Dynamic shifts of functional diversity through climate-Resilient strategies and farmland restoration in a mountain protected area.

Land-use land-cover (LULC) change contributes to major ecological impacts, particularly in areas undergoing land abandonment, inducing modifications on habitat structure and species distributions. Alternative land-use policies are potential solutions to alleviate the negative impacts of contemporary tendencies of LULC change on biodiversity. This work analyzes these tendencies in the Montesinho Natural Park (Portugal), an area representative of European abandoned mountain rural areas. We built ecological niche models for 226 species of vertebrates (amphibians, reptiles, birds, and mammals) and vascular plants, using a consensus modelling approach available in the R package 'biomod2'. We projected the models to contemporary (2018) and future (2050) LULC scenarios, under four scenarios aiming to secure relevant ecosystem services and biodiversity conservation for 2050: an afforestation and a rewilding scenario, focused on climate-smart management strategies, and a farmland and an agroforestry recovery scenario, based on re-establishing human traditional activities. We quantified the influences of these scenarios on biodiversity through species habitat suitability changes for 2018-2050. We analyzed how these management strategies could influence indices of functional diversity (functional richness, functional evenness and functional dispersion) within the park. Habitat suitability changes revealed complementary patterns among scenarios. Afforestation and rewilding scenarios benefited more species adapted to habitats with low human influence, such as forests and open woodlands. The highest functional richness and dispersion was predicted for rewilding scenarios, which could improve landscape restoration and provide opportunities for the expansion and recolonization of forest areas by native species. The recovery of traditional farming and agroforestry activities results in the lowest values of functional richness, but these strategies contribute to complex landscape matrices with diversified habitats and resources. Moreover, this strategy could offer opportunities for fire suppression and increase landscape fire resistance. An integrative approach reconciling rewilding initiatives with the recovery of extensive agricultural and agroforestry activities is potentially an harmonious strategy for supporting the provision of ecosystem services while securing biodiversity conservation and functional diversity within the natural park.

Biodiversity dataset and atlas of the special area of conservation Montesinho/Nogueira, Portugal.

Background: The special area of conservation Montesinho/Nogueira (SAC-MN) is a key area for biodiversity conservation in the Iberian Peninsula. Covering an area of approximately 1081 km² in the northeast of Portugal mainland, the SAC-MN is home to a wide range of species, including several endemic and endangered species and priority habitats. Despite its ecological significance and importance for conservation, there is a lack of publicly available biodiversity data, which urges the need to create a comprehensive and up-to-date biodiversity dataset for the SAC-MN. New information: To bridge the knowledge gap on biodiversity in SAC-MN, we undertook a thorough data collection process, including species occurrence records and conservation status information at regional (Portugal) and European levels, from multiple sources. We collected and compiled this information for five major taxonomic groups (amphibians, birds, flora - vascular plants, mammals and reptiles) in SAC-MN, resulting in a total of 31,871 records with 1,312 documented species. In addition, we developed an easy-to-navigate web-based geographic information system (Web GIS). In this article, we present an in-depth report on the process of compiling and preparing data, as well as the development and design of our Web GIS to increase awareness and enhance understanding of the importance of preserving biodiversity in SAC-MN.

Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region.

Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (Tair) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time.

SDM-CropProj - A model-assisted framework to forecast crop environmental suitability and fruit production.

The effects of climate change (CC) will impact species ranges, and crops are no exception. Anticipating these changes through forecasting the environmentally suitable area of crops would help to reduce or mitigate the impact and adapt ecological and economic strategies. To forecast the CC effects on crops, we describe here a model-assisted framework (hereafter SDM-CropProj) that combines two modelling steps to be implemented in sequence: i) a multi-technique calibration process and ensemble-forecasting approach to predict the current and future environmental suitability of target crops; ii) a parsimonious univariate log-log linear model to relate the average total annual production to the current SDM-based suitable area. Different metrics for assessing the model's predictive performance showed that:•Crop production is related to model-predicted suitable area, thus allowing to obtain future projections of total fruit production based on climate scenarios.•The SDM-CropProj framework can assess potential pathways and trends in annual production due to changes in the environmental suitability and the distribution of multiple crop varieties/types as a consequence of CC, offering insights to other areas and crop types.

Cross-scale monitoring of habitat suitability changes using satellite time series and ecological niche models.

One of the biggest challenges to deal with the global crisis of biodiversity loss is the lack of efficient and viable monitoring systems across scales. Unlike traditional in situ biodiversity monitoring, a usually costly and time-consuming enterprise, satellite remote sensing (SRS) data offer a technically feasible and sustainable in time solution. Here, we devise a cost-effective and upgradeable spatiotemporal framework for monitoring the species-specific habitat availability changes across scales by trend analysis of habitat suitability index (HSI) derived from ecological niche models (ENMs; Maxent) and using time series of SRS data (MODIS). The SRS-ENM framework was applied for a large suite of native species (911), from major taxonomic groups (flora (vascular plants), amphibians, reptiles, birds and mammals), and listed in the IUCN Red List at regional (Iberian Peninsula) and continental (Europe) scales. The HSI-trend analyses predict cumulative reductions in habitat suitability for Threatened and Non-Threatened species across scales for the period 2002-2016. Specifically, 19% and 66% of the total grid cells for both species' groups showed negative trends at both regional and continental scales, respectively. Results were similar when considering all IUCN threat categories. All taxa groups showed a decrease in habitat suitability, but amphibians and reptiles groups hosted the largest number of negative HSI-trends grid cells. Considering all groups together, 12% and 34% of both study areas have strong reductions in habitat quality. We conclude that our framework detects increases and decreases in species' habitat suitability regardless of the spatial scale, extent, and pixel size. Species' range predictions across space and time based on SRS time series represent a promising Earth observation tool to support traditional risk assessment protocols and anticipate the decision-making process, while serving as a cross-scale biodiversity monitoring system.

Projected climate changes are expected to decrease the suitability and production of olive varieties in southern Spain.

World olive production is based on the cultivation of different varieties that respond differently to abiotic factors. Climate change may affect the area of land suitable for olive cultivation and change production levels, thus causing serious damage to this economically-relevant and highly-productive olive grove agroecosystem. In Mediterranean regions such as Andalusia, one of the main areas of olive production, the effect of climate change seems threatening. Thus, our main aims are: (1) to examine the abiotic factors that characterise the current cultivated locations and predict the current and potential distribution of these locations; (2) to evaluate the effect of climate change (based on regional scenarios) on the future environmental suitability of each olive variety; and (3) to analyse the expected alteration in the annual olive production. We used the seven most-productive olive varieties in Andalusia and the wild olive species to develop Species Distribution Models (SDMs), coupled with soil properties, geomorphology, water balance and (bio-)climatic predictors at a fine scale. We also derived future climate projections to assess the effect of climate change on the environmental suitability and productivity of each olive variety. We found that soil pH was the most-important factor for most distribution models, while (bio-)climatic predictors - such as continentality index, summer and autumn precipitation and winter temperature - provided important contributions. In general, projections based on regional climate change scenarios point to a decrease in the area suitable for olive crops in Andalusia, due to an increase in evapotranspiration and a decrease in precipitation. These changes in suitable area are also projected to decrease olive production for almost all the olive-growing provinces investigated. Our findings may anticipate the effects of climate change on olive crops and provide early estimates of fruit production, at local and regional scales, as well as forming the basis of adaptation strategies.

Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling.

Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFA-based models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFA-based models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes.