Detecting management gaps for biodiversity conservation: An integrated assessment.

The latest report on the state of nature in Europe (2013-2018) shows that biodiversity is declining at an alarming rate, with most protected species and habitats in poor condition. Despite an increasing volume of collected biodiversity information, urgent action is needed to integrate biodiversity data and knowledge to improve conservation efforts. We conducted a study in Catalonia (NE Spain), where we collected management measures implemented between 2013 and 2018, including allocation, budget, pressures aimed, and habitats/species potentially benefiting. We integrated information on pressures and habitats/species with the measures to identify non-spatial management gaps. Then, we integrated the spatially explicit information to determine the spatial management gap, identifying geographical areas where species/habitats are under pressure without registered measures. We demonstrated the importance of integrating existing information. Our findings revealed that resources were often not distributed adequately across species/habitats, with biases towards certain taxa being a common issue. The non-spatial management gap analysis identified taxonomic groups, especially plants and mollusks with the wider management gaps. We also identified threatened areas, especially in the northeast of the region with the larger spatial management gaps. These results could guide priority objectives to optimize conservation efforts. Integrating different information sources provided a broader view of the challenges that conservation science is facing nowadays. Our study offers a path toward bending the curve of biodiversity loss by providing an integrative framework that could optimize the use of the available information and help narrow the knowing-doing gap. In the context of the EU, this example demonstrates how information can be used to promote some environmental policy instruments, such as the Prioritized Action Frameworks (PAFs). Additionally, our findings highlight the importance of supporting decision-making with systematic assessments to identify deficiencies in the conservation process, reduce the loss of critical ecosystems and species, and avoid biases among taxa.

Mapping drivers of change for biodiversity risk assessment to target conservation actions: Human frequentation in protected areas.

Mapping the drivers of change that pose negative pressures or threats to biodiversity can help to identify where biodiversity is most threatened and can be used to determine priority sites to target conservation actions. Overlapping drivers of change maps with distribution maps of sensitive species provides valuable information to identify where and when it would be better to target actions to minimize the risk. The overall aim of this study was to develop a methodology for the integration of risk mapping associated with high human frequentation to guide conservation actions in two case study: the Kentish plover (Charadrius alexandrinus) and Posidonia meadows (Posidonia oceanica), both sensitive to human frequentation. To achieve this, we used two types of geolocated mobile phone information from the STRAVA platform: mapped paths and roads number of visitors at hourly precisions and a sporting activities heatmap representative of a wider period, together with species ecological information and complementary human frequentation data. The final, monthly risk maps identified the areas for Kentish plover with null, low, moderate, high, very high risk attributed to different aspects of the breeding biology of the species, nests, nestlings, and adults. The risk thresholds for nests are lower than for nestlings and adults, thought nestlings were generally less sensitive to human frequentation than adults. Visitors number ranges between 250 and 700 approximately suppose a moderate risk for the three assessed periods, and more than 1200 visitors appeared to prevent the nesting of the species completely. The final risk maps for Posidonia meadows determine the areas with low, moderate, hight and very high risk for human marine activities. Human frequentation values in this case study are scaled between 0 and 1, the results shows that values above 0.1 imply a high risk for the species. Both types of information can be used to target concrete, spatially explicit actions to minimize the risk caused by human frequentation. Furthermore, the first case study would allow to adapt the target actions to the species breeding phenology. The proposed risk assessment workflow is flexible and may be adjusted to match the available information and eventually could be adapted to other conservation objectives arising from different threats. In addition, data gathered from mobile mobility applications show great potential to accurately identify human frequentation, both spatially and temporally.

Quantifying pine processionary moth defoliation in a pine-oak mixed forest using unmanned aerial systems and multispectral imagery.

Pine processionary moth (PPM) feeds on conifer foliage and periodically result in outbreaks leading to large scale defoliation, causing decreased tree growth, vitality and tree reproduction capacity. Multispectral high-resolution imagery acquired from a UAS platform was successfully used to assess pest tree damage at the tree level in a pine-oak mixed forest. We generated point clouds and multispectral orthomosaics from UAS through photogrammetric processes. These were used to automatically delineate individual tree crowns and calculate vegetation indices such as the normalized difference vegetation index (NDVI) and excess green index (ExG) to objectively quantify defoliation of trees previously identified. Overall, our research suggests that UAS imagery and its derived products enable robust estimation of tree crowns with acceptable accuracy and the assessment of tree defoliation by classifying trees along a gradient from completely defoliated to non-defoliated automatically with 81.8% overall accuracy. The promising results presented in this work should inspire further research and applications involving a combination of methods allowing the scaling up of the results on multispectral imagery by integrating satellite remote sensing information in the assessments over large spatial scales.

Calibrating the Severity of Forest Defoliation by Pine Processionary Moth with Landsat and UAV Imagery.

The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools.

Historical citizen science to understand and predict climate-driven trout decline.

Historical species records offer an excellent opportunity to test the predictive ability of range forecasts under climate change, but researchers often consider that historical records are scarce and unreliable, besides the datasets collected by renowned naturalists. Here, we demonstrate the relevance of biodiversity records developed through citizen-science initiatives generated outside the natural sciences academia. We used a Spanish geographical dictionary from the mid-nineteenth century to compile over 10 000 freshwater fish records, including almost 4 000 brown trout (Salmo trutta) citations, and constructed a historical presence-absence dataset covering over 2 000 10 × 10 km cells, which is comparable to present-day data. There has been a clear reduction in trout range in the past 150 years, coinciding with a generalized warming. We show that current trout distribution can be accurately predicted based on historical records and past and present values of three air temperature variables. The models indicate a consistent decline of average suitability of around 25% between 1850s and 2000s, which is expected to surpass 40% by the 2050s. We stress the largely unexplored potential of historical species records from non-academic sources to open new pathways for long-term global change science.