Species distribution modelling to support forest management. A literature review

Multi-model assessment of potential natural vegetation to support ecological restoration

Ecological restoration is imperative for controlling desertification. Potential natural vegetation (PNV), the theoretical vegetation succession state, can guides near-natural restoration. Although a rising transition from traditional statistical methods to advanced machine learning and deep learning is observed in PNV simulation, a comprehensive comparison of their performance is still unexplored. Therefore, we overview the performance of PNV mapping in terms of 12 commonly used methods with varying spatial scales and sample sizes. Our findings indicate that the methodology should be carefully selected due to the variation in performance of different model types, with Area Under the Curve (AUC) values ranging from 0.65 to 0.95 for models with sample sizes up to 80% of the total sample size. Specifically, semi-supervised learning performs best with small sample sizes (i.e., 10 to 200), while Random Forest, XGBoost, and artificial neural networks perform better with large sample sizes (i.e., over 500). Further, the performance of all models tends to improve significantly as the sample size increases and the grain size of the crystals becomes smaller. Take the downstream Tarim River Basin, a hyper-arid region undergoing ecological restoration, as a case study. We showed that its potential restored areas were overestimated by 2-3 fold as the spatial scale became coarser, revealing the caution needed while planning restoration projects at coarse resolution. These findings enhance the application of PNV in the design of restoration programs to prevent desertification.

Host tree availability shapes potential distribution of a target epiphytic moss species more than direct climate effects

Climate change significantly impacts the distribution of woody plants, indirectly influencing the dynamics of entire ecosystems. Understanding species' varied responses to the environment and their reliance on biotic interactions is crucial for predicting the global changes' impact on woodland biodiversity. Our study focusses on Dicranum viride, a moss of conservation priority, and its dependence on specific phorophytes (host trees). Using species distribution modelling (SDM) techniques, we initially modelled its distribution using climate-only variables. As a novel approach, we also modelled the distribution of the main phorophyte species and incorporated them into D. viride SDM alongside climate data. Finally, we analysed the overlap of climatic and geographic niches between the epiphyte and the phorophytes. Inclusion of biotic interactions significantly improved model performance, with phorophyte availability emerging as the primary predictor. This underscores the significance of epiphyte-phorophyte interactions, supported by substantial niche overlap. Predictions indicate a potential decline in the suitability of most of the current areas for D. viride, with noticeable shifts towards the northern regions of Europe. Our study underscores the importance of incorporating biotic interactions into SDMs, especially for dependent organisms. Understanding such connections is essential to implement successful conservation strategies and adapt forest management practices to environmental changes.

Potential migration pathways of broadleaved trees across the receding boreal biome under future climate change

Climate change has triggered poleward expansions in the distributions of various taxonomic groups, including tree species. Given the ecological significance of trees as keystone species in forests and their socio-economic importance, projecting the potential future distributions of tree species is crucial for devising effective adaptation strategies for both biomass production and biodiversity conservation in future forest ecosystems. Here, we fitted physiographically informed habitat suitability models (HSMs) at 50-m resolution across Sweden (55-68° N) to estimate the potential northward expansion of seven broadleaved tree species within their leading-edge distributions in Europe under different future climate change scenarios and for different time periods. Overall, we observed that minimum temperature was the most crucial variable for comprehending the spatial distribution of broadleaved tree species at their cold limits. Our HSMs projected a complex range expansion pattern for 2100, with individualistic differences among species. However, a frequent and rather surprising pattern was a northward expansion along the east coast followed by narrow migration pathways along larger valleys towards edaphically suitable areas in the north-west, where most of the studied species were predicted to expand. The high-resolution maps generated in this study offer valuable insights for our understanding of range shift dynamics at the leading edge of southern tree species as they expand into the receding boreal biome. These maps suggest areas where broadleaved tree species could already be translocated to anticipate forest and biodiversity conservation adaptation efforts in the face of future climate change.

Modeling habitat suitability of Dorema ammoniacum D Don. in the rangelands of central Iran

The purpose of this study was to evaluate the predictive accuracy of habitat suitability models, identifying the potential distribution range of Dorema ammoniacum, and its habitat requirements in the rangelands of Yazd province, central Iran. Bafgh, Mehriz and Nadoushan, were three habitats that were identified, and sampling was conducted in each habitat using a random-systematic method. A set of 10 plots were established (at equal distances) along 350 m long 18 transects. Soil samples (two depths: 0-30 and 30-60 cm from 36 profiles) were collected and measured in the laboratory. Elevation, slope, and aspect maps were derived, and climate information was collected from nearby meteorological stations. The habitat prediction of the species was modeled using Logistic Regression (LR), Maximum Entropy (MaxEnt), and Artificial Neural Network (ANN). The Kappa coefficient and the area under the curve (AUC) were calculated to assess the accuracy of the forecasted maps. The LR model for habitat prediction of the studied species in Mehriz (K = 0.67) and Nadoushan (K = 0.56) habitats were identified as good. The MaxEnt model predicted the habitat distribution for the selected species in Bafgh and Mehriz habitats as excellent (K = 0.89, AUC = 0.76, K = 0.89, AUC = 0.98), and in the Nadoushan habitat as very good (K = 0.78, AUC = 0.85). However, the ANN model predicted Bafgh and Nadoushan habitats as excellent and Mehriz habitat as very good (K = 0.87, K = 0.90, and K = 0.63, respectively). In general, in order to protect species D. ammoniacum, the development of its habitats in other areas of Yazd province and the habitats under study in conservation programs should be given priority.

A climate-induced tree species bottleneck for forest management in Europe

Large pulses of tree mortality have ushered in a major reorganization of Europe's forest ecosystems. To initiate a robust next generation of trees, the species that are planted today need to be climatically suitable throughout the entire twenty-first century. Here we developed species distribution models for 69 European tree species based on occurrence data from 238,080 plot locations to investigate the option space for current forest management in Europe. We show that the average pool of tree species continuously suitable throughout the century is smaller than that under current and end-of-century climate conditions, creating a tree species bottleneck for current management. If the need for continuous climate suitability throughout the lifespan of a tree planted today is considered, climate change shrinks the tree species pool available to management by between 33% and 49% of its current values (40% and 54% of potential end-of-century values), under moderate (Representative Concentration Pathway 2.6) and severe (Representative Concentration Pathway 8.5) climate change, respectively. This bottleneck could have strong negative impacts on timber production, carbon storage and biodiversity conservation, as only 3.18, 3.53 and 2.56 species of high potential for providing these functions remain suitable throughout the century on average per square kilometre in Europe. Our results indicate that the option space for silviculture is narrowing substantially because of climate change and that an important adaptation strategy in forestry-creating mixed forests-might be curtailed by widespread losses of climatically suitable tree species.

Regional uniqueness of tree species composition and response to forest loss and climate change

The conservation and restoration of forest ecosystems require detailed knowledge of the native plant compositions. Here, we map global forest tree composition and assess the impacts of historical forest cover loss and climate change on trees. The global occupancy of 10,590 tree species reveals complex taxonomic and phylogenetic gradients determining a local signature of tree lineage assembly. Species occupancy analyses indicate that historical forest loss has significantly restricted the potential suitable range of tree species in all forest biomes. Nevertheless, tropical moist and boreal forest biomes display the lowest level of range restriction and harbor extremely large ranged tree species, albeit with a stark contrast in richness and composition. Climate change simulations indicate that forest biomes are projected to differ in their response to climate change, with the highest predicted species loss in tropical dry and Mediterranean ecoregions. Our findings highlight the need for preserving the remaining large forest biomes while regenerating degraded forests in a way that provides resilience against climate change.

Unravelling the impact of climate change on honey bees: An ensemble modelling approach to predict shifts in habitat suitability in Queensland, Australia

Honey bees play a vital role in providing essential ecosystem services and contributing to global agriculture. However, the potential effect of climate change on honey bee distribution is still not well understood. This study aims to identify the most influential bioclimatic and environmental variables, assess their impact on honey bee distribution, and predict future distribution. An ensemble modelling approach using the biomod2 package in R was employed to develop three models: a climate-only model, an environment-only model, and a combined climate and environment model. By utilising bioclimatic data (radiation of the wettest and driest quarters and temperature seasonality) from 1990 to 2009, combined with observed honey bee presence and pseudo absence data, this model predicted suitable locations for honey bee apiaries for two future time spans: 2020-2039 and 2060-2079. The climate-only model exhibited a true skill statistic (TSS) value of 0.85, underscoring the pivotal role of radiation and temperature seasonality in shaping honey bee distribution. The environment-only model, incorporating proximity to floral resources, foliage projective cover, and elevation, demonstrated strong predictive performance, with a TSS of 0.88, emphasising the significance of environmental variables in determining habitat suitability for honey bees. The combined model had a higher TSS of 0.96, indicating that the combination of climate and environmental variables enhances the model's performance. By the 2020-2039 period, approximately 88% of highly suitable habitats for honey bees are projected to transition from their current state to become moderate (14.84%) to marginally suitable (13.46%) areas. Predictions for the 2060-2079 period reveal a concerning trend: 100% of highly suitable land transitions into moderately (0.54%), marginally (17.56%), or not suitable areas (81.9%) for honey bees. These results emphasise the critical need for targeted conservation efforts and the implementation of policies aimed at safeguarding honey bees and the vital apiary industry.

Assessing the Distribution and Richness of Mammalian Species Using a Stacking Species Distribution Model in a Temperate Forest

This study was conducted as an effort to examine the association between mammalian species richness and environmental, anthropogenic, and bioclimate factors in the Province of Chungnam, Korea, using a stacked species distribution model (SSDM) approach. An SSDM model was constructed using an extensive dataset collected from 1357 mammal sampling points and their corresponding forest, geographical, anthropogenic, and bioclimatic information. Distance to forest edge, elevation, slope, population density, and distance to water channels were identified as important variables for determining species richness, whereas the impact of bioclimate variables was less important. The endemism map showed a strong correlation with species richness, suggesting the important role of endemic species. Overestimation was observed in areas with lower species richness. However, the findings of the study still demonstrated that valuable insights can be obtained through the use of the SSDM, which may be helpful to land managers, aiding in the effective management of wildlife habitats, particularly in regions with an abundance of species richness and endemism.

Climatic variables are more effective on the spatial distribution of oak forests than land use change across their historical range

The current research is conducted to model the effect of climate change and land use change (LUC) on the geographical distribution of Quercus brantii Lindl. (QB) forests across their historical range. Forecasting was done based on six general circulation models under RCP 2.6 and RCP 8.5 future climate change scenarios for the future years 2050 and 2070. In order to model the species distribution, different modeling methods were used. The results indicated that, in general, climatic variables had a higher influence on the distribution of QB than land use-related attributes. The mean diurnal range (bio2), the precipitation seasonality (bio15), and the mean temperature of the driest quarter (bio9) were the main predictors in the distribution of QB forests, while land use variables were less important in oak species distribution. The GBM, MaxEnt, and RF had higher accuracy and performance in modeling species distribution. The outputs also showed that in the current climate circumstances, 97,608.81 km2 of the studied area has high desirability for the presence of QB, and by 2070, under the pessimistic scenario, 96.29% of these habitats will be lost under the concomitant effect of LUC and climate change. By using the results of this research, it is possible to predict and identify the effective factors in changing the habitat of this oak species with more certainty. Based on the insights obtained from the results of such studies, the protection and restoration planning of the habitat of this key species, which supports diverse species, will be provided more efficiently.

Selection of climate variables in ant species distribution models: case study in South Korea

The selection of explanatory variables is important in modeling prediction of changes in species distribution in response to climate change. In this study, we evaluated the importance of variable selection in species distribution models. We compared two different types of models for predicting the distribution of ant species: temperature-only and both temperature and precipitation. Ants were collected at 343 forest sites across South Korea from 2006 through 2009. We used a generalized additive model (GAM) to predict the future distribution of 16 species that showed significant responses to changes in climatic factors (temperature and/or precipitation). Four types of GAMs were constructed: temperature, temperature with interaction of precipitation, temperature and precipitation without interaction, and temperature and precipitation with interaction. Most species displayed similar results between the temperatureonly and the temperature and precipitation models. The results for predicted changes in species richness were different from the temperature-only model. This indicates higher uncertainty in the prediction of species richness, which is obtained by combining the prediction results of distribution change for each species, than in the prediction of distribution change. The turnover rate of the ant assemblages was predicted to increase with decreases in temperature and increases in elevation, which was consistent with other studies. Finally, our results showed that the prediction of the distribution or diversity of organisms responding to climate change is uncertain because of the high variability of the model outputs induced by the variables used in the models.

Assessment of the impact of climate change on endangered conifer tree species by considering climate and soil dual suitability and interspecific competition

Climate change results in the habitat loss of many conifer tree species and jeopardizes species biodiversity and forest ecological functions. Delineating suitable habitats for tree species via climate niche model (CNM) is widely used to predict the impact of climate change and develop conservation and management strategies. However, the robustness of CNM is broadly debated as it usually does not consider soil and competition factors. Here we developed a new approach to combine soil variables with CNM and evaluate interspecific competition potential in the niche overlapping areas. We used an endangered conifer species - Chamaecyparis formosensis (red cypress) - as a case study to predict the impact of climate change. We developed a novel approach to integrate the climate niche model and soil niche model predictions and considered interspecific competition to predict the impacts of climate change on tree species. Our results show that the suitable habitat for red cypress would decrease significantly in the future with an additional threat from the competition of an oak tree species. Our approach and results may represent significant implications in making conservation strategies and evaluating the impacts of climate change, and providing the direction of the refinement of the ecological niche model.

Population-Based Evidence of Climate Change Adaptation in an Endangered Plant Endemic to a Biodiversity Hotspot

Climate change is expected to impact both the population structure and geographic distribution of plants. Species distribution models are widely used to assess range shifts and the vulnerability of plants to climate change. Despite the abundance of modeling studies, little is known about how existing populations respond to climate change. We investigated the demographic structure and vulnerability to climate change in Anemone moorei, a sub-shrub with a highly restricted distribution in a biodiversity hotspot. We improved the distribution knowledge through intensive field work. We conducted a census of stem length as a proxy for age for all known populations. We used ensemble forecasting to project distributions considering 10 future climate scenarios and developed a novel climate change vulnerability index for the species' distribution. We found that the mean stem length decreases and the proportion of young plants increases, while the size of fruiting plants decreases as A. moorei faces greater climate change vulnerability. We interpret these results as evidence for the onset of recent adaptation to climate change, consisting of reduced adult longevity and an earlier onset of reproduction. As a result of these changes, the proportion of juveniles in the population increases.

Projected Shifts in Bird Distribution in India under Climate Change

Global climate change is causing unprecedented impacts on biodiversity. In India, there is little information available regarding how climate change affects biodiversity at the taxon/group level, and large-scale ecological analyses have been lacking. In this study, we demonstrated the applicability of eBird and GBIF (Global Biodiversity Information Facility), and produced national-scale forecasts to examine the possible impacts of climate change on terrestrial avifauna in India. Using data collected by citizen scientists, we developed fine-tuned Species Distribution Models (SDMs) and predicted 1091 terrestrial bird species that would be distributed in India by 2070 on two climatic surfaces (RCP 4.5 and 8.5), using Maximum Entropy-based species distribution algorithms. Of the 1091 species modelled, our findings indicate that 66–73% of bird species in India will shift to higher elevations or shift northward, and 58–59% of bird species (RCP 4.5 and 8.5) would lose a portion of their distribution ranges. Furthermore, distribution ranges of 41–40% of bird species would increase. Under both RCP scenarios (RCP 4.5 and 8.5), bird species diversity will significantly increase in regions above 2500 m in elevation. Both RCP scenarios predict extensive changes in the species richness of the western Himalayas, Sikkim, northeast India, and the western Ghats regions by 2070. This study has resulted in novel, high-resolution maps of terrestrial bird species richness across India, and we predict predominantly northward shifts in species ranges, similar to predictions made for avifauna in other regions, such as Europe and the USA.

Soil properties and plant species can predict population size and potential introduction sites of the endangered orchid Cypripedium calceolus

Background and Aims

To counteract the ongoing worldwide biodiversity loss, conservation actions are required to re-establish populations of threatened species. Two key factors predominantly involved in finding the most suitable habitats for endangered plant species are the surrounding plant community composition and the physicochemical parameters of the soil rooting zone. However, such factors are likely to be context- and species-dependent, so it remains unclear to what extent they influence the performance of target species.

Methods

We studied large and small Swiss populations of the endangered orchid Cypripedium calceolus. We measured functional traits related to C. calceolus plant and population performance (clonal patch area, plant height, number, of leaf, stems, flowers and fruits), realized vegetation surveys, soil profile analyses, and tested for relationships between plant traits and the surrounding vegetation structure or soil physicochemical parameters.

Results

Large populations contained bigger patches with more stems and leaves, and produced more flower per individual than small populations. Neither vegetation alliances nor soil classes per se could predict C. calceolus functional traits and population size. However, functional traits explaining population performance and size were related to specific soil parameters (soil organic matter content, pH and phosphorus), in addition to a combination of presence-absence of plant indicator species, relating to ecotones between forests and clearings.

Conclusion

We show that even for species that can grow across a wide range of vegetation groups both indicator species and specific soil parameters can be used to assess the most favourable sites to implement (re)-introduction actions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11104-023-05945-4.

Is the Endangered Species Act living to its full potential? The reassessment of the conservation status and recovery of Macbridea alba Chapm. as a case study

Since 1988, the Cooperative Endangered Species Conservation Fund or “Section 6” fund facilitates partnerships between the U.S. Fish and Wildlife Service and state agencies that aim to provide data pertinent to the recovery of Endangered Species Act (ESA) protected species. Despite the success of these efforts, research for rare plants is chronically underfunded and many species experience long periods of research inactivity that hinders their conservation. One example is Macbridea alba Chapm. (white birds-in-a-nest, Lamiaceae, M. alba from hereon), a federally threatened and state endangered mint endemic to four counties within the Florida panhandle. The species is a candidate for delisting after 30 years of protection under the ESA, however a lack of up-to-date data associated with the species has continually challenged the implementation of effective conservation programs and prolonged the recovery process. The focus of this paper is to review the timeline of recovery goals for M. alba, present a summary of recent research findings (i.e., species distribution models, habitat associations, reproductive ecology), and identify achievements as well as persistent obstacles to recovery and delisting. Our research focused on 5 of 10 recovery actions listed in the recovery plan for M. alba. Our findings provide updated data and make novel contributions to the protection of M. alba that will prioritize and improve management efforts. Overall, our work highlights frequent barriers to the recovery and delisting of rare species, using an endemic plant species as a case-study. Importantly, we outline effective methods for the rapid assessment of at-risk plant species that due to enduring data gaps, face an uncertain future in listing and recovery. We hope our work provides a convincing case demonstrating the critical need for current and expanded ESA funding and encourages a diversity of individuals and institutions to participate in critical rare plant research to swiftly fill research gaps and expedite recovery of some of the rarest plant species across the United States.

Assessment of suitable habitat of mangrove species for prioritizing restoration in coastal ecosystem of Sundarban Biosphere Reserve, India

Mangrove forests being the abode of diverse fauna and flora are vital for healthy coastal ecosystems. These forests act as a carbon sequester and protection shield against floods, storms, and cyclones. The mangroves of the Sundarban Biosphere Reserve (SBR), being one of the most dynamic and productive ecosystems in the world are in constant degradation. Hence, habitat suitability assessment of mangrove species is of paramount significance for its restoration and ecological benefits. The study aims to assess and prioritize restoration targets for 18 true mangrove species using 10 machine-learning algorithm-based habitat suitability models in the SBR. We identified the degraded mangrove areas between 1975 and 2020 by using Landsat images and field verification. The reserve was divided into 5609 grids using 1 km gird size for understanding the nature of mangrove degradation and collection of species occurrence data. A total of 36 parameters covering physical, environmental, soil, water, bio-climatic and disturbance aspects were chosen for habitat suitability assessment. Niche overlay function and grid-based habitat suitability classes were used to identify the species-based restoration prioritize grids. Habitat suitability analysis revealed that nearly half of the grids are highly suitable for mangrove habitat in the Reserve. Restoration within highly suitable mangrove grids could be achieved in the areas covered with less than 75 percent mangroves and lesser anthropogenic disturbance. The study calls for devising effective management strategies for monitoring and conserving the degraded mangrove cover. Monitoring and effective management strategies can help in maintaining and conserving the degraded mangrove cover. The model proves to be useful for assessing site suitability for restoring mangroves. The other geographical regions interested in assessing habitat suitability and prioritizing the restoration of mangroves may find the methodology adopted in this study effective.

Strategic roadmap to assess forest vulnerability under air pollution and climate change

Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly, most studies investigating the effects of air pollutants on forests have generally neglected the impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend (1) the establishment of much denser array of monitoring sites, particularly in the South Hemisphere; (2) further integration of ground and satellite monitoring; (3) generation of flux-based standards and critical levels taking into account the sensitivity of dominant forest tree species; (4) long-term monitoring of N, S, P cycles and base cations deposition together at global scale; (5) intensification of experimental studies, addressing the combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics and genomics; (7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and (8) development of models integrating air pollution and climate change data from long-term monitoring programs.

Mining rare Earth elements: Identifying the plant species most threatened by ore extraction in an insular hotspot

Conservation efforts in global biodiversity hotspots often face a common predicament: an urgent need for conservation action hampered by a significant lack of knowledge about that biodiversity. In recent decades, the computerisation of primary biodiversity data worldwide has provided the scientific community with raw material to increase our understanding of the shared natural heritage. These datasets, however, suffer from a lot of geographical and taxonomic inaccuracies. Automated tools developed to enhance their reliability have shown that detailed expert examination remains the best way to achieve robust and exhaustive datasets. In New Caledonia, one of the most important biodiversity hotspots worldwide, the plant diversity inventory is still underway, and most taxa awaiting formal description are narrow endemics, hence by definition hard to discern in the datasets. In the meantime, anthropogenic pressures, such as nickel-ore mining, are threatening the unique ultramafic ecosystems at an increasing rate. The conservation challenge is therefore a race against time, as the rarest species must be identified and protected before they vanish. In this study, based on all available datasets and resources, we applied a workflow capable of highlighting the lesser known taxa. The main challenges addressed were to aggregate all data available worldwide, and tackle the geographical and taxonomic biases, avoiding the data loss resulting from automated filtering. Every doubtful specimen went through a careful taxonomic analysis by a local and international taxonomist panel. Geolocation of the whole dataset was achieved through dataset cross-checking, local botanists’ field knowledge, and historical material examination. Field studies were also conducted to clarify the most unresolved taxa. With the help of this method and by analysing over 85,000 data, we were able to double the number of known narrow endemic taxa, elucidate 68 putative new species, and update our knowledge of the rarest species’ distributions so as to promote conservation measures.

Potential Impacts of Climate Change on the Habitat Suitability of the Dominant Tree Species in Greece

Climate change is affecting species distribution and ecosystem form and function. Forests provide a range of ecosystem services, and understanding their vulnerability to climate change is important for designing effective adaptation strategies. Species Distribution Modelling (SDM) has been extensively used to derive habitat suitability maps under current conditions and project species distribution shifts under climate change. In this study, we model the current and future habitat suitability of the dominant tree species in Greece (Abies cephalonica, Abies borisii-regis, Pinus brutia, Pinus halepensis, Pinus nigra, Quercus ilex, Quercus pubescens, Quercus frainetto and Fagus sylvatica), based on species-specific presence data from the EU-Forest database, enhanced with data from Greece that is currently under-represented in terms of tree species occurrence points. By including these additional presence data, areas with relatively drier conditions for some of the study species were included in the SDM development, yielding a potentially lower vulnerability under climate change conditions. SDMs were developed for each taxon using climate and soil data at a resolution of ~1 km². Model performance was assessed under current conditions and was found to adequately simulate potential distributions. Subsequently, the models were used to project the potential distribution of each species under the SSP1-2.6 and SSP5-8.5 scenarios for the 2041–2070 and 2071–2100 time periods. Under climate change scenarios, a reduction in habitat-suitable areas was predicted for most study species, with higher elevation taxa experiencing more pronounced potential habitat shrinkages. An exception was the endemic A. cephalonica and its sister species A. borisii-regis, which, although currently found at mid and high elevations, seem able to maintain their potential distribution under most climate change scenarios. Our findings suggest that climate change could significantly affect the distribution and dynamics of forest ecosystems in Greece, with important ecological, economic and social implications, and thus adequate mitigation measures should be implemented.

Spatial Distribution of Precise Suitability of Plantation: A Case Study of Main Coniferous Forests in Hubei Province, China

(1) Background. Conifers are the main plantation species in southern China, including Masson Pine (MP), Chinese fir (CF) and Chinese thuja (CT). Clarifying the suitable site conditions for these conifers is helpful for large-area afforestation, so as to manage forests to provide a higher level of ecosystem services. To achieve the research goals, we take the conifers in Hubei Province of southern China as a case study. (2) Methods. The situations of conifers, as well as environmental conditions of 448 sampling plots, were then investigated. The suitable growth environment of conifers in the studied area was determined by the maximum entropy algorithm, and the suitability spatial distribution of coniferous forests at the provincial level was also analyzed. (3) Results. The effect of the conifers suitability prediction model reached an accurate level, where AUC values of MP, CF and CT training set were 0.828, 0.856 and 0.970, respectively. Among multiple environmental factors, such as geography and climate, altitude is the most important factor affecting conifer growth. The contribution of altitude to the growth suitability of MP, CF and CT was 38.1%, 36.2% and 36.1%, respectively. Suitable areas of MP, CF and CT were 97,400 ha, 74,300 ha and 39,900 ha, accounting for 52.45%, 39.97% and 21.46% of the studied area, respectively. We concluded that the suitable site conditions of conifer plantations were 2800-5600 oC annual accumulated temperature, 40-1680 m a.s.l., and < 40° slopes. (4) Conclusions. The study suggests that accurate spatial suitability evaluation should be carried out to provide sufficient support for the large-area afforestation in southern China. However, due to our data and study area limitations, further studies are needed to explore the above findings for a full set of plantation species in an extensive area of southern China.

Retreat of Major European Tree Species Distribution under Climate Change—Minor Natives to the Rescue?

Climate change is projected to trigger strong declines in the potential distribution of major tree species in Europe. While minor natives have moved into the spotlight as alternatives, their ecology is often poorly understood. We use an ensemble species distribution modelling approach on a set of promising native tree species to gain insights into their distribution potential under different climate change scenarios. Moreover, we identify the urgency and potential of altered species distributions in favor of minor natives by comparing the niche dynamics of five major native tree species with the set of six minor natives in a case study. Our models project stark range contractions and range shifts among major tree species, strongly amplified under high emission scenarios. Abies alba, Picea abies and Fagus sylvatica are affected the strongest. While also experiencing range shifts, the minor European natives Castanea sativa, Sorbus torminalis, and Ulmus laevis all considerably expand their range potential across climate change scenarios. Accompanied by Carpinus betulus, with a stable range size, they hold the potential to substantially contribute to sustainably adapting European forest to climate change.

Understanding the biology of species' ranges: when and how does evolution change the rules of ecological engagement?

Understanding processes that limit species' ranges has been a core issue in ecology and evolutionary biology for many decades, and has become increasingly important given the need to predict the responses of biological communities to rapid environmental change. However, we still have a poor understanding of evolution at range limits and its capacity to change the ecological 'rules of engagement' that define these communities, as well as the time frame over which this occurs. Here we link papers in the current volume to some key concepts involved in the interactions between evolutionary and ecological processes at species' margins. In particular, we separate hypotheses about species' margins that focus on hard evolutionary limits, which determine how genotypes interact with their environment, from those concerned with soft evolutionary limits, which determine where and when local adaptation can persist in space and time. We show how theoretical models and empirical studies highlight conditions under which gene flow can expand local limits as well as contain them. In doing so, we emphasize the complex interplay between selection, demography and population structure throughout a species' geographical and ecological range that determines its persistence in biological communities. However, despite some impressively detailed studies on range limits, particularly in invertebrates and plants, few generalizations have emerged that can predict evolutionary responses at ecological margins. We outline some directions for future work such as considering the impact of structural genetic variants and metapopulation structure on limits, and the interaction between range limits and the evolution of mating systems and non-random dispersal. This article is part of the theme issue 'Species' ranges in the face of changing environments (Part II)'.

Habitat Suitability Modeling of Rare Turkeybeard (Xerophyllum asphodeloides) Species in the Talladega National Forest, Alabama, USA

This study focused on the rare and threatened plant species eastern turkeybeard (Xerophyllum asphodeloides (L.) Nutt.) and its presence or absence in the Talladega National Forest in Alabama, USA. An ensemble suitable habitat map was developed using four different modeling methods (MaxEnt, Generalized Linear Model, Generalized Additive Model, and Random Forest). AUC evaluation scores for each model were 0.99, 0.96, 0.98, and 0.99, respectively. Biserial correlation scores for models ranged from 0.71 (GLM) to 0.94 (RF). The four different models agreed suitable habitat was found to cover 159.57 ha of the land. The ground slope variable was the most contributive variable in the MaxEnt and RF models and was also significant in the GLM and GAM models. The knowledge gained from this research can be used to establish and implement habitat suitability strategies across the Talladega National Forest and similar ecosystems in the southern United States.

Projected climate-driven changes in pollen emission season length and magnitude over the continental United States

Atmospheric conditions affect the release of anemophilous pollen, and the timing and magnitude will be altered by climate change. As simulated with a pollen emission model and future climate data, warmer end-of-century temperatures (4-6 K) shift the start of spring emissions 10-40 days earlier and summer/fall weeds and grasses 5-15 days later and lengthen the season duration. Phenological shifts depend on the temperature response of individual taxa, with convergence in some regions and divergence in others. Temperature and precipitation alter daily pollen emission maxima by -35 to 40% and increase the annual total pollen emission by 16-40% due to changes in phenology and temperature-driven pollen production. Increasing atmospheric CO2 may increase pollen production, and doubling production in conjunction with climate increases end-of-century emissions up to 200%. Land cover change modifies the distribution of pollen emitters, yet the effects are relatively small (<10%) compared to climate or CO2. These simulations indicate that increasing pollen and longer seasons will increase the likelihood of seasonal allergies.

Assessment of Drought-Tolerant Provenances of Austria’s Indigenous Tree Species

(1) Background: Forestry will have to react to climate change because many tree species suffer. Mitigation can be realized either by planting non-native trees from regions with high climatic stress or by utilizing native tree provenances already adapted to stressful environments. Non-native trees have often generated problems in the past due to uncontrolled invasiveness. The use of native trees pre-adapted to the prospective climatic conditions is far less risky for the respective ecosystems. We offer a tool for selecting ecotypes of native trees as provenances for future forestry. (2) Methods: We propose the selection of tree species native to Middle Europe from a database of vegetation relevés of ± natural forest stands. By calculating the mean ecological indicator values of stands from their vegetation, cover sites can be elected that can provide seeds of provenances well adapted to future climatic conditions. (3) Results: By selecting the 10% partition of the most extreme stands of European tree species, seeds can be sampled and propagated for re-cultivating forests fit for future climate. (4) Conclusions: One can expect ecotypes of tree species that grow well on dry sites, since generations have faced evolutionary selection, for survival under stressful environments. This approach helps to avoid ecological risks of non-native trees.

The Current and Future Potential Geographical Distribution and Evolution Process of Catalpa bungei in China

Catalpa bungei C. A. Mey. (C. bungei) is one of the recommended native species for ecological management in China. It is a fast-growing tree of high economic and ecological importance, but its rare resources, caused by anthropogenic destruction and local climatic degradation, have not satisfied the requirements. It has been widely recommended for large-scale afforestation of ecological management and gradually increasing in recent years, but the impact mechanism of climate change on its growth has not been studied yet. Studying the response of species to climate change is an important part of national afforestation planning. Based on combinations of climate, topography, soil variables, and the multiple model ensemble (MME) of CMIP6, this study explored the relationship between C. bungei and climate change, then constructed Maxent to predict its potential distribution under SSP126 and SSP585 and analyzed its dominant environmental factors. The results showed that C. bungei is widely distributed in Henan, Hebei, Hubei, Anhui, Jiangsu, and Shaanxi provinces and others where it covers an area of 2.96 × 106 km2. Under SSP126 and SSP585, its overall habitat area will increase by more than 14.2% in 2080–2100, which mainly indicates the transformation of unsuitable areas into low suitable areas. The center of its distribution will migrate to the north with a longer distance under SSP585 than that under SSP126, and it will transfer from the junction of Shaanxi and Hubei province to the north of Shaanxi province under SSP585 by 2100. In that case, C. bungei shows a large-area degradation trend in the south of the Yangtze River Basin but better suitability in the north of the Yellow River Basin, such as the Northeast Plain, the Tianshan Mountains, the Loess Plateau, and others. Temperature factors have the greatest impact on the distribution of C. bungei. It is mainly affected by the mean temperature of the coldest quarter, followed by precipitation of the wettest month, mean diurnal range, and precipitation of the coldest quarter. Our results hence demonstrate that the increase of the mean temperature of the coldest quarter becomes the main reason for its degradation, which simultaneously means a larger habitat boundary in Northeast China. The findings provide scientific evidence for the ecological restoration and sustainable development of C. bungei in China.

Assessing Black Locust Biomass Accumulation in Restoration Plantations

Forests (either natural or planted) play a key role in climate change mitigation due to their huge carbon-storing potential. In the 1980s, the Hellenic Public Power Corporation (HPPC) started the rehabilitation of lignite post-mining areas in Northwest Greece by planting mainly black locust (Robinia pseudoacacia L.). Today, these plantations occupy about 2570 ha, but the accumulation of Above Ground Biomass (AGB) and deadwood has not been assessed to date. Therefore, we aimed at estimating these biomass pools by calibrating an allometric model for AGB, performing an inventory for both pools and predicting the spatial distribution of AGB. 214 sample plots of 100 m2 each were set up through systematic sampling in a grid dimension of 500 × 500 m and tree dbh and height were recorded. AGB was estimated using an exponential allometric model and performing inventory measurements and was on average 57.6 t ha−1. Kriging analysis reliably estimated mean AGB, but produced errors in the prediction of high and low biomass values, related to the high fragmentation and heterogeneity of the studied area. Mean estimated AGB was low compared with European biomass yield tables for black locust. Similarly, standing deadwood was low (6–10%) and decay degrees were mostly 1 and 2, indicating recent deadwood formation. The overall low biomass accumulation in the studied black locust restoration plantations may be partially attributed to their young age (5–30 years old), but is comparable to that reported in black locust restoration plantation in extremely degraded sites. Thus, black locust successfully adapted to the studied depositions of former mines and its accumulated biomass has the potential to improve the carbon footprint of the region. However, the invasiveness of the species should be considered for future management planning of these restoration plantations.

Evaluating the Influence of Climate Change on Sophora moorcroftiana (Benth.) Baker Habitat Distribution on the Tibetan Plateau Using Maximum Entropy Model

The ecosystems across the Tibetan Plateau are changing rapidly in response to climate change, which poses unprecedented challenges for the control and mitigation of desertification on the Tibetan Plateau. Sophora moorcroftiana (Benth.) Baker is a drought-resistant plant species that has great potential to be used for desertification and soil degradation control on the Tibetan Plateau. In this study, using a maximum entropy (MaxEnt) niche model, we characterized the habitat distribution of S. moorcroftiana on the Tibetan Plateau under both current and future climate scenarios. To construct a robust model, 242 population occurrence records, gathered from our field surveys, historical data records, and a literature review, were used to calibrate the MaxEnt model. Our results showed that, under current environmental conditions, the habitat of S. moorcroftiana was concentrated in regions along the Yarlung Tsangpo, Lancang, and Jinsha rivers on the Tibetan Plateau. Elevation, isothermality, and minimal air temperature of the coldest month played a dominant role in determining the habitat distribution of S. moorcroftiana. Under future climate scenarios, the increased air temperature was likely to benefit the expansion of S. moorcroftiana over the short term, but, in the long run, continued warming may restrict the growth of S. moorcroftiana and lead to a contraction in its habitat. Importantly, the Yarlung Tsangpo River valley was found to be the core habitat of S. moorcroftiana, and this habitat moved westwards along the Yarlung Tsangpo River under future climate scenarios, but did not detach from it. This finding suggests that, with the current pace of climate change, an increase in efforts to protect and cultivate S. moorcroftiana is necessary and critical to control desertification on the Tibetan Plateau.

A multi-data ensemble approach for predicting woodland type distribution: Oak woodland in Britain

Interactions between soil, topography, and climatic site factors can exacerbate and/or alleviate the vulnerability of oak woodland to climate change. Reducing climate-related impacts on oak woodland habitats and ecosystems through adaptation management requires knowledge of different site interactions in relation to species tolerance. In Britain, the required thematic detail of woodland type is unavailable from digital maps. A species distribution model (SDM) ensemble, using biomod2 algorithms, was used to predict oak woodland. The model was cross-validated (50%:50% - training:testing) 30 times, with each of 15 random sets of absence data, matching the size of presence data, to maximize environmental variation while maintaining data prevalence. Four biomod2 algorithms provided stable and consistent TSS-weighted ensemble mean results predicting oak woodland as a probability raster. Biophysical data from the Ecological Site Classification (forest site classification) for Britain were used to characterize oak woodland sites. Several forest datasets were used, each with merits and weaknesses: public forest estate subcompartment database map (PFE map) for oak-stand locations as a training dataset; the national forest inventory (NFI) "published regional reports" of oak woodland area; and an "NFI map" of indicative forest type broad habitat. Broadleaved woodland polygons of the NFI map were filled with the biomod2 oak woodland probability raster. Ranked pixels were selected up to the published NFI regional area estimate of oak woodland and matched to the elevation distribution of oak woodland stands, from "NFI survey" sample squares. Validation using separate oak woodland data showed that the elevation filter significantly improved the accuracy of predictions from 55% (p = .53) to 83% coincidence success rate (p < .0001). The biomod2 ensemble, with masking and filtering, produced a predicted oak woodland map, from which site characteristics will be used in climate change interaction studies, supporting adaptation management recommendations for forest policy and practice.

Biogeographic Distribution of Cedrela spp. Genus in Peru Using MaxEnt Modeling: A Conservation and Restoration Approach

The increasing demand for tropical timber from natural forests has reduced the population sizes of native species such as Cedrela spp. because of their high economic value. To prevent the decline of population sizes of the species, all Cedrela species have been incorporated into Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). The study presents information about the modeled distribution of the genus Cedrela in Peru that aims to identify potential habitat distribution of the genus, its availability in areas protected by national service of protected areas, and highlighted some areas because of their conservation relevance and the potential need for restoration. We modeled the distribution of the genus Cedrela in Peru using 947 occurrence records that included 10 species (C. odorata, C. montana, C. fissilis, C. longipetiolulata, C. angustifolia, C. nebulosa, C. kuelapensis, C. saltensis, C. weberbaueri, and C. molinensis). We aim to identify areas environmentally suitable for the occurrence of Cedrela that are legally protected by the National Service of Protected Areas (PAs) and those that are ideal for research and restoration projects. We used various environmental variables (19 bioclimatic variables, 3 topographic factors, 9 edaphic factors, solar radiation, and relative humidity) and the maximum entropy model (MaxEnt) to predict the probability of occurrence. We observed that 6.7% (86,916.2 km2) of Peru presents a high distribution probability of occurrence of Cedrela, distributed in 17 departments, with 4.4% (10,171.03 km2) of the area protected by PAs mainly under the category of protection forests. Another 11.65% (21,345.16 km2) of distribution covers areas highly prone to degradation, distributed mainly in the departments Ucayali, Loreto, and Madre de Dios, and needs immediate attention for its protection and restoration. We believe that the study will contribute significantly to conserve Cedrela and other endangered species, as well as to promote the sustainable use and management of timber species as a whole.

Chikungunya Beyond the Tropics: Where and When Do We Expect Disease Transmission in Europe?

Chikungunya virus disease (chikungunya) is a mosquito-borne infectious disease reported in at least 50 countries, mostly in the tropics. It has spread around the globe within the last two decades, with local outbreaks in Europe. The vector mosquito Aedes albopictus (Diptera, Culicidae) has already widely established itself in southern Europe and is spreading towards central parts of the continent. Public health authorities and policymakers need to be informed about where and when a chikungunya transmission is likely to take place. Here, we adapted a previously published global ecological niche model (ENM) by including only non-tropical chikungunya occurrence records and selecting bioclimatic variables that can reflect the temperate and sub-tropical conditions in Europe with greater accuracy. Additionally, we applied an epidemiological model to capture the temporal outbreak risk of chikungunya in six selected European cities. Overall, the non-tropical ENM captures all the previous outbreaks in Europe, whereas the global ENM had underestimated the risk. Highly suitable areas are more widespread than previously assumed. They are found in coastal areas of the Mediterranean Sea, in the western part of the Iberian Peninsula, and in Atlantic coastal areas of France. Under a worst-case scenario, even large areas of western Germany and the Benelux states are considered potential areas of transmission. For the six selected European cities, June–September (the 22th–38th week) is the most vulnerable time period, with the maximum continuous duration of a possible transmission period lasting up to 93 days (Ravenna, Italy).

Distribution and habitat attributes associated with the Himalayan red panda in the westernmost distribution range

The Himalayan red panda (Ailurus fulgens), a recently confirmed distinct species in the red panda genus, is distributed in Nepal, India, Bhutan, and south Tibet. Nepal represents the westernmost distribution of the Himalayan red panda. This study aims to determine important habitat features influencing the distribution of red panda and recommend possible habitat corridors. This manuscript described current potential habitat of 3,222 km2 with the relative abundance of 3.34 signs/km in Nepal. Aspect, canopy cover, bamboo cover, and distance to water were the important habitat attributes. It suggested five potential corridors in western Nepal. Overall, the study has important implications for conservation of the Himalayan red panda in western distribution range.

Ecological impact assessment of climate change and habitat loss on wetland vertebrate assemblages of the Great Barrier Reef catchment and the influence of survey bias

Wetlands are among the most vulnerable ecosystems, stressed by habitat loss and degradation from expanding and intensifying agricultural and urban areas. Climate change will exacerbate the impacts of habitat loss by altering temperature and rainfall patterns. Wetlands within Australia's Great Barrier Reef (GBR) catchment are not different, stressed by extensive cropping, urban expansion, and alteration for grazing. Understanding how stressors affect wildlife is essential for the effective management of biodiversity values and minimizing unintended consequences when trading off the multiple values wetlands support. Impact assessment is difficult, often relying on an aggregation of ad hoc observations that are spatially biased toward easily accessible areas, rather than systematic and randomized surveys. Using a large aggregate database of ad hoc observations, this study aimed to examine the influence of urban proximity on machine-learning models predicting taxonomic richness and assemblage turnover, relative to other habitat, landscape, and climate variables, for vertebrates dwelling in the wetlands of the GBR catchment. The distance from the nearest city was, by substantial margins, the most influential factor in predicting the richness and assemblage turnover of all vertebrate groups, except fish. Richness and assemblage turnover was predicted to be greatest nearest the main urban centers. The extent of various wetland habitats was highly influential in predicting the richness of all groups, while climate (predominately the rainfall in the wettest quarter) was highly influential in predicting assemblage turnover for all groups. Bias of survey records toward urban centers strongly influenced our ability to model wetland-affiliated vertebrates and may obscure our understanding of how vertebrates respond to habitat loss and climate change. This reinforces the need for randomized and systematic surveys to supplement existing ad hoc surveys. We urge modelers in other jurisdictions to better portray the potential influence of survey biases when modeling species distributions.

Capturing response differences of species distribution to climate and human pressures by incorporating local adaptation: Implications for the conservation of a critically endangered species

Considering local adaptation has been increasingly involved in forecasting species distributions under climate change and the management of species conservation. Herein, we take the critically endangered Chinese giant salamander (Andrias davidianus) that has both a low dispersal ability and distinct population divergence in different regions as an example. Basin-scale models that represent different populations in the Huanghe River Basin (HRB), the Yangtze River Basin (YRB), and the Pearl River Basin (PRB) were established using ensemble species distribution models. The species ranges under the future human population density (HPD) and climate change were predicted, and the range loss was evaluated for local basins in 2050 and 2070. Our results showed that the predominant factors affecting species distributions differed among basins, and the responses of the species occurrence to HPD and climate factors were distinctly different from northern to southern basins. Future HPD changes would be the most influential factor that engenders negative impacts on the species distribution in all three basins, especially in the HRB. Climate change will likely be less prominent in decreasing the species range, excluding in the YRB and PRB under the highest-emissions scenario in 2050. Overall, the high-emissions scenario would more significantly aggravate the negative impacts produced by HPD change in both 2050 and 2070, with maximum losses of species ranges in the HRB, YRB, and PRB of 83.4%, 60.0%, and 53.5%, respectively, under the scenarios of the combined impacts of HPD and climate changes. We proposed adapted conservation policies to effectively protect the habitat of this critically endangered animal in different basins based on the outcomes. Our research addresses the importance of incorporating local adaptation into species distribution modeling to inform conservation and management decisions.

Black locust (Robinia pseudoacacia L.) range contraction and expansion in Europe under changing climate

Robinia pseudoacacia is one of the most frequent non-native species in Europe. It is a fast-growing tree of high economic and cultural importance. On the other hand, it is an invasive species, causing changes in soil chemistry and light regime, and consequently altering the plant communities. Previously published models developed for the potential distribution of R. pseudoacacia concerned 2070, and were based mainly on data from Western and Central Europe; here we extended these findings and included additional data from Eastern Europe. To fill the gap in current knowledge of R. pseudoacacia distribution and improve the reliability of forecasts, we aimed to (i) determine the extent to which the outcome of range modeling will be affected by complementing R. pseudoacacia occurrence data with sites from Central, Southeastern, and Eastern Europe, (ii) identify and quantify the changes in the availability of climate niches for 2050 and 2070, and discuss their impacts on forest management and nature conservation. We showed that the majority of the range changes expected in 2070 will occur as early as 2050. In comparison to previous studies, we demonstrated a greater eastward shift of potential niches of this species and a greater decline of potential niches in Southern Europe. Consequently, future climatic conditions will likely favor the occurrence of R. pseudoacacia in Central and Northeastern Europe where this species is still absent or relatively rare. There, controlling the spread of R. pseudoacacia will require monitoring sources of invasion in the landscape and reducing the occurrence of this species. The expected effects of climate change will likely be observed 20 years earlier than previously forecasted. Hence we highlighted the urgent need for acceleration of policies aimed at climate change mitigation in Europe. Also, our results showed the need for using more complete distribution data to analyze potential niche models.

Predicting the Global Distribution of Solenopsis geminata (Hymenoptera: Formicidae) under Climate Change Using the MaxEnt Model

Simple Summary

Climate change influences the distribution of species. The tropical fire ant Solenopsis geminata (Hymenoptera: Formicidae) is a serious invasive species that damages the native ecosystem. In this study, we evaluated the current and future distribution of S. geminata under climate change using the ecological niche model. The model results showed that the favorable habitat area of S. geminata will expand to higher latitudes on a global scale due to future global warming. Some countries located in America and East Asia, such as Brazil, China, South Korea, the USA, and Uruguay, can be threatened by S. geminata due to climate change.

Abstract

The tropical fire ant Solenopsis geminata (Hymenoptera: Formicidae) is a serious invasive species that causes a decline in agricultural production, damages infrastructure, and harms human health. This study was aimed to develop a model using the maximum entropy (MaxEnt) algorithm to predict the current and future distribution of S. geminata on a global scale for effective monitoring and management. In total, 669 occurrence sites of S. geminata and six bioclimatic variables of current and future climate change scenarios for 2050 and 2100 were used for the modeling. The annual mean temperature, annual precipitation, and precipitation in the driest quarter were the key influential factors for determining the distribution of S. geminata. Although the potential global distribution area of S. geminata is predicted to decrease slightly under global warming, the distribution of favorable habitats is predicted to expand to high latitudes under climate scenarios. In addition, some countries in America and East Asia, such as Brazil, China, South Korea, the USA, and Uruguay, are predicted to be threatened by S. geminata invasion under future climate change. These findings can facilitate the proactive management of S. geminata through monitoring, surveillance, and quarantine measures.

Evaluation of Land Suitability Methods with Reference to Neglected and Underutilised Crop Species: A Scoping Review

In agriculture, land use and land classification address questions such as "where", "why" and "when" a particular crop is grown within a particular agroecology. To date, there are several land suitability analysis (LSA) methods, but there is no consensus on the best method for crop suitability analysis. We conducted a scoping review to evaluate methodological strategies for LSA. Secondary to this, we assessed which of these would be suitable for neglected and underutilised crop species (NUS). The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multi-criteria decision-making (MCDM) methods such as analytical hierarchy process (AHP) (14.9%) and fuzzy methods (12.9%); crop simulation models (9.9%) and machine learning related methods (25.7%) are gaining popularity over traditional methods. The MCDM methods, namely AHP and fuzzy, are commonly applied to LSA while crop models and machine learning related methods are gaining popularity. A total of 67 parameters from climatic, hydrology, soil, socio-economic and landscape properties are essential in LSA. Unavailability and the inclusion of categorical datasets from social sources is a challenge. Using big data and Internet of Things (IoT) improves the accuracy and reliability of LSA methods. The review expects to provide researchers and decision-makers with the most robust methods and standard parameters required in developing LSA for NUS. Qualitative and quantitative approaches must be integrated into unique hybrid land evaluation systems to improve LSA.

Probabilistic Provenance Detection and Management Pathways for Pseudotsuga menziesii (Mirb.) Franco in Italy Using Climatic Analogues

The introduction of Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] in Europe has been one of the most important and extensive silvicultural experiments since the 1850s. This success was mainly supported by the species’ wide genome and phenotypic plasticity even if the genetic origin of seeds used for plantations is nowadays often unknown. This is especially true for all the stands planted before the IUFRO experimentation in the 1960s. In this paper, a methodology to estimate the Douglas-fir provenances currently growing in Italy is proposed. The raw data from the last Italian National Forest Inventory were combined with literature information to obtain the current spatial distribution of the species in the country representing its successful introduction. Afterwards, a random forest classification model was run using downscaled climatic data as predictors and the classification scheme adopted in previous research studies in the Pacific North West of America. The analysis highlighted good matching between the native and the introduction range in Italy. Coastal provenances from British Columbia and the dry coast of Washington were detected as the most likely seed sources, covering 63.4% and 33.8% of the current distribution of the species in the country, respectively. Interior provenances and those from the dry coast of Oregon were also represented but limited to very few cases. The extension of the model on future scenarios predicted a gradual shift in suitable provenances with the dry coast of Oregon in the mid-term (2050s) and afterwards California (2080s) being highlighted as possible new seed sources. However, only further analysis with genetic markers and molecular methods will be able to confirm the proposed scenarios. A validation of the genotypes currently available in Italy will be mandatory as well as their regeneration processes (i.e., adaptation), which may also diverge from those occurring in the native range due to a different environmental pressure. This new information will also add important knowledge, allowing a refinement of the proposed modeling framework for a better support for forest managers.

ClimateEU, scale-free climate normals, historical time series, and future projections for Europe

Interpolated climate data have become essential for regional or local climate change impact assessments and the development of climate change adaptation strategies. Here, we contribute an accessible, comprehensive database of interpolated climate data for Europe that includes monthly, annual, decadal, and 30-year normal climate data for the last 119 years (1901 to 2019) as well as multi-model CMIP5 climate change projections for the 21^st century. The database also includes variables relevant for ecological research and infrastructure planning, comprising more than 20,000 climate grids that can be queried with a provided ClimateEU software package. In addition, 1 km and 2.5 km resolution gridded data generated by the software are available for download. The quality of ClimateEU estimates was evaluated against weather station data for a representative subset of climate variables. Dynamic environmental lapse rate algorithms employed by the software to generate scale-free climate variables for specific locations lead to improvements of 10 to 50% in accuracy compared to gridded data. We conclude with a discussion of applications and limitations of this database.

Measurement(s)	temperature of air • volume of hydrological precipitation
Technology Type(s)	digital curation • computational modeling technique
Factor Type(s)	monthly temperature measurement • monthly precipitation measurement
Sample Characteristic - Environment	climate system
Sample Characteristic - Location	Europe

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13190834

Distribution Models of Timber Species for Forest Conservation and Restoration in the Andean-Amazonian Landscape, North of Peru

The Andean-Amazonian landscape has been universally recognized for its wide biodiversity, and is considered as global repository of ecosystem services. However, the severe loss of forest cover and rapid reduction of the timber species seriously threaten this ecosystem and biodiversity. In this study, we have modeled the distribution of the ten most exploited timber forest species in Amazonas (Peru) to identify priority areas for forest conservation and restoration. Statistical and cartographic protocols were applied with 4454 species records and 26 environmental variables using a Maximum Entropy model (MaxEnt). The result showed that the altitudinal variable was the main regulatory factor that significantly controls the distribution of the species. We found that nine species are distributed below 1000 m above sea level (a.s.l.), except Cedrela montana, which was distributed above 1500 m a.s.l., covering 40.68%. Eight of 10 species can coexist, and the species with the highest percentage of potential restoration area is Cedrela montana (14.57% from Amazonas). However, less than 1.33% of the Amazon has a potential distribution of some species and is protected under some category of conservation. Our study will contribute as a tool for the sustainable management of forests and will provide geographic information to complement forest restoration and conservation plans.

Potential Impact of Climate Change on the Forest Coverage and the Spatial Distribution of 19 Key Forest Tree Species in Italy under RCP4.5 IPCC Trajectory for 2050s

Forests provide a range of ecosystem services essential for human wellbeing. In a changing climate, forest management is expected to play a fundamental role by preserving the functioning of forest ecosystems and enhancing the adaptive processes. Understanding and quantifying the future forest coverage in view of climate changes is therefore crucial in order to develop appropriate forest management strategies. However, the potential impacts of climate change on forest ecosystems remain largely unknown due to the uncertainties lying behind the future prediction of models. To fill this knowledge gap, here we aim to provide an uncertainty assessment of the potential impact of climate change on the forest coverage in Italy using species distribution modelling technique. The spatial distribution of 19 forest tree species in the country was extracted from the last national forest inventory and modelled using nine Species Distribution Models algorithms, six different Global Circulation Models (GCMs), and one Regional Climate Models (RCMs) for 2050s under an intermediate forcing scenario (RCP 4.5). The single species predictions were then compared and used to build a future forest cover map for the country. Overall, no sensible variation in the spatial distribution of the total forested area was predicted with compensatory effects in forest coverage of different tree species, whose magnitude and patters appear largely modulated by the driving climate models. The analyses reported an unchanged amount of total land suitability to forest growth in mountain areas while smaller values were predicted for valleys and floodplains than high-elevation areas. Pure woods were predicted as the most influenced when compared with mixed stands which are characterized by a greater species richness and, therefore, a supposed higher level of biodiversity and resilience to climate change threatens. Pure softwood stands along the Apennines chain in central Italy (e.g., Pinus, Abies) were more sensitive than hardwoods (e.g., Fagus, Quercus) and generally characterized by pure and even-aged planted forests, much further away from their natural structure where admixture with other tree species is more likely. In this context a sustainable forest management strategy may reduce the potential impact of climate change on forest ecosystems. Silvicultural practices should be aimed at increasing the species richness and favoring hardwoods currently growing as dominating species under conifers canopy, stimulating the natural regeneration, gene flow, and supporting (spatial) migration processes.

Predicting Suitable Habitats of Camptotheca acuminata Considering Both Climatic and Soil Variables

Camptotheca acuminata is considered a natural medicinal plant with antitumor activity. The assessment of climate change impact on its suitable habitats is important for cultivation and conservation. In this study, we applied a novel approach to build ecological niche models with both climate and soil variables while the confounding effects between the variables in the two categories were avoided. We found that the degree-days below zero and mean annual precipitation were the most important climatic factors, while the basic soil saturation, soil gravel volume percentage, and clay content were the main soil factors, determining the suitable habitats of C. acuminata. We found that suitable habitats of this species would moderately increase in future climates under both the RCP4.5 and RCP8.5 climate change scenarios for the 2020s, 2050s, and 2080s. However, substantial shifts among levels of habitat suitability were projected. The dual high-suitable habitats would expand, which would be favorable for commercial plantations. Our findings contribute to a better understanding of the impact of climate change on this species and provide a scientific basis for the cultivation and conservation purposes.