Frost in late spring causes severe ecosystem damage in temperate and boreal regions. We here analyze late-spring frost occurrences between 1959 and 2017 and woody species’ resistance strategies to forecast forest vulnerability under climate change. Leaf-out phenology and leaf-freezing resistance data come from up to 1,500 species cultivated in common gardens. The greatest increase in leaf-damaging spring frost has occurred in Europe and East Asia, where species are more vulnerable to spring frost than in North America. The data imply that 35 and 26% of Europe’s and Asia’s forests are increasingly threatened by frost damage, while this is only true for 10% of North America. Phenological strategies that helped trees tolerate past frost frequencies will thus be increasingly mismatched to future conditions.
Our results show that phylogenetically diverse assemblages with large phylogenetic age differences among species are associated with relatively high long‐term climate stability, with intra‐regional links between long‐term climate variability and phylogenetic composition especially strong in the more unstable regions. These findings point to future climate change as a key risk to the preservation of the phylogenetically diverse assemblages in regions characterized by relatively high paleoclimate stability, with China as a key example.
This paper [was highlighted in the *Editor's Picks* section of the Science Journal](https://www.dropbox.com/s/6k308eczv7i6kbj/2017_BMB_Journal_of_Biogeography_editors_choice.pdf?dl=1), and was among the [top downloaded articles](https://www.dropbox.com/s/sowq1h4bdngmipy/2017_BMB_Journal_of_Biogeography.png?dl=1) from the *Journal of Biogeography* during the 12 months after its publication.
We had three key findings. First, dry forest is the least protected biome in Mesoamerica (4.5% protected), indicating that further action to safeguard this biome is warranted. Secondly, the poor overlap between protected areas and high-value forest conservation areas found herein may provide evidence that the establishment of protected areas may not be fully accounting for tree priority rank map. Third, high percentages of forest cover and high-value forest conservation areas still need to be represented by the protected areas network. Because deforestation rates are still increasing in this region, Mesoamerica needs funding and coordinated action by policy makers, national and local governmental and non-governmental organizations, conservationists and other stakeholders.
*Maytenus senegalensis* subsp. *europaea* communities are unique vegetal formations in Europe. In fact, they are considered Priority Habitat by Directive 92/43/EEC. These are ecologically valuable plant communities found in the southeast of Spain. By combining modeling methods of environmental variables, historical photo-interpretation, and fieldwork, a chronosequence of the evolution of their extent of occurrence (EOO) has been reconstructed in 1957 and 2011. Results showed a strong regression range of *Maytenus senegalensis* subsp. *europaea* populations. More than 26,000 ha of EOO for this species have been lost in the province of Almería. Considering the final number of polygons, this area has been fragmented 18 times since the 1950s. These results reinforce the idea that the alteration and fragmentation of habitat due to human activities is one of the most important drivers of biodiversity loss and global change. These activities are mostly intensive greenhouse agriculture and urbanization without sustainable land planning. Knowledge about the distribution of M. senegalensis subsp. europaea is of great interest for future habitat restoration. Therefore, this would be the key species to recover these damaged ecosystems.
Herein we investigate the distribution and conservation problems of a relict interaction in the Sierra Nevada mountains (southern Europe) between the butterfly *Agriades zullichi* —a rare and threatened butterfly— and its larval foodplant *Androsace vitaliana* subsp. *nevadensis*. We designed an intensive field survey to obtain a comprehensive presence dataset. This was used to calibrate species distribution models with absences taken at local and regional extents, analyze the potential distribution, evaluate the influence of environmental factors in different geographical contexts, and evaluate conservation threats for both organisms.
The Mediterranean Basin is threatened by climate change, and there is an urgent need for studies to determine the risk of plant range shift and potential extinction. In this study, we simulate potential range shifts of 176 plant species to perform a detailed prognosis of critical range decline and extinction in a transformed mediterranean landscape. Particularly, we seek to answer two pivotal questions: (1) what are the general plant‐extinction patterns we should expect in mediterranean landscapes during the 21st century? and (2) does dispersal ability prevent extinction under climate change?.
A time series of 14-year distribution data of Zostera marina in the Ems estuary (The Netherlands) was used to build different data subsets: (1) total presence area; (2) a conservative estimate of the total presence area, defined as the area which had been occupied during at least 4 years; (3) core area, defined as the area which had been occupied during at least 2/3 of the total period; and (4–6) three random selections of monitoring years. On average, colonized and disappeared areas of the species in the Ems estuary showed remarkably similar transition probabilities of 12.7% and 12.9%, respectively. SDMs based upon machine-learning methods (Boosted Regression Trees and Random Forest) outperformed regression-based methods. Current velocity and wave exposure were the most important variables predicting the species presence for widely distributed data. Depth and sea floor slope were relevant to predict conservative presence area and core area.
We generated 380 S‐SDMs of 1224 tree species in Mesoamerica by combining 19 distribution modelling methods with 20 different thresholds using presence‐only data from the Global Biodiversity Information Facility. We compared the predicted richness and composition with inventory data obtained from the BIOTREE‐NET forest plot database. We designed two indicators of predictive performance that were based on the diversity factors used to measure species turnover: a (shared species between the observed and predicted compositions), b and c (the exclusive species of the predicted and observed compositions respectively) and compared them with the Sorensen and Beta‐Simpson turnover measures. Some modelling methods – especially machine learning and ensemble model forecasting methods performed significantly better than others in minimizing the error in predicted richness and composition. Our results also indicate that restrictive thresholds (with high omission errors) lead to more accurate S‐SDMs in terms of species richness and composition. Here, we demonstrate that particular combinations of modelling methods and thresholds provide results with higher predictive performance.