Simulating climate change impacts on forests and associated vascular epiphytes in a subtropical island of East Asia

Aim This study aims to assess the impact of climate change on forests and vascular epiphytes, using species distribution models (SDMs). Location Island of Taiwan, subtropical East Asia. Methods A hierarchical modelling approach incorporating forest migration velocity and forest type–epiphyte interactions with classical SDMs was used to model the responses of eight forest types and 237 vascular epiphytes for the year 2100 under two climate change scenarios. Forest distributions were modelled and modified by dominant tree species’ dispersal limitations and hypothesized persistence under unfavourable climate conditions (20 years for broad‐leaved trees and 50 years for conifers). The modelled forest projections together with 16 environmental variables were used as predictors in models of epiphyte distributions. A null method was applied to validate the significance of epiphyte SDMs, and potential vulnerable species were identified by calculating range turnover rates. Results For the year 2100, the model predicted a reduction in the range of most forest types, especially for Picea and cypress forests, which shifted to altitudes c. 400 and 300 m higher, respectively. The models indicated that epiphyte distributions are highly correlated with forest types, and the majority (77–78%) of epiphyte species were also projected to lose 45–58% of their current range, shifting on average to altitudes c. 400 m higher than currently. Range turnover rates suggested that insensitive epiphytes were generally lowland or widespread species, whereas sensitive species were more geographically restricted, showing a higher correlation with temperature‐related factors in their distributions. Main conclusions The hierarchical modelling approach successfully produced interpretable results, suggesting the importance of considering biotic interactions and the inclusion of terrain‐related factors when developing SDMs for dependant species at a local scale. Long‐term monitoring of potentially vulnerable sites is advised, especially of those sites that fall outside current conservation reserves where additional human disturbance is likely to exacerbate the effect of climate change.     

Indicator plant species selection for monitoring the impact of climate change based on prediction uncertainty

To develop a long-term volunteer-based system for monitoring the impacts of climate change on plant distributions, potential indicator plants and monitoring sites were assessed considering habitat prediction uncertainty. We used species distribution models (SDMs) to project potential habitats for 19 popular edible wild plants in Japan. Prediction uncertainties of SDMs were assessed using three high-performance modeling algorithms and 19 simulated future climate data. SDMs were developed using presence/absence records, four climatic variables, and five non-climatic variables. The results showed that prediction uncertainties for future climate simulations were greater than those from the three different modeling algorithms. Among the 19 edible wild plant species, six had highly accurate SDMs and greater changes in occurrence probabilities between current and future climate conditions. The potential habitats of these six plants under future climate simulations tended to shift northward and upward, with predicted losses in potential southern habitats. These results suggest that these six plants are candidate indicators for long-term biological monitoring of the impacts of climate change. If temperature continuously increases as predicted, natural populations of these plants will decline in Kyushu, Chugoku and Shikoku districts, and in low altitudes of Chubu and Tohoku districts. These results also indicate the importance of occurrence probability and prediction uncertainty of SDMs for selecting target species and site locations for monitoring programs. Sasa kurilensis, a very popular and widespread dominant scrub bamboo in the cool-temperate regions of Japan, was found to be the most effective plant for monitoring.     

Using Publicly Available Forest Inventory Data in Climate-Based Models of Tree Species Distribution: Examining Effects of True Versus Altered Location Coordinates

Species distribution models (SDMs) were built with US Forest Inventory and Analysis (FIA) publicly available plot coordinates, which are altered for plot security purposes, and compared with SDMs built with true plot coordinates. Six species endemic to the western US, including four junipers (Juniperus deppeana var. deppeana, J. monosperma, J. occidentalis, J. osteosperma) and two piñons (Pinus edulis, P. monophylla), were analyzed. The presence–absence models based on current climatic variables were generated over a series of species-specific modeling extents using Random Forests and applied to forecast climatic conditions. The distributions of predictor variables sampled with public coordinates were compared to those sampled with true coordinates using t tests with a Bonferroni adjustment for multiple comparisons. Public- and true-based models were compared using metrics of classification accuracy. The modeled current and forecast distributions were compared in terms of their overall areal agreement and their geographic mean centroids. Comparison of the underlying distributions of predictor variables sampled with true versus public coordinates did not indicate a significant difference for any species at any extent. Both the public- and true-based models had comparable classification accuracies across extent for each species, with the exception of one species, J. occidentalis. True-based models produced geographic distributions with smaller areas under current and future scenarios. The greatest areal difference occurred in the species with the lowest modeled accuracies (J. occidentalis), and had a forecast distribution which diverged severely. The other species had forecast distributions with similar magnitudes of modeled distribution shifts.     

No‐analog climates and shifting realized niches during the late quaternary: implications for 21st‐century predictions by species distribution models

Empirically derived species distributions models (SDMs) are increasingly relied upon to forecast species vulnerabilities to future climate change. However, many of the assumptions of SDMs may be violated when they are used to project species distributions across significant climate change events. In particular, SDM's in theory assume stable fundamental niches, but in practice, they assume stable realized niches. The assumption of a fixed realized niche relative to climate variables remains unlikely for various reasons, particularly if novel future climates open up currently unavailable portions of species’ fundamental niches. To demonstrate this effect, we compare the climate distributions for fossil‐pollen data from 21 to 15 ka bp (relying on paleoclimate simulations) when communities and climates with no modern analog were common across North America to observed modern pollen assemblages. We test how well SDMs are able to project 20th century pollen‐based taxon distributions with models calibrated using data from 21 to 15 ka. We find that taxa which were abundant in areas with no‐analog late glacial climates, such as Fraxinus, Ostrya/Carpinus and Ulmus, substantially shifted their realized niches from the late glacial period to present. SDMs for these taxa had low predictive accuracy when projected to modern climates despite demonstrating high predictive accuracy for late glacial pollen distributions. For other taxa, e.g. Quercus, Picea, Pinus strobus, had relatively stable realized niches and models for these taxa tended to have higher predictive accuracy when projected to present. Our findings reinforce the point that a realized niche at any one time often represents only a subset of the climate conditions in which a taxon can persist. Projections from SDMs into future climate conditions that are based solely on contemporary realized distributions are potentially misleading for assessing the vulnerability of species to future climate change.     

Potential distributional shifts in North America of allelopathic invasive plant species under climate change models

Predictive studies play a crucial role in the study of biological invasions of terrestrial plants under possible climate change scenarios. Invasive species are recognized for their ability to modify soil microbial communities and influence ecosystem dynamics. Here, we focused on six species of allelopathic flowering plants—Ailanthus altissima, Casuarina equisetifolia, Centaurea stoebe ssp. micranthos, Dioscorea bulbifera, Lantana camara, and Schinus terebinthifolia—that are invasive in North America and examined their potential to spread further during projected climate change. We used Species Distribution Models (SDMs) to predict future suitable areas for these species in North America under several proposed future climate models. ENMEval and Maxent were used to develop SDMs, estimate current distributions, and predict future areas of suitable climate for each species. Areas with the greatest predicted suitable climate in the future include the northeastern and the coastal northwestern regions of North America. Range size estimations demonstrate the possibility of extreme range loss for these invasives in the southeastern United States, while new areas may become suitable in the northeastern United States and southeastern Canada. These findings show an overall northward shift of suitable climate during the next few decades, given projected changes in temperature and precipitation. Our results can be utilized to analyze potential shifts in the distribution of these invasive species and may aid in the development of conservation and management plans to target and control dissemination in areas at higher risk for potential future invasion by these allelopathic species.     

Niche dynamics of Memecylon in Sri Lanka: Distribution patterns,climate change effects,and conservation priorities

Recent climate projections have shown that the distribution of organisms in island biotas is highly affected by climate change. Here, we present the result of the analysis of niche dynamics of a plant group, Memecylon, in Sri Lanka, an island, using species occurrences and climate data. We aim to determine which climate variables explain current distribution, model how climate change impacts the availability of suitable habitat for Memecylon, and determine conservation priority areas for Sri Lankan Memecylon. We used georeferenced occurrence data of Sri Lankan Memecylon to develop ecological niche models and assess both current and future potential distributions under six climate change scenarios in 2041–2060 and 2061–2080. We also overlaid land cover and protected area maps and performed a gap analysis to understand the impacts of land‐cover changes on Memecylon distributions and propose new areas for conservation. Differences among suitable habitats of Memecylon were found to be related to patterns of endemism. Under varying future climate scenarios, endemic groups were predicted to experience habitat shifts, gains, or losses. The narrow endemic Memecylon restricted to the montane zone were predicted to be the most impacted by climate change. Projections also indicated that changes in species’ habitats can be expected as early as 2041–2060. Gap analysis showed that while narrow endemic categories are considerably protected as demonstrated by their overlap with protected areas, more conservation efforts in Sri Lankan forests containing wide endemic and nonendemic Memecylon are needed. This research helped clarify general patterns of responses of Sri Lankan Memecylon to global climate change. Data from this study are useful for designing measures aimed at filling the gaps in forest conservation on this island.     

The Predictive Performance and Stability of Six Species Distribution Models

Background

Predicting species’ potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.

Methodology

We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.

Results

The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points).

Conclusions

According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.     

Habitat availability and gene flow influence diverging local population trajectories under scenarios of climate change: a place‐based approach

Ecological niche theory holds that species distributions are shaped by a large and complex suite of interacting factors. Species distribution models (SDMs) are increasingly used to describe species’ niches and predict the effects of future environmental change, including climate change. Currently, SDMs often fail to capture the complexity of species’ niches, resulting in predictions that are generally limited to climate‐occupancy interactions. Here, we explore the potential impact of climate change on the American pika using a replicated place‐based approach that incorporates climate, gene flow, habitat configuration, and microhabitat complexity into SDMs. Using contemporary presence–absence data from occupancy surveys, genetic data to infer connectivity between habitat patches, and 21 environmental niche variables, we built separate SDMs for pika populations inhabiting eight US National Park Service units representing the habitat and climatic breadth of the species across the western United States. We then predicted occurrence probability under current (1981–2010) and three future time periods (out to 2100). Occurrence probabilities and the relative importance of predictor variables varied widely among study areas, revealing important local‐scale differences in the realized niche of the American pika. This variation resulted in diverse and – in some cases – highly divergent future potential occupancy patterns for pikas, ranging from complete extirpation in some study areas to stable occupancy patterns in others. Habitat composition and connectivity, which are rarely incorporated in SDM projections, were influential in predicting pika occupancy in all study areas and frequently outranked climate variables. Our findings illustrate the importance of a place‐based approach to species distribution modeling that includes fine‐scale factors when assessing current and future climate impacts on species’ distributions, especially when predictions are intended to manage and conserve species of concern within individual protected areas.     

Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long‐term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat‐induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long‐term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range – with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot‐spells, in driving species–climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species.     

Modelling current and future potential distributions of two desert jerboas under climate change in Iran

Species distribution models (SDMs) are excellent tools to understand the factors that affect the potential distribution of several organisms at different scale. In this study, we analyzed the current potential distribution of the Blanford's Jerboa Jaculus blanfordi and the Arabian Jerboa Jaculus loftusi (Mammalia: Rodentia) in Iran and predicted the impact of climate change on their future potential distributions using two different modelling software packages: Maxent and sdm. Our results showed that precipitation was the most important variable affecting the potential distributions of J. blanfordi and J. loftusi in Iran. We also showed that the potential distributions of the two jerboas species are unlikely to be affected by climate change. All our models showed high levels of predictive performances. Thus, SDMs are a promising tool to complement data from laboratory and field studies to illuminate the biology and ecology of jerboa and inform management decisions.     

South African mouse shrews (<Emphasis Type="Italic">Myosorex</Emphasis>) feel the heat: using species distribution models (SDMs) and IUCN Red List criteria to flag extinction risks due to climate change

Five species of mouse or forest shrews (Myosorex) are endemic to South Africa, Lesotho and Swaziland, four of which (Myosorex varius, Myosorex cafer, Myosorex longicaudatus and Myosorex cf. tenuis) are associated with montane or temperate grassland, fynbos and/or forest habitats while a fifth (Myosorex sclateri) is associated with lowland subtropical forests. Due to their small size, specialised habitat, low dispersal capacity, high metabolism and sensitivity to temperature extremes, we predicted that, particularly for montane species, future climate change should have a negative impact on area of occupancy (AOO) and ultimately extinction risks. Species distribution models (SDMs) indicated general declines in AOO of three species by 2050 under the A1b and A2 climate change scenarios (M. cafer, M. varius, M. longicaudatus) while two species (M. sclateri and M. cf. tenuis) remained unchanged (assuming no dispersal) or increased their AOO (assuming dispersal). While temperate species such as M. varius appear to be limited by temperature maxima (preferring cooler temperatures), the subtropical species M. sclateri appears to be limited by temperature minima (preferring warmer temperatures). Evidence for declines in AOO informed the uplisting (to a higher category of threat) of the Red List status of four Myosorex species to either vulnerable or endangered as part of a separate regional International Union for Conservation of Nature (IUCN) Red List assessment.     

Dendrochronological potential of four neotropical dry-forest tree species: Climate-growth correlations in northeast Brazil

Tropical dry forests (TDF) are highly important tropical forest ecosystems. Yet, these forests are highly threatened, usually neglected and only poorly studied. Understanding the long-term influences of environmental conditions on tree growth in these forests is crucial to understand the functioning, carbon dynamics and potential responses to future climate change of these forests. Dendrochronology can be used as a tool to provide these insights but has only scantly been applied in (dry) tropical forests. Here we evaluate the dendrochronological potential of four Caatinga neotropical dry forest tree species – Aspidosperma pyrifolium, Ziziphus joazeiro, Tabebuia aurea, and Libidibia ferrea – collected in two locations in northeastern Brazil (Sergipe state). We provide an anatomical characterization of the ring boundaries for the four species and investigate correlations of their growth with local and regional climatic variables. All four species form annual rings and show high inter-correlation (up to 0.806) and sensitivity (up to 0.565). Growth of all species correlated with local precipitation as well as with sea-surface temperatures in the tropical Atlantic and/or tropical Pacific oceans. We also show teleconnections between growth and the El Niño South Oscillation. The strong dependence of tree on precipitation is worrisome, considering that climate change scenarios forecast increased drought conditions in the Caatinga dry forest. Including more species and expanding dendrochronological studies to more areas would greatly improve our understanding of tree growth and functioning in TDFs. This type of knowledge is essential to assist the conservation, management and restoration of these critical tropical ecosystems.     

Unraveling climate influences on the distribution of the parapatric newts <Emphasis Type="Italic">Lissotriton vulgaris meridionalis</Emphasis> and <Emphasis Type="Italic">L. italicus</Emphasis>

Background

Climate is often considered as a key ecological factor limiting the capability of expansion of most species and the extent of suitable habitats. In this contribution, we implement Species Distribution Models (SDMs) to study two parapatric amphibians, Lissotriton vulgaris meridionalis and L. italicus, investigating if and how climate has influenced their present and past (Last Glacial Maximum and Holocene) distributions. A database of 901 GPS presence records was generated for the two newts. SDMs were built through Boosted Regression Trees and Maxent, using the Worldclim bioclimatic variables as predictors.

Results

Precipitation-linked variables and the temperature annual range strongly influence the current occurrence patterns of the two Lissotriton species analyzed. The two newts show opposite responses to the most contributing variables, such as BIO7 (temperature annual range), BIO12 (annual precipitation), BIO17 (precipitation of the driest quarter) and BIO19 (precipitation of the coldest quarter). The hypothesis of climate influencing the distributions of these species is also supported by the fact that the co-occurrences within the sympatric area fall in localities characterized by intermediate values of these predictors. Projections to the Last Glacial Maximum and Holocene scenarios provided a coherent representation of climate influences on the past distributions of the target species. Computation of pairwise variables interactions and the discriminant analysis allowed a deeper interpretation of SDMs’ outputs. Further, we propose a multivariate environmental dissimilarity index (MEDI), derived through a transformation of the multivariate environmental similarity surface (MESS), to deal with extrapolation-linked uncertainties in model projections to past climate. Finally, the niche equivalency and niche similarity tests confirmed the link between SDMs outputs and actual differences in the ecological niches of the two species.

Conclusions

The different responses of the two species to climatic factors have significantly contributed to shape their current distribution, through contractions, expansions and shifts over time, allowing to maintain two wide allopatric areas with an area of sympatry in Central Italy. Moreover, our SDMs hindcasting shows many concordances with previous phylogeographic studies carried out on the same species, thus corroborating the scenarios of potential distribution during the Last Glacial Maximum and the Holocene emerging from the models obtained.

     

Disentangling the effects of global climate and regional land-use change on the current and future distribution of mangroves in South Africa

The mangrove distribution in South Africa is fragmented and restricted to small forest patches occupying only 16 % of the estuaries within the current range. In this study we used species distribution models to test (1) whether the absence of mangrove forest and its species (Avicennia marina, Bruguiera gymnorrhiza and Rhizophora mucronata) within their current range is driven by climate or by climate combined with human or geomorphic perturbation and (2) how climate change may potentially affect the latitudinal limit of the mangrove forests and its species in South Africa. We used three modelling techniques (generalized linear models, generalized additive models and gradient boosting machines) and a set of three climate-based predictive variables (minimum air temperature of the coldest month, waterbalance and growing-degree days) combined separately with an index of human or geomorphic perturbation. Climate variables for the future projections were derived from two general circulation models driven by two socio-economic scenarios (A2a and B2a). Within the range of the mangrove forest, the fragmented distribution of the mangroves in South Africa was not explained by our set of climate variables alone. The index of human perturbations slightly improved the predictions but the index of geomorphic perturbation did not. Climate change will create climatically suitable sites for the mangrove forest and the two species A. marina and B. gymnorrhiza beyond their current limits, but model outcomes did not agree on the future potential distribution of R. mucronata. We were able to successfully predict range limits and to detect future climatically suitable sites beyond the current limits. Factors controlling mangrove distribution within its range are still to be identified although absences were partly explained by human perturbations.     

Forecasting the combined effects of future climate and land use change on the suitable habitat of Davidia involucrata Baill

Accurately predicting the future distribution of species is crucial for understanding how species will response to global environmental change and for evaluating the effectiveness of current protected areas (PAs). Here, we assessed the effect of climate and land use change on the projected suitable habitats of Davidia involucrata Baill under different future scenarios using the following two types of models: (a) only climate covariates (climate SDMs) and (b) climate and land use covariates (full SDMs). We found that full SDMs perform significantly better than climate SDMs in terms of both AUC (p < .001) and TSS (p < .001) and also projected more suitable habitat than climate SDMs both in the whole study area and in its current suitable range, although D. involucrate is predicted to loss at least 26.96% of its suitable area under all future scenarios. Similarly, we found that these range contractions projected by climate SDMs would negate the effectiveness of current PAs to a greater extent relative to full SDMs. These results suggest that although D. involucrate is extremely vulnerability to future climate change, conservation intervention to manage habitat may be an effective option to offset some of the negative effects of a changing climate on D. involucrate and can improve the effectiveness of current PAs. Overall, this study highlights the necessity of integrating climate and land use change to project the future distribution of species.     

Responses of coral reef fishes to past climate changes are related to life‐history traits

Coral reefs and their associated fauna are largely impacted by ongoing climate change. Unravelling species responses to past climatic variations might provide clues on the consequence of ongoing changes. Here, we tested the relationship between changes in sea surface temperature and sea levels during the Quaternary and present‐day distributions of coral reef fish species. We investigated whether species‐specific responses are associated with life‐history traits. We collected a database of coral reef fish distribution together with life‐history traits for the Indo‐Pacific Ocean. We ran species distribution models (SDMs) on 3,725 tropical reef fish species using contemporary environmental factors together with a variable describing isolation from stable coral reef areas during the Quaternary. We quantified the variance explained independently by isolation from stable areas in the SDMs and related it to a set of species traits including body size and mobility. The variance purely explained by isolation from stable coral reef areas on the distribution of extant coral reef fish species largely varied across species. We observed a triangular relationship between the contribution of isolation from stable areas in the SDMs and body size. Species, whose distribution is more associated with historical changes, occurred predominantly in the Indo‐Australian archipelago, where the mean size of fish assemblages is the lowest. Our results suggest that the legacy of habitat changes of the Quaternary is still detectable in the extant distribution of many fish species, especially those with small body size and the most sedentary. Because they were the least able to colonize distant habitats in the past, fish species with smaller body size might have the most pronounced lags in tracking ongoing climate change.     

Predicting range shifts of Davidia involucrata Ball. under future climate change

Understanding and predicting how species will respond to climate change is crucial for biodiversity conservation. Here, we assessed future climate change impacts on the distribution of a rare and endangered plant species, Davidia involucrate in China, using the most recent global circulation models developed in the sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC6). We assessed the potential range shifts in this species by using an ensemble of species distribution models (SDMs). The ensemble SDMs exhibited high predictive ability and suggested that the temperature annual range, annual mean temperature, and precipitation of the driest month are the most influential predictors in shaping distribution patterns of this species. The projections of the ensemble SDMs also suggested that D. involucrate is very vulnerable to future climate change, with at least one‐third of its suitable range expected to be lost in all future climate change scenarios and will shift to the northward of high‐latitude regions. Similarly, at least one‐fifth of the overlap area of the current nature reserve networks and projected suitable habitat is also expected to be lost. These findings suggest that it is of great importance to ensure that adaptive conservation management strategies are in place to mitigate the impacts of climate change on D. involucrate.     

Current and future potential distributions of three Dracaena Vand. ex L. species under two contrasting climate change scenarios in Africa

Forest undergrowth plants are tightly connected with the shady and humid conditions that occur under the canopy of tropical forests. However, projected climatic changes, such as decreasing precipitation and increasing temperature, negatively affect understory environments by promoting light‐demanding and drought‐tolerant species. Therefore, we aimed to quantify the influence of climate change on the spatial distribution of three selected forest undergrowth plants, Dracaena Vand. ex L. species, D. afromontana Mildbr., D. camerooniana Baker, and D. surculosa Lindl., simultaneously creating the most comprehensive location database for these species to date. A total of 1,223 herbarium records originating from tropical Africa and derived from 93 herbarium collections worldwide have been gathered, validated, and entered into a database. Species‐specific Maxent species distribution models (SDMs) based on 11 bioclimatic variables from the WorldClim database were developed for the species. HadGEM2‐ES projections of bioclimatic variables in two contrasting representative concentration pathways (RCPs), RCP2.6 and RCP8.5, were used to quantify the changes in future potential species distribution. D. afromontana is mostly sensitive to temperature in the wettest month, and its potential geographical range is predicted to decrease (up to ?63.7% at RCP8.5). Optimum conditions for D. camerooniana are low diurnal temperature range (6–8°C) and precipitation in the wettest season exceeding 750 mm. The extent of this species will also decrease, but not as drastically as that of D. afromontana. D. surculosa prefers high precipitation in the coldest months. Its potential habitat area is predicted to increase in the future and to expand toward the east. This study developed SDMs and estimated current and future (year 2050) potential distributions of the forest undergrowth Dracaena species. D. afromontana, naturally associated with mountainous plant communities, was the most sensitive to predicted climate warming. In contrast, D. surculosa was predicted to extend its geographical range, regardless of the climate change scenario.     

Predicting the impacts of climate change on the distribution of threatened forest‐restricted birds in Madagascar

The greatest common threat to birds in Madagascar has historically been from anthropogenic deforestation. During recent decades, global climate change is now also regarded as a significant threat to biodiversity. This study uses Maximum Entropy species distribution modeling to explore how potential climate change could affect the distribution of 17 threatened forest endemic bird species, using a range of climate variables from the Hadley Center's HadCM3 climate change model, for IPCC scenario B2a, for 2050. We explore the importance of forest cover as a modeling variable and we test the use of pseudo‐presences drawn from extent of occurrence distributions. Inclusion of the forest cover variable improves the models and models derived from real‐presence data with forest layer are better predictors than those from pseudo‐presence data. Using real‐presence data, we analyzed the impacts of climate change on the distribution of nine species. We could not predict the impact of climate change on eight species because of low numbers of occurrences. All nine species were predicted to experience reductions in their total range areas, and their maximum modeled probabilities of occurrence. In general, species range and altitudinal contractions follow the reductive trend of the Maximum presence probability. Only two species (Tyto soumagnei and Newtonia fanovanae) are expected to expand their altitude range. These results indicate that future availability of suitable habitat at different elevations is likely to be critical for species persistence through climate change. Five species (Eutriorchis astur, Neodrepanis hypoxantha, Mesitornis unicolor, Euryceros prevostii, and Oriola bernieri) are probably the most vulnerable to climate change. Four of them (E. astur, M. unicolor, E. prevostii, and O. bernieri) were found vulnerable to the forest fragmentation during previous research. Combination of these two threats in the future could negatively affect these species in a drastic way. Climate change is expected to act differently on each species and it is important to incorporate complex ecological variables into species distribution models.     

Contracting montane cloud forests: a case study of the Andean alder (Alnus acuminata) and associated fungi in the Yungas

Alnus acuminata is a keystone tree species in the Yungas forests and host to a wide range of fungal symbionts. While species distribution models (SDMs) are routinely used for plants and animals to study the effects of climate change on montane forest communities, employing SDMs in fungi has been hindered by the lack of data on their geographic distribution. The well‐known host specificity and common biogeographic history of A. acuminata and associated ectomycorrhizal (ECM) fungi provide an exceptional opportunity to model the potential habitat for this symbiotic assemblage and to predict possible climate‐driven changes in the future. We (1) modeled the present and future distributions of suitable habitats for A. acuminata; (2) characterized fungal communities in different altitudinal zones of the Yungas using DNA metabarcoding of soil and root samples; and (3) selected fungi that were significant indicators of Alnus. Fungal communities were strongly structured according to altitudinal forest types and the presence of Alnus. Fungal indicators of Alnus, particularly ECM and root endophytic fungi, were also detected in Alnus roots. Current and future (year 2050) habitat models developed for A. acuminata predict a 25–50 percent decrease in suitable area and an upslope shift of the suitable habitat by ca. 184–380 m, depending on the climate change scenario. Although A. acuminata is considered to be an effective disperser, recent studies suggest that Andean grasslands are remarkably resistant to forest invasion, and future range contraction for A. acuminata may be even more pronounced than predicted by our models.