Land abandonment in European mountains threatens habitats shaped for centuries by low-intensity agriculture and grazing. Hence, it is important to identify spatiotemporal patterns in rural abandonment, and relate them to biophysical and socioeconomic drivers. We pursued these goals in the theoretical context of transitions from traditional to productivist and then to post-productivist agriculture. We conducted a case study in a representative of southern Europe sub-mountainous marginal area that was once traditionally exploited (Pindus range, Epirus, Greece). Land cover was mapped from the outset of abandonment (years 1945, 1970, 1996 and 2015), and we subsequently calculated landscape metrics. An Intensity Analysis facilitated the comparison of rates of land cover change between time periods. By employing random forest modelling, we related socioeconomic, physiographic, geological and climatic predictors to land type occurrence and succession intensity. We found that farmland decreased from 30% to 3% during the 70 years of the study period, and that forest increased from 22% to 63%. The landscape’s heterogeneity, ecotone diversity, and spatial aggregation decreased. Abandonment and succession accelerated and relocated to lower elevation, especially during the latest time period, which was related to a second depopulation wave and livestock decrease. The remaining lowland farmlands were of productivist agriculture, and no widespread post-productivist regime was found. Thus, our study supports the view that policies, which have been mainly based on the linear transition of agricultural regimes in northern Europe, must take into account southern European mountains, where widespread abandonment can coexist with limited intensification and extensification.

Land use and cover (LUC) of southern European mountains is dramatically changing, mainly due to observed socioeconomic demands and climatic changes. It is therefore important to understand LUC changes to accurately predict future landscapes and their threats. Simulation models of LUC change are ideal for this task because they allow the in silico experimentation under different socioeconomic and climatic scenarios. In the present study, we employed the trans-CLUE-S model, to predict for 2055 the LUC of a typical southern European sub-mountainous area, which has experienced widespread abandonment until recently. Four demand scenarios were tested, and under each demand scenario, we compared three climatic scenarios, ranging from less to more warm and dry conditions. We found that farmland declined from 3.2% of the landscape in 2015 to 0.4% in 2055 under the business-as-usual demand scenario, whereas forest further increased from 62.6% to 79%. For any demand scenario, differences in LUC between maps predicted under different climatic scenarios constituted less than 10% of the landscape. In the less than 10% that differed, mainly farmland and forest shifted to higher elevation under a warmer and drier climate, whereas grassland and scrubland to lower. Such insights by modelling analyses like the present study’s can improve the planning and implementation of management and restoration policies which will attempt to conserve ecosystem services and mitigate the negative effects of socioeconomic and climatic changes in the mountainous regions of southern Europe.

In recent decades, climate change has significantly influenced the frequency and intensity of wildfires across Mediterranean pine forests. The loss of forest cover can bring long-term ecological changes that impact the overall biodiversity and alter species composition. Understanding the long-term impact of wildfires requires effective and cost-efficient methods for monitoring the postfire ecosystem dynamics. Passive acoustic monitoring (PAM) has been increasingly used to monitor the biodiversity of vocal species at large spatial and temporal scales. Using acoustic indices, where the biodiversity of an area is inferred from the overall structure of the soundscape, rather than the more labor-intensive identification of individual species, has yielded mixed results, emphasizing the importance of testing their efficacy at the regional level. In this study, we examined whether widely used acoustic indicators were effective at capturing changes in the avifauna diversity in Pinus halepensis forest stands with different fire burning histories (burnt in 2001, 2009, and 2018 and unburnt for >20 years) on the Sithonia Peninsula, Greece. We recorded the soundscape of each stand using two–three sensors across 11 days of each season from March 2022 to January 2023. We calculated for each site and season the following five acoustic indices: the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Normalized Difference Soundscape Index (NDSI), and Bioacoustic Index (BI). Each acoustic index was then assessed in terms of its efficacy at predicting the local avifauna diversity, as estimated via two proxies—the species richness (SR) and the Shannon Diversity Index (SDI) of vocal bird calls. Both the SR and SDI were calculated by having an expert review the species identification of calls detected within the same acoustic dataset by the BirdNET convolutional neural network algorithm. A total of 53 bird species were identified. Our analysis shows that the BI and NDSI have the highest potential for monitoring the postfire biodiversity dynamics in Mediterranean pine forests. We propose the development of regional-scale acoustic observatories at pine and other fire-prone Mediterranean habitats, which will further improve our understanding of how to make the best use of acoustic indices as a tool for rapid biodiversity assessments.

We investigated the combined effects of climate and land-use change on plant diversity in northwestern Greece, a region representative of broader European trends in land abandonment. We based our study on comprehensive field data on plants’ distribution and modelling of land-use changes based on socio-economic trends. We build distribution models for 358 taxa based on current (2015) and future (2055) conditions according to the combinations of three climate and three land-use change scenarios. We compared species distribution changes between current and future conditions for each scenario, and we investigated species trends concerning their ecological indicator values and strategies. Additionally, by analyzing the distribution changes in aggregated differential taxa representing the various plant communities in the study area, we identified patterns of distribution shifts at the community level. Our results indicated more pronounced differences between land-use scenarios than between climate ones, which was attributed to the local scale of the study area, its climatic and physiographic characteristics, and its complex land-use legacy. Both climate and land-use changes drastically reduced the distribution of some species, with species distribution loss exceeding 80% under certain combinations of socioeconomic and climate change scenarios. Species ecological indicator values and strategies showed a buffering effect of forest microclimate against climate change, which, however, may favor only species of forest communities. At the community level, land-use change had again a stronger impact than climate change, with consistent patterns within major vegetation types (forests and open habitats) but contrasting trends between them. Our results highlight the need for appropriate conservation plans to counteract the negative impacts of land abandonment and to take advantage of its positive impacts.

The CLUE-S model is a popular choice for modelling land use and land cover change from local to regional scales, but it spatially allocates the demand for only the total cover of each land class in the predicted map. In the present work, we introduce a CLUE-S variant that allocates demand at the more detailed level of land type transitions, the trans-CLUE-S model. We implemented this extension algorithmically in R, without the need of new parameters. By processing each row of the land transition matrix separately, the model allocates the demand of each land category’s transitions via the CLUE-S allocation routine for only the cells which were of that category in the map of the previous time step. We found that the trans-CLUE-S model had half the total and configuration disagreement of the CLUE-S predictions in an empirical landscape, and in simulated landscapes of different characteristics. Moreover, the trans-CLUE-S performance was less sensitive to the number of environmental predictors of land type suitability for allocating demand. Although trans-CLUE-S is computationally more demanding due to running a CLUE-S allocation for each land class, we appended the solution of a land-use assignment optimisation problem that facilitates the convergence and acceleration of allocation. We additionally provide R functions for: CLUE-S variants at other levels of demand resolution; random instead of environment-based allocation; and for simulating landscapes of desired characteristics. Our R code for the models and functions can contribute to more reproducible, transparent and accurate modelling, analysis and interpretation of land cover change. HIGHLIGHTS The trans-CLUE-S model employs demand at the finer level of land type transitions The trans-CLUE-S predictions were twice more accurate than the CLUE-S model’s The trans-CLUE-S accuracy was less dependent on the amount of environmental data Algorithmic addition of a land assignment task enabled and sped up full convergence R code is provided for our models and auxiliary functions

There has been little research on the distribution and ecology of the four dormouse species occurring in Greece; the Edible Dormouse (Glis glis), Forest Dormouse (Dryomys nitedula), Hazel dormouse (Muscardinus avellanarius) and Mouse-tailed Dormouse (Myomimus roachi). As a result, the latter three species are listed as data deficient (DD) in the National Red Data Book. Recently, the government has tried to address this knowledge gap, funding dormouse surveys within the Natura 2000 network. In this context, we used a combination of nest-tubes (n=442) and track-tunnels (n=238) to study dormouse distribution and habitat use across 37 sites representing different habitat types (with varying levels of grazing) of two mountainous N2K sites (GR1270001, GR1270005) in central Macedonia. We detected G. glis at 28 sites, D. nitedula at 32 sites, and M. avellanarius at seven sites. Positive identification of the different species was twice as likely in track-tunnels (unbaited; metal sheets covered in soot) than nest-tubes. We estimated relative abundance across sites using Royle-Nichols occupancy models, except for M. avellanarius due to data limitations. For all species, we examined habitat use using MaxEnt ecological-niche models. Our findings show that D. nitedula has the widest distribution, occurring even in sparse forests and maqui with moderate or high livestock grazing intensity. G. glis is common, but restricted to medium-high elevation forests. M. avellanarius appears to have a discontinuous distribution. If this study is representative of its status across the country, that species requires conservation efforts.