervals of enhanced aridity due to reduced monsoon rainfall and land use history. By developing a historical perspective, our aim is to provide region-specific management information for biodiversity conservation in the Western Ghats. We observe that the agroforestry landscape switches between periods of no fires (4000-1800 yr BP, and 1400-400 yr BP) and fires (1800-1400 yr BP, and 400-0 yr BP), with both fire periods concomitant with intervals of enhanced aridity. We find synergistic impacts of anthropogenic fires and aridity on plant diversity uneven across time, pointing towards varied land management strategies implemented by the contemporary societies. For example, during 1800–1400 yr BP, diversity reduced in conjunction with a significant decrease in the canopy cover related to sustained use of fires, possibly linked to large-scale intensification of agriculture. On the contrary, the substantially reduced fires during 400–0 yr BP may be associated with the emergence of sacred forest groves, a cultural practice supporting the maintenance of plant diversity. Overall, notwithstanding apparent changes in fires, aridity, and land use over the past 4000 years, present-day plant diversity in the Western Ghats agroforestry landscape falls within the range of historical variability. Importantly, we find a strong correlation between plant diversity and canopy cover, emphasising the crucial role of maintenance of trees in the landscape for biodiversity conservation. Systematic tree management in tropical social-ecological systems is vital for livelihoods of billions of people, who depend on forested landscapes. In this context, we argue that agroforestry landscapes can deliver win-win solutions for biodiversity as well as people in the Western Ghats and wet tropics at large.

The eradication of invasive species from islands is an important part of managing these ecologically unique and at‐risk regions. Island eradications are complex projects and mathematical models play an important role in supporting efficient and transparent decision‐making. In this review, we cover the past applications of modeling to island eradications, which range from large‐scale prioritizations across groups of islands, to project‐level decision‐making tools. While quantitative models have been formulated and parameterized for a range of important problems, there are also critical research gaps. Many applications of quantitative modeling lack uncertainty analyses, and are therefore overconfident. Forecasting the ecosystem‐wide impacts of species eradications is still extremely challenging, despite recent progress in the field. Overall, the field of quantitative modeling is well‐developed for island eradication planning. Multiple practical modeling tools are available for, and are being applied to, a diverse suite of important decisions, and quantitative modeling is well placed to address pressing issues in the field.

Species distribution models (SDMs) are widely used in ecology, biogeography and conservation biology to estimate relationships between environmental variables and species occurrence data and make predictions of how their distributions vary in space and time. During the past two decades, the field has increasingly made use of machine learning approaches for constructing and validating SDMs. Model accuracy has steadily increased as a result, but the interpretability of the fitted models, for example the relative importance of predictor variables or their causal effects on focal species, has not always kept pace. Here we draw attention to an emerging subdiscipline of artificial intelligence, explainable AI (xAI), as a toolbox for better interpreting SDMs. xAI aims at deciphering the behavior of complex statistical or machine learning models (e.g. neural networks, random forests, boosted regression trees), and can produce more transparent and understandable SDM predictions. We describe the rationale behind xAI and provide a list of tools that can be used to help ecological modelers better understand complex model behavior at different scales. As an example, we perform a reproducible SDM analysis in R on the African elephant and showcase some xAI tools such as local interpretable model-agnostic explanation (LIME) to help interpret local-scale behavior of the model. We conclude with what we see as the benefits and caveats of these techniques and advocate for their use to improve the interpretability of machine learning SDMs.

Knowledge on the spatio-temporal distribution of invasive plant species is vital to maintain biodiversity in grasslands which are threatened by the invasion of such plants and to evaluate the effect of control activities conducted. Manual digitising of aerial images with field verification is the standard method to create maps of the invasive Lupinus polyphyllus Lindl. (Lupine) in semi-natural grasslands of the UNESCO biosphere reserve “Rhön”. As the standard method is labour- and time-intensive, a workflow was developed to map lupine coverage using an unmanned aerial vehicle (UAV)-borne remote sensing (RS) along with object-based image analysis (OBIA). UAV-borne red, green, blue (R, G, B) and thermal imaging, as well as photogrammetric canopy height modelling (CHM), were applied. Images were segmented by unsupervised parameter optimisation into image objects representing lupine plants and grass vegetation. Image objects obtained were classified using random forest classification modelling based on objects’ attributes. The developed classification model was employed to create lupine distribution maps of test areas and predicted data were compared with manually digitised lupine coverage maps. The classification models yielded a mean prediction accuracy of 89 %, and 0.78 mean kappa statistics. The maximum difference in lupine area between classified and digitised lupine maps was 5 %. Moreover, the pixel-wise map comparison showed that 88 % of all pixels matched between classified and digitised maps. Our results indicate that lupine coverage mapping using UAV-borne RS data and OBIA provides similar results as the standard manual digitising method and, thus, offers a valuable tool to map invasive lupine on grasslands.

Long-term monitoring programs are a fundamental part of both understanding system dynamics and informing management decisions. However, monitoring programs not always designed to consider statistical power, site selection, or the full costs and benefits of monitoring. Further, data from monitoring programs with different goals and protocols are now being combined for comparative analyses. Key considerations can be incorporated into the optimal design of a management program with simulations and experiments. Here, we advocate for the expanded use of a third approach: non-random sampling of previously-collected data. This approach conducts experiments with available data to understand the consequences of different monitoring approaches. We first illustrate non-random sampling in the context of monitoring programs to assess species trends. We then apply the approach to a pair of additional, more general case studies to show the versatility of conducting experiments with previously-collected data. Non-random sampling of previously-collected data is underutilized, but has the potential to improve monitoring programs. We show that this approach is useful in monitoring species trends, understanding fisheries and agriculture, as well as other areas. When combined with data on the cost of monitoring, this approach can also be used to assess the value of information gained from monitoring.

The impact of climate change on agricultural practices is rising question marks on future food security of billions of people in tropical and sub-tropical region. Recently introduced, Climate Smart Agriculture (CSA) techniques encourages the practices of sustainable agriculture, increasing adaptive capacity and resilience to shocks at multiple levels. However, in reality, it is extremely difficult to develop a single framework for climate change resilient agricultural practices for different agrarian production landscape. Agriculture accounts for nearly 30% of Indian Gross Domestic Product (GDP) and provide livelihood of nearly two-third of the population of the country. With 60% of the agricultural land still depends on rain-fed irrigation, agrarian production economy of India has become extremely vulnerable to the monsoonal anomaly, heat stress, pest invasion and increasing rate of extreme events in upcoming decades. Due to their close relationship with environment and resources, indigenous people are considered as one of the most vulnerable community due to the changing climate. In the milieu of the climate emergency, we have selected multiple indigenous tribes from different agro-ecological zones over India in the present study to explore the adaptive potential of Indigenous Traditional Knowledge (ITK) based agricultural practices against climate change. The selected tribes are inhabitants of eastern Himalaya (Apatani), western Himalaya (Lahaulas), Eastern Ghat (Dongria-Gondh) and Western Ghat (Irular) representing rainforest, cold desert, moist upland and rain shadow landscape respectively. The effect of climate change over the respective regions has been identified using different IPCC scenario and agricultural practices resilient to climate change are quantified. Primary results indicate moderate to extreme susceptibility and preparedness of the tribes against climate change due to the exceptionally adaptive ITK based agricultural practices. A policy brief has been prepared where knowledge exchange and technology transfer among the indigenous tribes have been suggested to achieve complete climate change resiliency.

A prominent signal of the Anthropocene is the extinction and population reduction of the megabiota – the largest animals and plants on the planet. However, we lack a predictive framework for the sensitivity of megabiota during times of rapid global change and how they impact the functioning of ecosystems and the biosphere. Here, we extend metabolic scaling theory and use global simulation models to demonstrate that the megabiota (i) are more prone to extinction due to human land use, hunting, and climate change; (ii) their loss has a negative impact on ecosystem metabolism and functioning; and (iii) their continued reduction will significantly decrease biosphere functioning. Analyses of several forest and animal datasets and large-scale simulation models largely support these predictions. Global simulations show that continued loss of large animals could lead to a 44%, 18% and 92% reduction in terrestrial heterotrophic biomass, metabolism, and fertility respectively. Landscapes with megabiota buffer ecosystem functioning, diversity, and likely human health. As a result, policies that emphasize the promotion of large trees and animals will have disproportionate impact on biodiversity, ecosystem processes, and climate mitigation.

Understanding species-specific relationships with their environment is essential for ecology, biogeography, and conservation biology. Moreover, understanding how these relationships change with spatial scale is critical to mitigating potential threats to biodiversity. But methods which measure inter-specific variation in responses to environmental parameters, generalizable across multiple spatial scales, are lacking. We used broad-scale citizen science data, over a continental scale, integrated with remotely-sensed products, to produce a measure of response to urbanization for a given species at a continental-scale. We then compared these responses to modelled responses to urbanization at a local-scale, based on systematic sampling within a series of small cities. For 49 species which had sufficient data for modelling, we found a significant relationship (R2 = 0.51) between continental-scale urbanness and local-scale urbanness. Our results suggest that continental-scale responses are representative of small-scale responses to urbanization. We also found that relatively few citizen science observations (~250) are necessary for reliable estimates of continental-scale urban scores to predict local-scale response to urbanization. Our method of producing species-specific urban scores is robust and can be generalized to other taxa and other environmental variables with relative ease.

Current paper details methodology and principles of the zoning and ranging of the nature reserve area of Šumava National Park (Czech Republic) aimed at the effective planning and monitoring special nature areas.Methodology includes complex geoecological assessment of the territory, GIS application and processing of statristical data. The specific case study includes unique nature area of the Šumava National Park located on the border terrotory Czech Republic-Germany. The methods used in this case study can be applied for similar research aimed at nature conservation and environmental audit.

Градов, О. В. Многофакторная патч-кламп-спектроскопия как метод характеризации сигнальных систем растений и источник комплементарных систематических дескрипторов для биохимической таксономии с привязкой к биогеографическим картам и феноспектральной ауксанометрии. In Сигнальные системы растений: от рецептора до ответной реакции организма (2016), Изд-во Санкт-Петербургского государственного университета СПб, pp. 79–81. =[Multiparametric multivariate patch-clamp-spectroscopy as a method for plant signaling characterization and complementary systematic descriptor for biochemical taxonomy and biogeographic biochemical plant signaling mapping based on correlational phenospectal auxanometry]

The Possibility of Telemetric System Design for Automatic Ethological Monitoring of Forest Ants Compared with Mathematical ModelsGradov O.V., Notchenko A.V.This paper provides a novel viewpoint on the tracing of forest ants in natural environment. Our approach offers considerable mathematical advantages, because similar telemetric systems based on a chip (for ex.: charged coupled device) with computer aided data analysis can process on-line-monitoring information extremely rapidly. One can generalize this idea by introducing any other principle of ant trace detection in addition to the luminescent method proposed. With sufficient experience, any user would be able to create similar DIY-systems for his tasks. This work offers a wide field for bio-statistical myrmecological research. [Градов, О. В., and Нотченко, А. В. О возможности создания телеметрических установок для автоматического мониторинга этологии лесных муравьёв в сопоставлении с математической моделью. In Муравьи и защита леса. Мат. XIV Всероссийского мирмекологического симпозиума, Москва, 19-23 августа 2013 года (2013), М.: Тов-во научных изданий КМК, pp. 41–44.]

Градов, О. В. Микротоннельные многофакторные телеметрические установки как общедоступные зонды для поточного исследования болотных сред. In Болотные экосистемы: фундаментальные аспекты охраны и рационального природопользования (2012), ПГТУ, Йошкар-Ола, pp. 134–138.

Градов, О. В. Физико-химическая биоценотическая и биогеографическая систематика водных растений на основе комплексных показателей реактивности к гидрохимически обусловленным факторам. In Материалы Международной конференции "Проблемы систематики и географии водных растений" (2015), Ярославль, pp. 28–29.

Basic Principles of Myrmecological Lab-on-a-Chip Design & Application in Ethological Monitoring of Forest Ants for Automatic Comparison with Mathematical ModelsGradov O.V., Notchenko A.V.This paper describes a new modern construction for ethological monitoring in forest ant colonies. The method to be described here involves some low-cost materials and components. Charge coupled devices or new complementary-symmetry metal–oxide–semiconductor sensors with artificial labyrinth tracks have been successfully used in formicariums or ant colonies. We strongly recommend fluorescent markers as indicators of ant tracks on CMOS or CCD sensors, because this detection method is the most suitable for the problem under consideration. The reader is welcome to check that such similar DIY-systems are both simple and handy for use in myrmecological practice. Градов, О. В., and Нотченко, А. В. Принципы создания и эксплуатации diy-лабораторий на чипе для автоматического мониторинга этологии лесных муравьёв в сравнении с математической моделью. In [Муравьи и защита леса. Мат. XIV Всероссийского мирмекологического симпозиума, Москва, 19-23 августа 2013 года (2013), М.: Тов-во научных изданий КМК, pp. 45–50].

Орехов, Ф. К., and Градов, О. В. Спектральная и спектрозональная лазерная криоскопическая видео-миллиосмометрия – техники контроля осмотического анабиоза полихет в неспецифических солевых средах. In ЭКСПЕРИМЕНТАЛЬНАЯ И ТЕОРЕТИЧЕСКАЯ БИОФИЗИКА ‘15 (2015), Пущино, p. 149.

We present a regionalization of the entire Earth’s landmass into land units of homogeneous landscape patterns. The input to the regionalization is a high resolution Global Land Cover (GLC) dataset. The GLC is first divided into local landscapes – small non-overlapping square blocks of GLC cells. These blocks are agglomerated into much larger land units using a pattern-based segmentation algorithm. These units are tracts encompassing cohesive patterns of land cover and the procedure divides the entire landmass into tracts of land with discernibly different patterns. We characterize a pattern in each unit by a set of 39 landscape metrics. The resulting spatial database of land units is the major product of this study. We make this database freely available to the community in order to provide foundational information for studies aiming at explaining relationships between landscape pattern and ecological process and between the process and patterns and their controlling factors. The procedure of obtaining the database is described, the quality assessment of units delineation is given, and the statistics of the major properties of the units are presented. To showcase the utility of the new database we use it to demonstrate that a variability of geometric configurations of landscape patterns worldwide can be captured in terms of only two variables – complexity and aggregation – as they explain 70% of the variability. This allows for a meaningful, two-dimensional classification and mapping of landscape patterns on the basis of their geometry. Such mapping reveals that the majority of terrestrial landscapes are characterized by a simple, frequently monothematic, pattern of land cover. Thus, landscapes on Earth are mostly segregated by the land cover type and complex landscapes with a diverse mix of different land cover types are rare exceptions from the prevailing monothematic cover.