Assessing the Climatic Vulnerability of the Micrurus sangilensis (Niceforo Maria, 1942) under Future Scenarios

Assuming that co-distributed species are exposed to similar environmental conditions, ecological niche models (ENMs) of bird and plant species inhabiting tropical dry forests (TDFs) in Mexico were developed to evaluate future projections of their distribution for the years 2050 and 2070. We used ENM-based predictions and climatic data for two Global Climate Models, considering two Representative Concentration Pathway scenarios (RCP4.5/RCP8.5). We also evaluated the effects of habitat loss and the importance of the Mexican system of protected areas (PAs) on the projected models for a more detailed prediction of TDFs and to identify hot spots that require conservation actions. We identified four major distributional areas: the main one located along the Pacific Coast (from Sonora to Chiapas, including the Cape and Bajío regions, and the Balsas river basin), and three isolated areas: the Yucatán peninsula, central Veracruz, and southern Tamaulipas. When considering the effect of habitat loss, a significant reduction (~61%) of the TDFs predicted area occurred, whereas climate-change models suggested (in comparison with the present distribution model) an increase in area of 3.0-10.0% and 3.0-9.0% for 2050 and 2070, respectively. In future scenarios, TDFs will occupy areas above its current average elevational distribution that are outside of its present geographical range. Our findings show that TDFs may persist in Mexican territory until the middle of the XXI century; however, the challenges about long-term conservation are partially addressed (only 7% unaffected within the Mexican network of PAs) with the current Mexican PAs network. Based on our ENM approach, we suggest that a combination of models of species inhabiting present TDFs and taking into account change scenarios represent an invaluable tool to create new PAs and ecological corridors, as a response to the increasing levels of habitat destruction and the effects of climate change on this ecosystem.

Background:The geographic range of the tick Amblyomma americanum, a vector of diseases of public health significance such as ehrlichiosis, has expanded from the southeast of the United States northward during the 20th century. Recently, populations of this tick have been reported to be present close to the Canadian border in Michigan and New York states, but established populations are not known in Canada. Previous research suggests that changing temperature patterns with climate change may influence tick life cycles and permit northward range expansion of ticks in the northern hemisphere.Objectives:We aimed to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range and to investigate the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades.Methods:A simulation model of the tick A. americanum was used, via simulations using climate data from meteorological stations in the United States and Canada, to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range.Results:The predicted geographic scope of temperature suitability [ annual cumulative degree days (DD) ] included most of the central and eastern U.S. states east of longitude 110°W, which is consistent with current surveillance data for the presence of the tick in this region, as well as parts of southern Quebec and Ontario in Canada. Regional climate model output raises the possibility of northward range expansion into all provinces of Canada from Alberta to Newfoundland and Labrador during the coming decades, with the greatest northward range expansion (up to by the year 2100) occurring under the greenhouse gas (GHG) emissions of Representative Concentration Pathway (RCP) 8.5. Predicted northward range expansion was reduced by approximately half under the reduced GHG emissions of RCP4.5.Discussion:Our results raise the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades, and conclude that surveillance for this tick, and the diseases it transmits, would be prudent. https://doi.org/10.1289/EHP5668

Common species often play vital roles in ecosystem functions and processes. Globally, conservation strategies are mostly focused on threatened species and rarely explored the potential of using common species as indicators of critical ecosystems. The Himalayan mountains have unique riverine ecosystems harbouring high diversity of specialist river birds. Ecological niche modelling provides effective tools to predict suitable habitats of a species and identify habitats for conservation. We used two common water-dependent bird species, Blue Whistling Thrush and White-capped Water Redstart as indicators of riverine ecosystems within the Sikkim Himalayan region and predicted their suitable habitats using an ensemble modelling approach. We selected six predictor variables for the final model including three bioclimatic and three topographic variables. For both species, bioclimatic variables such as mean annual temperature and precipitation were the most important factors compared to topographic variables. At least 70 percent of the most suitable habitats are distributed below 2000 m elevation alongside major drainages. Also, most of their potential habitats are distributed outside the protected area networks in the region. This habitat suitability pattern may be applied to other sympatric species in the region. Since major water bodies in Sikkim are largely affected by developmental activities and climate change, these riverine birds might face threats of losing suitable habitats. We recommend a dynamic approach to evaluate the habitat quality of riverine birds, especially outside protected area networks in the region to plan conservation strategies. This approach will ensure habitat conservation of many water-dependent birds and other taxa associated with the riverine ecosystems of the Eastern Himalaya.

Common species often play vital roles in ecosystem functions and processes. Globally, conservation strategies are mostly focused on threatened species and rarely explored the potential of using common species as indicators of critical ecosystems. The Himalayan mountains have unique riverine ecosystems harbouring high diversity of specialist river birds. Ecological niche modelling provides effective tools to predict suitable habitats of a species and identify habitats for conservation. We used two common water-dependent bird species, Blue Whistling Thrush and White-capped Water Redstart as indicators of riverine ecosystems within the Sikkim Himalayan region and predicted their suitable habitats using an ensemble modelling approach. We selected six predictor variables for the final model including three bioclimatic and three topographic variables. For both species, bioclimatic variables such as mean annual temperature and precipitation were the most important factors compared to topographic variables. At least 70 percent of the most suitable habitats are distributed below 2000 m elevation alongside major drainages. Also, most of their potential habitats are distributed outside the protected area networks in the region. This habitat suitability pattern may be applied to other sympatric species in the region. Since major water bodies in Sikkim are largely affected by developmental activities and climate change, these riverine birds might face threats of losing suitable habitats. We recommend a dynamic approach to evaluate the habitat quality of riverine birds, especially outside protected area networks in the region to plan conservation strategies. This approach will ensure habitat conservation of many water-dependent birds and other taxa associated with the riverine ecosystems of the Eastern Himalaya.

Ecological niche modeling is a widely used tool to predict species distribution considering current, past, or future climate change scenarios across different geographic areas. Modeling scenarios allow researchers to assess the impacts of climate change on species distribution and identify priority areas for conservation. This study aimed to model the current and future potential distribution of Ceiba glaziovii under different climate change scenarios in Brazil. The MaxEnt algorithm was used to correlate species occurrence points with bioclimatic variables in current and future climate scenarios. Four General Circulation Models (GCMs) from CMIP6 were employed: BCC-CSM2-MR, CNRM-CM6-1, IPSL-CM6A-LR, and MIROC6, considering optimistic and pessimistic projections. The contribution of variables and model accuracy were assessed using the Jackknife statistical test and the Area Under the Curve (AUC) parameter. AUC values for current and future scenarios demonstrated high accuracy. The bioclimatic variables of precipitation and temperature were the main contributors to determining areas with higher habitat suitability. In the future climate scenario, there was a reduction in areas with good climatic suitability for all four GCMs, considering optimistic and pessimistic projections. Among the areas with high habitat suitability, the IPSL-CM6A-1 model in the optimistic projection showed the smallest reduction, while in the pessimistic scenario, all areas with high suitability disappeared. The species' climatic niche is expected to decrease under all tested climate change scenarios. The central areas of the Caatinga and its transition zones exhibit the highest climatic suitability in current and future scenarios and should be prioritized for the species' conservation.

Wind energy is essential in many decarbonization strategies and potentially vulnerable to climate change. While existing wind climate change assessments rely on regional or global climate models, a systematic investigation of the global-to-regional climate modeling chain is missing. In this presentation, I therefore address the differences in climate change impacts on winds according to  regional and global climate model ensembles under three different future scenarios. I highlight two key limitations, namely (a) the differing representation of land-use change in global and regional climate models which compromises comparability, and (b) the consistency of large-scale features along the global-to-regional climate modeling chain. To this end, I analyze the large EURO-CORDEX ensemble (rcp85: N=49; rcp45: N=18; rcp26: N=22) along with the driving global models (rcp85: N=7; rcp45: N=5; rcp26: N=7), finding evidence that climate change reduces mean wind speeds by up to -0.8 m/s (offshore) and -0.3 m/s (onshore). Moreover, I provide physical explanations for these changes by identifying two key drivers. First, onshore wind speeds drop in the driving global models in regions and scenarios with strong land use change but show no drop in EURO-CORDEX where land use is held constant. Second, offshore wind reductions follow decreases in the equator-to-pole temperature gradient remarkably well with correlations reaching around 0.9 in resource-rich European countries like Ireland, the United Kingdom and Norway, implying that arctic amplification is a severe risk for European offshore wind energy. My results suggest that earlier conclusions of negligible climate change impacts on wind energy might be premature if either land use changes strongly or polar amplification is at or above the range sampled in global climate models. In conjunction with earlier work that demonstrated the relevance of multidecadal wind fluctuations caused by climate variability, these results call for a better inclusion of climate risk in wind energy planning. Reference Wohland, J. Process-based climate change assessment for European winds using EURO-CORDEX and global models. Environ. Res. Lett. (2022) doi:10.1088/1748-9326/aca77f.

Ecological niche models are widely used for mapping the distribution of species during the last glacial maximum (LGM). Although the selection of the variables and General Circulation Models (GCMs) used for constructing those maps determine the model predictions, we still lack a discussion about which variables and which GCM should be included in the analysis and why. Here, we analyzed the climatic predictions for the LGM of 9 different GCMs in order to help biogeographers to select their GCMs and climatic layers for mapping the species ranges in the LGM. We 1) map the discrepancies between the climatic predictions of the nine GCMs available for the LGM, 2) analyze the similarities and differences between the GCMs and group them to help researchers choose the appropriate GCMs for calibrating and projecting their ecological niche models (ENM) during the LGM, and 3) quantify the agreement of the predictions for each bioclimatic variable to help researchers avoid the environmental variables with a poor consensus between models. Our results indicate that, in absolute values, GCMs have a strong disagreement in their temperature predictions for temperate areas, while the uncertainties for the precipitation variables are in the tropics. In spite of the discrepancies between model predictions, temperature variables (BIO1-BIO11) are highly correlated between models. Precipitation variables (BIO12- BIO19) show no correlation between models, and specifically, BIO14 (precipitation of the driest month) and BIO15 (Precipitation Seasonality (Coefficient of Variation)) show the highest level of discrepancy between GCMs. Following our results, we strongly recommend the use of different GCMs for constructing or projecting ENMs, particularly when predicting the distribution of species that inhabit the tropics and the temperate areas of the Northern and Southern Hemispheres, because climatic predictions for those areas vary greatly among GCMs. We also recommend the exclusion of BIO14 and BIO15 from ENMs because those variables show a high level of discrepancy between GCMs. Thus, by excluding them, we decrease the level of uncertainty of our predictions. All the climatic layers produced for this paper are freely available in http://ecoclimate.org/.

The identification and conservation of climate refugia for two Colombian endemic titi (Plecturocebus) monkeys

AimWe conduct the first assessment of likely future climate change impacts for biodiversity across the West African protected area (PA) network using climate projections that capture important climate regimes (e.g. West African Monsoon) and mesoscale processes that are often poorly simulated in general circulation models (GCMs).LocationWest Africa.MethodsWe use correlative species distribution models to relate species (amphibians, birds, mammals) distributions to modelled contemporary climates, and projected future distributions across the PA network. Climate data were simulated using a physically based regional climate model to dynamically downscale GCMs. GCMs were selected because they accurately reproduce important regional climate regimes and generate a range of regional climate change responses. We quantify uncertainty arising from projected climate change, modelling methodology and spatial dependency, and assess the spatial and temporal patterns of climate change impacts for biodiversity across the PA network.ResultsSubstantial species turnover across the network is projected for all three taxonomic groups by 2100 (amphibians = 42.5% (median); birds = 35.2%; mammals = 37.9%), although uncertainty is high, particularly for amphibians and mammals, and, importantly, increases across the century. However, consistent patterns of impacts across taxa emerge by early to mid‐century, suggesting high impacts across the Lower Guinea forest.Main conclusionsReducing (e.g. using appropriate climate projections) and quantifying uncertainty in climate change impact assessments helps clarify likely impacts. Consistent patterns of high biodiversity impacts emerge in the early and mid‐century projections, while end‐of‐century projections are too uncertain for reliable assessments. We recommend that climate change adaptation should focus on earlier projections, where we have most confidence in species responses, rather than on end‐of‐century projections that are frequently used. In addition, our work suggests climate impact should consider a broad range of species, as we simulate divergent responses across taxonomic groups.

Climate change (CC) and human footprint (HF) shape species spatial patterns and may affect the effectiveness of Protected Areas (PAs) network. Spatial patterns of threatened bird species of Subtropical-temperate hotspots in Southeastern South American grasslands are relevant biodiversity features to guide conservation policies. However, the PAs network covers less than 1% of grassland areas and does not overlap areas with the most suitable environmental conditions for threatened birds. Our aim was to find the most environmentally suitable areas for both current and future threatened birds (2050 and 2070) in Entre Ríos. We applied Systematic Conservation Planning protocols with Ecological Niche Models (ENMs) and ZONATION using distribution interaction function and HF as a cost. Then we overlapped binary maps to find priority areas among time periods. HF showed a more fragmented spatial configuration. The PAs network may include environmentally suitable conditions for threatened birds in CC scenarios and HF. We found areas that showed more connectivity in landscape prioritization over time and ensure high-quality environmental conditions for birds. We concluded that the effectiveness of the PAs network could be improved by overlapping priority areas. Our approach provides a knowledge base as a contribution to conservation-related decisions by considering HF and CC.

Despite the current rates of deforestation and the expected climatic changes, protecting species in their natural habitats is still the simplest, cheapest, and most effective way of safeguarding biodiversity. Here, the network of protected areas in the Brazilian Amazon was evaluated to assess its effectiveness in safeguarding species of Odonata. Ecological niche models were built to assess the suitability of the habitat for 503 Amazonian odonate species. Then, the effectiveness for the protection of odonate species of three classes of protected areas (strictly protected area, sustainable use area, and indigenous territory) was evaluated. Approximately 30% of the species are protected within the network of protected areas. These findings highlight the importance of protected areas for safeguarding most odonate species in the Amazon. For under‐represented or gap species, additional resources are still needed for effective management and protection on some private properties, which need to set aside land for conservation. In this way, it is possible to preserve habitats for odonate species and guarantee their conservation in the Amazon.

AimTo establish the robustness of two alternative methods for predicting the future ranges and abundances for two wild‐harvested abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814): single atmosphere–ocean general circulation model (GCM) or ensemble‐averaged GCM forecasts.LocationSouth Australia.MethodsWe assessed the ability of 20 GCMs to simulate observed seasonal sea surface temperature (SST) between 1980–1999, globally, and regionally for the Indian and Pacific Oceans south of the Equator. We used model rankings to characterize a set of representative climate futures, using three different‐sized GCM ensembles and two individual GCMs (the Parallel Climate Model and the Community Climate System Model, version 3.0). Ecological niche models were then coupled to physiological information to compare forecast changes in area of occupancy, population size and harvest area based on forecasts using the various GCM selection methods, as well as different greenhouse gas emission scenarios and climate sensitivities.ResultsWe show that: (1) the skill with which climate models reproduce recent SST records varies considerably amongst GCMs, with multimodel ensemble averages showing closer agreement to observations than single models; (2) choice of GCM, and the decision on whether or not to use ensemble‐averaged climate forecasts, can strongly influence spatiotemporal predictions of range, abundance and fishing potential; and (3) comparable hindcasting skill does not necessarily guarantee that GCM forecasts and ecological and evolutionary responses to these forecast changes, will be similar amongst closely ranked models.ConclusionBy averaging across an ensemble of seven highly ranked skilful GCMs, inherent uncertainties stemming from GCM differences are incorporated into forecasts of change in species range, abundance and sustainable fishing area. Our results highlight the need to make informed and explicit decisions on GCM choice, model sensitivity and emission scenarios when exploring conservation options for marine species and the sustainability of future harvests using ecological niche models.

BackgroundAedes aegypti and Ae. albopictus are the primary vectors that transmit several arboviral diseases, including dengue, chikungunya, and Zika. The world is presently experiencing a series of outbreaks of these diseases, so, we still require to better understand the current distributions and possible future shifts of their vectors for successful surveillance and control programs. Few studies assessed the influences of climate change on the spatial distributional patterns and abundance of these important vectors, particularly using the most recent climatic scenarios. Here, we updated the current potential distributions of both vectors and assessed their distributional changes under future climate conditions.MethodsWe used ecological niche modeling approach to estimate the potential distributions of Ae. aegypti and Ae. albopictus under present-day and future climate conditions. This approach fits ecological niche model from occurrence records of each species and environmental variables. For each species, future projections were based on climatic data from 9 general circulation models (GCMs) for each representative concentration pathway (RCP) in each time period, with a total of 72 combinations in four RCPs in 2050 and 2070. All ENMs were tested using the partial receiver operating characteristic (pROC) and a set of 2,048 and 2,003 additional independent records for Ae. aegypti and Ae. albopictus, respectively. Finally, we used background similarity test to assess the similarity between the ENMs of Ae. aegypti and Ae. albopictus.ResultsThe predicted potential distribution of Ae. aegypti and Ae. albopictus coincided with the current and historical known distributions of both species. Aedes aegypti showed a markedly broader distributional potential across tropical and subtropical regions than Ae. albopictus. Interestingly, Ae. albopictus was markedly broader in distributional potential across temperate Europe and the United States. All ecological niche models (ENMs) were statistically robust (P < 0.001). ENMs successfully anticipated 98% (1,999/2,048) and 99% (1,985/2,003) of additional independent records for both Ae. aegypti and Ae. albopictus, respectively (P < 0.001). ENMs based on future conditions showed similarity between the overall distributional patterns of future-day and present-day conditions; however, there was a northern range expansion in the continental USA to include parts of Southern Canada in case of Ae. albopictus in both 2050 and 2070. Future models also anticipated further expansion of Ae. albopictus to the East to include most of Europe in both time periods. Aedes aegypti was anticipated to expand to the South in East Australia in 2050 and 2070. The predictions showed differences in distributional potential of both species between diverse RCPs in 2050 and 2070. Finally, the background similarity test comparing the ENMs of Ae. aegypti and Ae. albopictus was unable to reject the null hypothesis of niche similarity between both species (P > 0.05).ConclusionThese updated maps provided details to better guide surveillance and control programs of Ae. aegypti and Ae. albopictus. They have also significant public health importance as a baseline for predicting the emergence of arboviral diseases transmitted by both vectors in new areas across the world.

Climate change will redistribute the global biodiversity in the Anthropocene. As climates change, species might move from one place to another, due to local extinctions and colonization of new environments. However, the existence of permeable migratory routes precedes faunal migrations in fragmented landscapes. Here, we investigate how dispersal will affect the outcome of climate change on the distribution of Amazon's primate species. We modeled the distribution of 80 Amazon primate species, using ecological niche models, and projected their potential distribution on scenarios of climate change. Then, we imposed landscape restrictions to primate dispersal, derived from a natural biogeographical barrier to primates (the main tributaries of the Amazon river) and an anthropogenic constraint to the migration of many canopy‐dependent animals (deforested areas). We also highlighted potential conflict zones, i.e. regions of high migration potential but predicted to be deforested. Species response to climate change varied across dispersal limitation scenarios. If species could occupy all newly suitable climate, almost 70% of species could expand ranges. Including dispersal barriers (natural and anthropogenic), however, led to range expansion in only less than 20% of the studied species. When species were not allowed to migrate, all of them lost an average of 90% of the suitable area, suggesting that climate may become unsuitable within their present distributions. All Amazon primate species may need to move as climate changes to avoid deleterious effects of exposure to non‐analog climates. The effect of climate change on the distribution of Amazon primates will ultimately depend on whether landscape permeability will allow climate‐driven faunal migrations. The network of protected areas in the Amazon could work as ‘stepping stones’ but most are outside important migratory routes. Therefore, protecting important dispersal corridors is foremost to allow effective migrations of the Amazon fauna in face of climate change and deforestation.

AimEcological niche modelling is one of the main tools that allows for the incorporation of climate change effects into conservation planning. For example, ecological niche model predictions can be used to rank species by degree of predicted future habitat loss. While many studies have considered how different modelling decisions contribute to uncertainty in niche model outputs, here we evaluate how metrics used to rank species by conservation risk respond to the choice of global climate models, greenhouse gas emission scenarios, suitable versus unsuitable threshold values, and the degree of model complexity.LocationCalifornia,USA.MethodsWe built ecological niche models for 153 species of reptiles and amphibians. Reduced complexity models were compared to default complexity models usingAICc to select climate variables and tune Maxent' s built‐in regularization parameter. We predicted the distribution of climatically suitable habitat under future (2041–2060) climate conditions according to 11 global climate models, four representative concentration pathways, and three threshold values. Two metrics to rank species by predicted future loss of climatically suitable habitat were calculated for each set of modelling decisions. To determine the effects of modelling decisions on rankings, we used mixed models.ResultsOur results indicate that while individual modelling decisions had relatively small effects on species ranks alone, in combination, they lead to very different conservation assessments.Main conclusionsWe recommend that a wide range of modelling decisions be explored and that variation in ranks across runs be reported as a first step in identifying the uncertainty in rank metrics used for assessing conservation risk under changing, but uncertain climate predictions.