RESUMEN
An increasing amount of California's landscape has burned in wildfires in recent decades, in conjunction with increasing temperatures and vapor pressure deficit due to climate change. As the wildland-urban interface expands, more people are exposed to and harmed by these extensive wildfires, which are also eroding the resilience of terrestrial ecosystems. With future wildfire activity expected to increase, there is an urgent demand for solutions that sustain healthy ecosystems and wildfire-resilient human communities. Those who manage disaster response, landscapes, and biodiversity rely on mapped projections of how fire activity may respond to climate change and other human factors. California wildfire is complex, however, and climate-fire relationships vary across the state. Given known geographical variability in drivers of fire activity, we asked whether the geographical extent of fire models used to create these projections may alter the interpretation of predictions. We compared models of fire occurrence spanning the entire state of California to models developed for individual ecoregions and then projected end-of-century future fire patterns under climate change scenarios. We trained a Maximum Entropy model with fire records and hydroclimatological variables from recent decades (1981 to 2010) as well as topographic and human infrastructure predictors. Results showed substantial variation in predictors of fire probability and mapped future projections of fire depending upon geographical extents of model boundaries. Only the ecoregion models, accounting for the unique patterns of vegetation, climate, and human infrastructure, projected an increase in fire in most forested regions of the state, congruent with predictions from other studies.
Asunto(s)
Cambio Climático , Ecosistema , Predicción , Geografía , Incendios Forestales , California , Humanos , Incendios , Modelos TeóricosRESUMEN
The tree of life (TOL) is severely threatened by climate and land-cover changes. Preserving the TOL is urgent, but has not been included in the post-2020 global biodiversity framework. Protected areas (PAs) are fundamental for biological conservation. However, we know little about the effectiveness of existing PAs in preserving the TOL of plants and how to prioritize PA expansion for better TOL preservation under future climate and land-cover changes. Here, using high-resolution distribution maps of 8732 woody species in China and phylogeny-based Zonation, we find that current PAs perform poorly in preserving the TOL both at present and in 2070s. The geographical coverage of TOL branches by current PAs is approx. 9%, and less than 3% of the identified priority areas for preserving the TOL are currently protected. Interestingly, the geographical coverage of TOL branches by PAs will be improved from 9% to 52-79% by the identified priority areas for PA expansion. Human pressures in the identified priority areas are high, leading to high cost for future PA expansion. We thus suggest that besides nature reserves and national parks, other effective area-based conservation measures should be considered. Our study argues for the inclusion of preserving the TOL in the post-2020 conservation framework, and provides references for decision-makers to preserve the Earth's evolutionary history.
Asunto(s)
Geografía , Humanos , ChinaRESUMEN
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.