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• Automating wildlife monitoring with wireless 4G cameras and end-to-end data streams. • Remote monitoring of sensor performance, API handling and automated task management. • Deep learning for automated identification of focal species and human detection. • Total cost saving of >40 % through automation, AI and less regular site visits. • Enabling technologies allow scaling-up of a cost-efficient biodiversity monitoring. Modern approaches with advanced technology can automate and expand the extent and resolution of biodiversity monitoring. We present the development of an innovative system for automated wildlife monitoring in a coastal Natura 2000 nature reserve of the Netherlands with 65 wireless 4G wildlife cameras which are deployed autonomously in the field with 12 V/2A solar panels, i.e. without the need to replace batteries or manually retrieve SD cards. The cameras transmit images automatically (through a mobile network) to a sensor portal, which contains a PostgreSQL database and functionalities for automated task scheduling and data management, allowing scientists and site managers via a web interface to view images and remotely monitor sensor performance (e.g. number of uploaded files, battery status and SD card storage of cameras). The camera trap sampling design combines a grid-based sampling stratified by major habitats with the camera placement along a traditional monitoring route, and with an experimental set-up inside and outside large herbivore exclosures. This provides opportunities for studying the distribution, habitat use, activity, phenology, population structure and community composition of wildlife species and allows comparison of traditional with novel monitoring approaches. Images are transferred via application programming interfaces to external services for automated species identification and long-term data storage. A deep learning model for species identification was tested and showed promising results for identifying focal species. Furthermore, a detailed cost analysis revealed that establishment costs of the automated system are higher but the annual operating costs much lower than those for traditional camera trapping, resulting in the automated system being >40 % more cost-efficient. The developed end-to-end data pipeline demonstrates that continuous monitoring with automated wildlife camera networks is feasible and cost-efficient, with multiple benefits for extending the current monitoring methods. The system can be applied in open habitats of other nature reserves with mobile network coverage. [ABSTRACT FROM AUTHOR]

[Display omitted] • UAV and airborne LiDAR surveys require standardized reporting of data and metadata. • Open-source tools can enhance transparency in geospatial data processing workflows. • Robust metrics and model transferability are needed for reliable habitat indicators. • Big data processing for habitat mapping demands parallel or distributed computing. • Cloud-based VREs can enhance high-resolution habitat monitoring with UAVs and LiDAR. Indicators of habitat condition are essential for tracking conservation progress, but measuring biotic, abiotic and landscape characteristics at fine resolution over large spatial extents remains challenging. In this viewpoint article, we provide a comprehensive synthesis of the challenges and solutions for consistently measuring and monitoring habitat condition with remote sensing using airborne Light Detection and Ranging (LiDAR) and affordable Unmanned Aerial Vehicles (UAVs) over multiple sites and transnational or continental extents. Key challenges include variability in sensor characteristics and survey designs, non-transparent pre-processing workflows, heterogeneous and complex data, issues with the robustness of metrics and indices, limited model generalizability and transferability across sites, and difficulties in handling big data, such as managing large volumes and utilizing parallel or distributed computing. We suggest that a collaborative cloud virtual research environment (VRE) for habitat condition research and monitoring could provide solutions, including tools for data discovery, access, and data standardization, as well as geospatial processing workflows for airborne LiDAR and UAV data. A VRE would also improve data management, metadata standardization, workflow reproducibility, and transferability of structure-from-motion algorithms and machine learning models such as random forests and convolutional neural networks. Along with best practices for data collection and adopting FAIR (findability, accessibility, interoperability, reusability) principles and open science practices, a VRE could enable more consistent and transparent data processing and metric retrieval, e.g., for Natura 2000 habitats. Ultimately, these improvements would support the development of more reliable habitat condition indicators, helping prevent habitat degradation and promoting the sustainable use of natural resources. [ABSTRACT FROM AUTHOR]

• The Kunming-Montreal global biodiversity framework was systematically reviewed. • Indicators for tracking the state of terrestrial biodiversity were identified. • Indicators mainly use species or ecosystem distribution and fractional cover data. • A priority list shows innovation potential for satellite remote sensing workflows. • Opportunities arise to advance products for ecosystem structure and plant traits. Satellite remote sensing (SRS) provides huge potential for tracking progress towards conservation targets and goals, but SRS products need to be tailored towards the requirements of ecological users and policymakers. In this viewpoint article, we propose to advance SRS products with a terrestrial biodiversity focus for tracking the goals and targets of the Kunming-Montreal global biodiversity framework (GBF). Of 371 GBF biodiversity indicators, we identified 58 unique indicators for tracking the state of terrestrial biodiversity, spanning 2 goals and 8 targets. Thirty-six shared enough information to analyse their underlying workflows and spatial information products. We used the concept of Essential Biodiversity Variables (EBV) to connect spatial information products to different dimensions of biodiversity (e.g. species populations, species traits, and ecosystem structure), and then counted EBV usage across GBF goals and targets. Combined with published scores on feasibility, accuracy, and immaturity of SRS products , we identified a priority list of terrestrial SRS products representing opportunities for scientific development in the next decade. From this list, we suggest two key directions for advancing SRS products and workflows in the GBF context using current instruments and technologies. First, existing terrestrial ecosystem distributions and live cover fraction SRS products (of above-ground biomass, ecosystem fragmentation, ecosystem structural variance, fraction of vegetation cover, plant area index profile, and land cover) need to be refined using a co-design approach to achieve harmonized ecosystem taxonomies, reference states and improved thematic detail. Second, new SRS products related to plant physiology and primary productivity (e.g. leaf area index, chlorophyll content & flux, foliar N/P/K content, and carbon cycle) need to be developed to better estimate plant functional traits, especially with deep learning techniques, radiative transfer models and multi-sensor frameworks. Advancements along these two routes could greatly improve the tracking of GBF target 2 ('improve connectivity of priority terrestrial ecosystems), target 3 ('ensure management of protected areas'), target 6 ('control the introduction and impact of invasive alien species'), target 8 ('minimize impact of climate change on biodiversity'), target 10 ('increase sustainable productivity of agricultural and forested ecosystems') and target 12 ('increase public urban green/blue spaces'). Such improvements can have secondary benefits for other EBVs, e.g. as predictor variables for modelling species distributions and population abundances (i.e. data that are required in several GBF indicators). We hope that our viewpoint stimulates the advancement of biodiversity monitoring from space and a stronger collaboration among ecologists, SRS scientists and policy experts. [ABSTRACT FROM AUTHOR]

Tropical forests support immense biodiversity and provide essential ecosystem services for billions of people. Despite this value, tropical deforestation continues at a high rate. Emerging evidence suggests that elections can play an important role in shaping deforestation, for instance by incentivising politicians to allow increased utilisation of forests in return for political support. Nevertheless, the role of elections as driver of deforestation has not yet been comprehensively tested at broad geographic scales. Here, we created an annual database from 2001 to 2018 on political elections and forest loss for 55 tropical nations and modelled the effect of elections on deforestation. In total, 1.5 million km2 of forest was lost during this time period, especially in the Amazon, the Congo Basin and in Southeast Asia. The annual rate of deforestation increased in 37 (67 %) of the analysed countries. Deforestation was significantly lower in years with uncompetitive lower chamber elections compared to competitive election years (i.e. when the opposition can participate in elections and has a legitimate chance to gain governmental power). Our results show a pervasive loss of tropical forests and suggest that competitive elections can be potential drivers of deforestation. Future analyses at higher resolution (intra-annual deforestation and sub-national governance) and simultaneous collection of data on additional mechanisms (legislative changes, financial investments, and binding term limits) will likely provide additional insights into the impacts of elections. We therefore recommend that organisations monitoring election transparency and fairness should also monitor environmental impacts such as forest loss, habitat destruction and resource exploitation. • Tropical deforestation is continuing to increase in most countries. • Uncompetitive election years are associated with lower deforestation than non-election years. • Competitive elections are linked with higher forest loss compared to uncompetitive elections. • Results suggest that politicians may use natural resources to win elections. [ABSTRACT FROM AUTHOR]

Abstract: Current forestry practices in Chile largely rely on exotic tree plantations, and limited management experiences are available for the species-rich native evergreen rain forests. Yet, conservationists and forest scientists call for sustainable management of native forests as an alternative to plantations so as to maintain important ecosystem services. We parameterised the process-based forest growth model FORMIND for a Valdivian coastal temperate rain forest in Chiloé Island, Chile, to assess the ecological implications of different logging practices including selective logging and strip-cutting. We tested the model by comparing simulation results with field data from the study site and carried out an extensive sensitivity analysis to explore the impacts of parameter values on model results. Simulated logging practices were compared in regard to expected timber harvest and long-term impacts on forest structure and composition. Results showed that highest harvests could be achieved when strip-cutting was applied, because it promoted the regeneration of the relatively light-demanding and fast-growing Eucryphia cordifolia. However, forest structure and composition were severely altered by this practice. In contrast, selective logging, although providing lower harvests, better conserved old-growth forest structure and composition. Canopy gaps created by selective logging were not large enough to ensure regeneration of E. cordifolia, but favoured the shade-tolerant Laureliopsis philippiana. Overall, the similarity of logged stands to undisturbed forest decreased linearly with increasing harvesting intensity. Management strategies that rely on native species and keep an uneven-aged forest structure ensure the maintenance of native biodiversity, protect ecosystems from exotic species invasions, and promote the conservation of biotic interactions essential for tree reproduction. [Copyright &y& Elsevier]

Abstract: The breeding system of the European tussock grass Nardus stricta L. (Poaceae) was investigated with pollination experiments. Plants were sampled from two populations at Lake Pukaki, Canterbury, New Zealand, where the species is recognised as an alien invader. Bagging of flowers with king-sized cigarette paper and hand-pollination were used to test for three modes of reproduction in the greenhouse: (1) agamospermy (apomixis), (2) autogamy and self-compatibility, and (3) allogamy (outcrossing). Two control groups without experimental treatments were further tested for seed set under (1) greenhouse and (2) field conditions. The success or failure of all experimental treatments was assessed with seed set and germination trials. All agamospermy treatments showed high seed set and germination proportions arguing for an apomictic mode of reproduction in Nardus stricta. Cross-pollination treatments were also successful making it difficult to estimate the degree of outcrossing, selfing, and agamospermous seed production in Nardus stricta. Fecundity in field populations was considerably reduced, possibly due to environmental factors acting upon seed development during maturation. The reproductive strategy of Nardus stricta might be particularly beneficial during invasion because single tussocks can form reproducing colonies and high reproductive output is ensured even in the absence of pollination. Genetic studies in combination with pollination experiments would be necessary to gain deeper insights into the breeding strategy of Nardus stricta. [Copyright &y& Elsevier]

Plant–animal interactions are fundamentally important in ecosystems, but have often been ignored by studies of climate-change impacts on biodiversity. Here, we present a trait-based framework for predicting the responses of interacting plants and animals to climate change. We distinguish three pathways along which climate change can impact interacting species in ecological communities: (i) spatial and temporal mismatches in the occurrence and abundance of species, (ii) the formation of novel interactions and secondary extinctions, and (iii) alterations of the dispersal ability of plants. These pathways are mediated by three kinds of functional traits: response traits, matching traits, and dispersal traits. We propose that incorporating these traits into predictive models will improve assessments of the responses of interacting species to climate change. Climate change is leading to a fundamental reorganization of ecological communities. Current biodiversity models are mostly limited to the responses of individual species to climate change. Interactions between species, though critically important for species' performance and survival, are less frequently considered in biodiversity models. Functional traits of plants and animals can be used to quantify their environmental requirements, their probability of interaction in ecological networks, and their movement and dispersal ability. New predictive biodiversity models can be developed which incorporate functional traits and species interactions to better forecast interdependent species responses, novel communities, and eco-evolutionary consequences of climate change. [ABSTRACT FROM AUTHOR]

Objective: Non-adherence is a major problem in the treatment of schizophrenia. Depot antipsychotic drugs are thought to reduce relapse rates by improving adherence, but a systematic review of long-term studies in outpatients is not available.Method: We searched the Cochrane Schizophrenia Group's register, ClinicalTrials.gov, Cochrane reviews on depot medication, and the reference sections of included studies for randomised controlled trials lasting at least 12 months in outpatients that compared depot with oral antipsychotics in schizophrenia. Data on relapse (primary outcome), rehospitalisation, non-adherence, and dropout due to any reason, inefficacy of treatment and adverse events were summarised in a meta-analysis using a random-effects model. Study quality was assessed with the Cochrane collaboration's risk of bias tool, and publication bias with funnel plots.Results: Ten studies with 1700 participants met the inclusion criteria. Depot formulations significantly reduced relapses with relative and absolute risk reductions of 30% and 10%, respectively (RR 0.70, CI 0.57-0.87, NNT 10, CI 6-25, P=0.0009), and dropout due to inefficacy (RR 0.71, CI 0.57-0.89). Limited data on non-adherence, rehospitalisation and dropout due to any reason and adverse events revealed no significant differences. There were several potential sources of bias such as limited information on randomisation methods, problems of blinding and different medications in the depot and oral groups. Other studies reduced a potential superiority of depot by excluding non-adherent patients.Discussion: Depot antipsychotic drugs significantly reduced relapse. Due to a number of methodological problems in the single trials the evidence is, nonetheless, subject to possible bias. [ABSTRACT FROM AUTHOR]

Objective: To use the degree of response after 2 weeks of treatment to predict non-response at 4 to 6 weeks.Method: Post-hoc re-analysis of a large multi-centered double-blind trial including 1996 patients with schizophrenia using receiver-operator curves and logistic regression analyses to predict non-response at 4 weeks and at 4-6 weeks from the percentage BPRS change at weeks 1 and 2. The primary non-response criterion was a less than 25% BPRS reduction from baseline.Results: A 0% BPRS reduction at 2 weeks predicted non-response at 4 weeks with a positive predictive value of 77.1%; and sustained non-response at weeks 4, 5 and 6 with a positive predictive value of 75.8%. In a secondary last-observation-carried forward-analysis a less stringent cutoff of < or =15% BPRS reduction was associated with an acceptable positive predictive value (75%), with even higher sensitivity (76%).Conclusions: Those patients who showed little to no reduction of symptoms at week 2 were unlikely to show even minimal response at weeks 4 to 6. There is increasing evidence that such patients may benefit from a change in treatment. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To evaluate whether peer-counseling, common in non-psychiatric medical fields, is feasible and useful for inpatients with schizophrenia. METHODS: The participants of a one-to-one peer-counseling were given the opportunity to present all questions related to their illness to a peer-counselor, who had himself been living with a schizoaffective disorder for over 20 years. The peer-counseling was evaluated by the participants (STEPP-questionnaire), by the counselor (structured protocol) and by a physician (supervision). Descriptive methods were used for statistical analyses. RESULTS: Eighty-eight patients (mean age 37 years, 35% female, hospitalized for 5 weeks) took part. The main topics addressed were 'symptoms of schizophrenia'; main suggestions were 'taking medication for symptom control and relapse prevention', 'having patience' and providing 'emotional support'. The mean ratings in the STEPP-subscales were: relationship-perspective 18.6 out of 21, problem-solving 19.8 out of 28, clarification-perspective 24.9 out of 35. Ninety-six percent of the participants would recommend this peer-counseling to others. CONCLUSIONS: The results of this pilot-study show that peer-counseling is feasible in schizophrenia. The peer-counselor was able to answer the questions adequately; patients felt well understood and obtained support and encouragement. PRACTICE IMPLICATIONS: The data suggest that peer-counseling is a potentially useful, additional tool for inpatients with schizophrenia as it appears to meet as yet unattended needs. [ABSTRACT FROM AUTHOR]

• Vegetation height can be robustly estimated from different ALS datasets. • The estimation of biomass and LAI is sensitive to the ALS recording types. • Full waveform ALS captures more structural details in dense reed vegetation. Mapping and quantifying 3D vegetation structure is essential for assessing and monitoring ecosystem structure and function within wetlands. Airborne Laser Scanning (ALS) is a promising data source for developing indicators of 3D vegetation structure, but derived metrics are often not compared with 3D structural field measurements and the acquisition of ALS data is rarely standardized across different remote sensing surveys. Here, we compare a set of Light Detection And Ranging (LiDAR) metrics derived from ALS datasets with varying characteristics to a standardized set of field measurements of vegetation height, biomass and Leaf Area Index (LAI) across three Hungarian lakes (Lake Balaton, Lake Fertő and Lake Tisza). The ALS datasets differed in whether the recording type was full waveform (FWF) or discrete return, and in their point density (4 pt/m2 and 21 pt/m2). A total of eight LiDAR metrics captured radiometric information as well as descriptors of vegetation cover, height and vertical variability. Multivariate regression models with field-based measurements of vegetation height, biomass or LAI as response variable and LiDAR metrics as predictors showed major differences between ALS recording types, and were affected by differences in spatial resolution, temporal offset and seasonality between field and ALS data acquisition. Vegetation height could be estimated with high to intermediate accuracy (FWF ALS data only: R2 = 0.84; combination of ALS datasets: R2 = 0.67), demonstrating its potential as a robust indicator of 3D vegetation structure across different ALS datasets. In contrast, the estimation of biomass and LAI in these wetlands was sensitive to variation in ALS characteristics and to the discrepancies between field and ALS data in terms of spatial resolution, temporal offset and seasonality (biomass: R2 = 0.20–0.22; LAI: R2 = 0.08–0.30). We recommend the use of FWF ALS data within wetlands because it captures more vegetation structural details in dense reed and marshland vegetation. We further suggest that ecologists and remote sensing scientist should better coordinate the simultaneous and standardized acquisition of field and ALS data for testing the robustness of quantitative descriptors of vegetation cover, height and vertical variability within wetlands. This is important for establishing operational and spatially contiguous ALS-based indicators of 3D ecosystem structure across wetlands. [ABSTRACT FROM AUTHOR]

Tropical forest ecosystems are undergoing rapid transformation as a result of changing environmental conditions and direct human impacts. However, we cannot adequately understand, monitor or simulate tropical ecosystem responses to environmental changes without capturing the high diversity of plant functional characteristics in the species-rich tropics. Failure to do so can oversimplify our understanding of ecosystems responses to environmental disturbances. Innovative methods and data products are needed to track changes in functional trait composition in tropical forest ecosystems through time and space. This study aimed to track key functional traits by coupling Sentinel-2 derived variables with a unique data set of precisely located in-situ measurements of canopy functional traits collected from 2434 individual trees across the tropics using a standardised methodology. The functional traits and vegetation censuses were collected from 47 field plots in the countries of Australia, Brazil, Peru, Gabon, Ghana, and Malaysia, which span the four tropical continents. The spatial positions of individual trees above 10 cm diameter at breast height (DBH) were mapped and their canopy size and shape recorded. Using geo-located tree canopy size and shape data, community-level trait values were estimated at the same spatial resolution as Sentinel-2 imagery (i.e. 10 m pixels). We then used the Geographic Random Forest (GRF) to model and predict functional traits across our plots. We demonstrate that key plant functional traits can be accurately predicted across the tropicsusing the high spatial and spectral resolution of Sentinel-2 imagery in conjunction with climatic and soil information. Image textural parameters were found to be key components of remote sensing information for predicting functional traits across tropical forests and woody savannas. Leaf thickness (R 2 = 0.52) obtained the highest prediction accuracy among the morphological and structural traits and leaf carbon content (R 2 = 0.70) and maximum rates of photosynthesis (R 2 = 0.67) obtained the highest prediction accuracy for leaf chemistry and photosynthesis related traits, respectively. Overall, the highest prediction accuracy was obtained for leaf chemistry and photosynthetic traits in comparison to morphological and structural traits. Our approach offers new opportunities for mapping, monitoring and understanding biodiversity and ecosystem change in the most species-rich ecosystems on Earth. Unlabelled Image • We use a pantropical array of precisely geolocated leaf traits from 2434 trees. • We assess the ability of Sentinel-2 imagery to map leaf traits in tropical forests. • Functional traits can be mapped at the pixel-level across forests with Sentinel-2. • Leaf photosynthetic and chemical traits show the highest detectability. • Spatial texture information makes an important contribution to mapping leaf traits. [ABSTRACT FROM AUTHOR]