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Long-term analyses of biodiversity data highlight a 'biodiversity conservation paradox': biological communities show substantial species turnover over the past century1,2, but changes in species richness are marginal1,3-5. Most studies, however, have focused only on the incidence of species, and have not considered changes in local abundance. Here we asked whether analysing changes in the cover of plant species could reveal previously unrecognized patterns of biodiversity change and provide insights into the underlying mechanisms. We compiled and analysed a dataset of 7,738 permanent and semi-permanent vegetation plots from Germany that were surveyed between 2 and 54 times from 1927 to 2020, in total comprising 1,794 species of vascular plants. We found that decrements in cover, averaged across all species and plots, occurred more often than increments; that the number of species that decreased in cover was higher than the number of species that increased; and that decrements were more equally distributed among losers than were gains among winners. Null model simulations confirmed that these trends do not emerge by chance, but are the consequence of species-specific negative effects of environmental changes. In the long run, these trends might result in substantial losses of species at both local and regional scales. Summarizing the changes by decade shows that the inequality in the mean change in species cover of losers and winners diverged as early as the 1960s. We conclude that changes in species cover in communities represent an important but understudied dimension of biodiversity change that should more routinely be considered in time-series analyses.

Large-scale biodiversity databases have great potential for quantifying long-term trends of species, but they also bring many methodological challenges. Spatial bias of species occurrence records is well recognized. Yet, the dynamic nature of this spatial bias – how spatial bias has changed over time – has been largely overlooked. We examined the spatial bias of species occurrence records within multiple biodiversity databases in Germany and tested whether spatial bias in relation to land cover or land use (urban and protected areas) has changed over time. We focused our analyses on urban and protected areas as these represent two well-known correlates of sampling bias in biodiversity datasets. We found that the proportion of annual records from urban areas has increased over time while the proportion of annual records within protected areas has not consistently changed. Using simulations, we examined the implications of this changing sampling bias for estimation of long-term trends of species' distributions. When assessing biodiversity change, our findings suggest that the effects of spatial bias depend on how it affects sampling of the underlying land-use change drivers affecting species. Oversampling of regions undergoing the greatest degree of change, for instance near human settlements, might lead to overestimation of the trends of specialist species. For robust estimation of the long-term trends in species' distributions, analyses using species occurrence records may need to consider not only spatial bias, but also changes in the spatial bias through time.

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in

Large-scale biodiversity databases have great potential for quantifying long-term trends of species, but they also bring many methodological challenges. Spatial bias of species occurrence records is well recognized. Yet, the dynamic nature of this spatial bias – how spatial bias has changed over time – has been largely overlooked. We examined the spatial bias of species occurrence records within multiple biodiversity databases in Germany and tested whether spatial bias in relation to land cover or land use (urban and protected areas) has changed over time. We focused our analyses on urban and protected areas as these represent two well-known correlates of sampling bias in biodiversity datasets. We found that the proportion of annual records from urban areas has increased over time while the proportion of annual records within protected areas has not consistently changed. Using simulations, we examined the implications of this changing sampling bias for estimation of long-term trends of species' distributions. When assessing biodiversity change, our findings suggest that the effects of spatial bias depend on how it affects sampling of the underlying land-use change drivers affecting species. Oversampling of regions undergoing the greatest degree of change, for instance near human settlements, might lead to overestimation of the trends of specialist species. For robust estimation of the long-term trends in species' distributions, analyses using species occurrence records may need to consider not only spatial bias, but also changes in the spatial bias through time.

Christensen et al. criticized the application of Beals’ index of sociological favourability to adjust for incomplete species lists when comparing repeated surveys. Their main argument was that using Beals’ conditional occurrence probabilities would systematically underestimate biodiversity change compared to using observed frequencies. Although this might be the case for rare species, as we explicitly stated in our original publication, we here use a worked-out example to show that this criticism is unjustified for species that are sufficiently represented in the reference data set. In our opinion, the misconception derives from ignoring one of the key requirements for applying Beal's index, which is the use of a sufficiently large reference data set to derive a reliable co-occurrence matrix. We here show how the predicted probability for the occurrence of a species depends on the size of the reference data set and give recommendations on the premises for applying Beals’ approach for monitoring purposes.

Seit Inkrafttreten des Stromeinspeisegesetzes im Jahr 1991 und des Erneuerbaren-Energien-Gesetzes (EEG) im Jahr 2000 entwickeln sich die erneuerbaren Energien (EE) insbesondere im Strombereich rasant. Wurden im Jahr 1990 nur ca. 19 TWh Strom aus EE erzeugt, waren es im Jahr 2017 schon ca. 216 TWh. EE nehmen nach Braun- und Steinkohle damit den zweiten Rang im deutschen Strommix ein. Ihr Anteil am Bruttostromverbrauch lag im Jahr 2017 bereits bei ca. 36 %. Ein weiterer kräftiger Ausbau ist notwendig, um bis zum Jahr 2050 den Zielwert von 80 % Anteil am Bruttostromverbrauch zu erreichen.Die Notwendigkeit des Umbaus des Energiesystems sowie die positiven Effekte der EE für die Reduktion von Treibhausgasemissionen sind unstrittig. Klimaschutz ist eine wichtige Voraussetzung für den Erhalt von Natur und Landschaft und trägt dazu bei, das Überleben von Arten langfristig zu sichern. Galten EE lange Zeit als per se umweltfreundlich, so treten mit zunehmendem Ausbau Auswirkungen auf Natur und Umwelt deutlicher zu Tage, die zu zunehmenden Konflikten mit den Zielen des Naturschutzes führen können. Eine bundesweite, systematische und wiederkehrende Beschreibung, die die durch die Errichtung und den Betrieb von Anlagen zur Erzeugung und Übertragung von Strom aus EE verursachten Beeinträchtigungen von Natur und Umwelt bündelt, gibt es derzeit noch nicht. Ein solches Monitoringsystem könnte unter anderem sowohl die nationale Berichterstattung zum Ausbau der EE um die Auswirkungen auf Natur und Landschaft erweitern als auch mittelfristig das Wirkungswissen schrittweise verbessern. Problematische Entwicklungen könnten damit frühzeitig erkannt und vermieden werden. Ziel des Forschungsvorhabens „Naturschutzfachliches Monitoring des Ausbaus der erneuerbaren Energien im Strombereich und Entwicklung von Instrumenten zur Verminderung der Beeinträchtigung von Natur und Landschaft“ war daher eine möglichst systematische Erfassung und Darstellung (Monitoring) der Entwicklungen und daraus fol

With the increase in wind turbines, bird collisions have developed as a potential hazard. In the federal state of Brandenburg, Germany, despite the on-going mitigation efforts of increasing the distances of wind turbines from the breeding areas of the more severely affected populations of red kites (Milvus milvus), the additional detrimental influences on the buzzard populations (Buteo buteo) have added to the challenges for wind power expansion. Using data on the regional distribution of the buzzards, along with their carcass detections around the wind turbines (WTs), we aimed to better understand their collision distribution patterns in relation to their habitat use patterns to predict their exposure to collision risk using boosted regression trees (BRTs). Additionally, we integrated the developed collision potential map with the regional density map of buzzards to identify areas of increased strike susceptibility in turbine installations. Our study showed that the buzzard collisions were primarily concentrated at the turbines situated at sensitive distances from the edges of watercourses (>1000 metres), as well as those along the edges of grasslands (>750 metres), in the green open areas around/areas with minimal settlements (750 metres-1750 metres), and along the edges of bushlands (>1500 metres), together explaining 58% of the variance in their collision distribution. Conclusively, our study is applicable to conservation because it demonstrates the identification of potential collision areas along with the causes of the collisions, in addition to demonstrating the benefits of incorporating a species collision dataset as a proxy for species presence into species distribution models to make informed management decisions to eventually combat biodiversity loss.

The risk of collision with wind turbines remains a critical issue for bird conservation. Undoubtedly, for the continued development of wind farms to increase the energy capacity, wind farm locations must be carefully chosen going forward. This can be achieved not only by avoiding areas with higher bird densities but also by avoiding installations at sensitive distances from their ecologically important land‐use types. Through analyses of the Euclidean distances to the different land‐use types, we utilized the random forest (RF) machine learning algorithm to model the distance‐based impacts of wind turbine locations on detected bird collisions for the frequently‐hit groups of birds at WTs. Although, the predicted areas with potential collision risk in total had a small but highly dispersed expanse of ~2,130 km2 across the vast 29,479 km2 area of the federal state. Our results further segregated these assessed areas based on their different probabilities of collision thresholds (between 0 and 1) to only detect the areas with collision probabilities <.05, which were interpreted as the actual “no risk areas”. These “no risk areas” summed to a total of merely 754 km2 of the land space in Brandenburg, suggesting that any further planned additions of wind energy farms in the state that is, the proposed wind turbines, to be suitably positioned only in these safer areas. Additionally, the study also enabled the identification of any existing wind turbines already installed in the remaining less safe 28,725 km2 area of the state. These areas are also essential to be include in the collision detection surveys and bird population dynamic studies. This would further our understanding regarding the deleterious consequences of collisions at the population levels of birds, eventually helping in the formulation of adequate mitigation measures.

Efficient biodiversity conservation requires that limited resources be allocated in accordance with national responsibilities and priorities. Without appropriate computational tools, the process of determining these national responsibilities and conservation priorities is time intensive when considering many species across geographic scales. Here, we have developed a computational tool as a module for the ArcGIS geographic information system. The ArcGIS National Responsibility Assessment Tool (NRA-Tool) can be used to create hierarchical lists of national responsibilities and priorities for global species conservation. Our tool will allow conservationists to prioritize conservation efforts and to focus limited resources on relevant species and regions. We showcase our tool with data on 258 bird species and various biophysical regions, including Environmental Zones in 58 Asian countries and regions. Our tool provides a decision support system for conservation policy with attractive and easily interpretable visual outputs illustrating national responsibilities and priorities for species conservation. The graphical output allows for smooth integration into assessment reports, such as the European Article 17 report, the Living Planet Index report, or similar regional and global reports.

Ecology often faces the problem that many threatened species are highly elusive but also conflict-laden.Thus, proper monitoring data are inevitable for their conservation and management. Indirect monitoringthrough scats is frequently used for such species, but scats of related species or species with similar diet areoften visually indistinguishable. Since genetic methods for species identification are time-consuming andcost-intensive, a verification of the target species beforehand would be extremely beneficial in reducing effortto the analysis of the target species only. Such species discrimination could be provided through speciesspecific scat detection dogs. Therefore, we evaluated the reliability of species-specific scat detection dogs for two mustelid species feeding on identical diets: the Eurasian otter (Lutra lutra) and the American mink (Neovison vison), both of which are conflict-laden and increasing their populations and distribution ranges in central Europe. Their scats resemble each other in morphology and odour, exacerbating the differentiation even for experts. To evaluate whether detection dogs can reliably discriminate between related species feeding on similar diets and if their use would be beneficial, we tested their abilities against those of humans.