Aim: The anthropogenic climate change and land use change are considered two of the main factors that are altering biodiversity at the global scale. An evaluation that combined both factors can be relevant to detect which species could be the most vulnerable and reveal the regions of highest stability or susceptibility to biodiversity. We aimed to: (a) assess the effect of climate change and land use on the distribution of Cerrado plant species for different countries where they occur, (b) evaluate the effectiveness of the current network of protected areas (PAs) to safeguards species under different greenhouse–gas (GHG) emissions and land use scenarios, and (c) estimate the vulnerability of species based on protection effectiveness and habitat loss.Location: Bolivia, Brazil and Paraguay.Methods: We modelled the distribution of 1,553 plant species of Cerrado and evaluated species range loss caused by present and future land use and two GHG for 2050 and 2080. We assessed species vulnerability combining the representativeness of species within conservation units with the loss of species’ ranges outside PAs.Results: We found that climate change and land use will cause great damage to Cerrado flora by 2050 and 2080, even under optimistic conditions. The greatest impacts of land use will occur in the regions where the greatest richness will be harboured. The conservation of the species will be seriously affected since the PA network is not as effective in safeguarding them under current or future conditions.Main conclusions: The low level of protection together with the losses caused by the advance of agricultural lands will lead most species being highly vulnerable. Due to the distinct impacts of climate and land use over the three countries, conservation strategies should be implemented at transboundary and national levels.

Species distribution models (SDM) are widely used in diverse research areas because of their simple data requirements and application versatility. However, SDM outcomes are sensitive to data input and methodological choices. Such sensitivity and diverse applications mean that flexibility is necessary to create SDMs with tailored protocols for a given set of data and model use. We introduce the r package flexsdm for supporting flexible species distribution modelling workflows. flexsdm functions and their arguments serve as building blocks to construct a specific modelling protocol for user's needs. The main flexsdm features are modelling flexibility, integration with other modelling tools, simplicity of the objects returned and function speed. As an illustration, we used flexsdm to define a complete workflow for California red fir Abies magnifica. This package provides modelling flexibility by incorporating comprehensive tools structured in three steps: (a) The Pre‐modelling functions that prepare input, for example, sampling bias correction, sampling pseudo‐absences and background points, data partitioning, and reducing collinearity in predictors. (b) The Modelling functions allow fitting and evaluating different modelling approaches, including individual algorithms, tuned models, ensembles of small models and ensemble models. (c) The Post‐modelling functions include tools related to models' predictions, interpolation and overprediction correction. Because flexsdm comprises a large part of the SDM process, from outlier detection to overprediction correction, flexsdm users can delineate partial or complete workflows based on the combination functions to meet specific modelling needs.

Ecological niche modeling (ENM) is a broadly used tool in different fields of plant ecology. Despite the importance of edaphic conditions in determining the niche of terrestrial plant species, edaphic data have rarely been included in ENMs of plant species perhaps because such data are not available for many regions. Recently, edaphic data has been made available at a global scale allowing its potential inclusion and evaluation on ENM performance for plant species. Here, we take advantage of such data and address the following main questions: What is the influence of distinct predictor variables (e.g. climatic vs edaphic) on different ENM algorithms? and what is the relationship between the performance of different predictors and geographic characteristics of species? We used 125 plant species distributed over the Neotropical region to explore the effect on ENMs of using edaphic data available from the SoilGrids database and its combination with climatic data from the CHELSA database. In addition, we related these different predictor variables to geographic characteristics of the target species and different ENM algorithms. The use of different predictors (climatic, edaphic, and both) significantly affected model performance and spatial complexity of the predictions. We showed that the use of global edaphic plus climatic variables generates ENMs with similar or better accuracy compared to those constructed only with climate variables. Moreover, the performance of models considering these different predictors, separately or jointly, was related to geographic properties of species records, such as number and distribution range. The large geographic extent, the variability of environments and the different species’ geographical characteristics considered here allowed us to demonstrate that global edaphic data adds useful information for plant ENMs. This is particularly valuable for studies of species that are distributed in regions where more detailed information on soil properties is poor or does not even exist.