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Climate variations in temperature and precipitation significantly impact forest productivity. Precipitation influences the physiology and growth of species, while temperature regulates photosynthesis, respiration, and transpiration. This study developed bioclimatic models to assess how climate change will affect the carbon density of aboveground biomass (cdAGB) in Mexico's coniferous forests for 2050 and 2070. We used cdAGB data from the National Forest and Soils Inventory (INFyS) of Mexico and 19 bioclimatic variables from WorldClim ver. 2.0. The best predictors of cdAGB were obtained using machine learning techniques with the "caret" library in R. The model was trained with 80% of the data and validated with the remaining 20% using Generalized Linear Models (GLMs). Current cdAGB prediction maps were generated using the best predictors. Future cdAGB was calculated with the average of three general circulation models (GCMs) of future climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5), under four Representative Concentration Pathways (RCPs): 2.6, 4.5, 6.0, and 8.5 W/m2. The results indicate cdAGB losses in all climate scenarios, reaching up to 15 Mg C ha−1, and could occur under the RCP 8.5 scenario by 2070 in the central region of the country. Temperature-related variables are more important than precipitation variables. Bioclimatic variables can explain up to 20% of the total variance in cdAGB. The temperature in the study area is expected to increase by 2.66 °C by 2050 and 3.36 °C by 2070, while precipitation is expected to fluctuate by ±10% relative to the current values, which could geographically redistribute the cdAGB of the country's coniferous forests. These findings underscore the need for forest management to focus not only on biodiversity conservation but also on the carbon storage capacity of these ecosystems. [ABSTRACT FROM AUTHOR]

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Species distributions are driven by abiotic and biotic factors, but the importance of variation in the availability and quality of critical resources is poorly understood. Disentangling the relative importance of these factors—abiotic environment, availability of critical resources, and resource quality—will be important to modeling species current distributions and responses to projected climate change. We address these questions using species distribution models (SDMs) for the western monarch butterfly population (Danaus plexippus), whose larvae feed exclusively on Asclepias species known for their heterogeneous distribution and variation in host quality. We modeled the distribution of 24 Asclepias species to compare three monarch distribution models with increasing levels of complexity: (1) a null model using only environmental factors (climate envelope model), (2) a model using environmental factors and Asclepias availability estimated as species richness, (3) and a model using environmental factors and Asclepias' availability weighted by host plant quality as assessed through a greenhouse bioassay of larval performance. Asclepias models predicted that half of the Asclepias species will expand their ranges and shift toward higher latitudes, while half will contract. These patterns were uncorrelated with host plant quality. Among the three monarch models, the climate envelope model was the poorest performing. Models accounting for host plant availability performed best, while accounting for host plant quality did not improve model performance. The climate envelope model estimated more restrictive contemporary and future monarch ranges compared to both host plant models. Although all three models predicted future monarch range expansions, the projected future distributions varied among models. The climate envelope model predicted range expansions along the Pacific coast and contractions inland. In contrast, the host plant availability and quality models predicted range expansions in both of these regions and, as a result, 14% and 19% increases in distribution (respectively) relative to the climate envelope model. These models do not include other factors affecting monarch persistence. Nevertheless, our findings suggest that accounting for information on host plant availability and response to climate change is necessary to predict future species distributions, but that variation in the quality of those critical resources may be of secondary importance. [ABSTRACT FROM AUTHOR]

Abstract Species distributions are driven by abiotic and biotic factors, but the importance of variation in the availability and quality of critical resources is poorly understood. Disentangling the relative importance of these factors—abiotic environment, availability of critical resources, and resource quality—will be important to modeling species current distributions and responses to projected climate change. We address these questions using species distribution models (SDMs) for the western monarch butterfly population (Danaus plexippus), whose larvae feed exclusively on Asclepias species known for their heterogeneous distribution and variation in host quality. We modeled the distribution of 24 Asclepias species to compare three monarch distribution models with increasing levels of complexity: (1) a null model using only environmental factors (climate envelope model), (2) a model using environmental factors and Asclepias availability estimated as species richness, (3) and a model using environmental factors and Asclepias’ availability weighted by host plant quality as assessed through a greenhouse bioassay of larval performance. Asclepias models predicted that half of the Asclepias species will expand their ranges and shift toward higher latitudes, while half will contract. These patterns were uncorrelated with host plant quality. Among the three monarch models, the climate envelope model was the poorest performing. Models accounting for host plant availability performed best, while accounting for host plant quality did not improve model performance. The climate envelope model estimated more restrictive contemporary and future monarch ranges compared to both host plant models. Although all three models predicted future monarch range expansions, the projected future distributions varied among models. The climate envelope model predicted range expansions along the Pacific coast and contractions inland. In contrast, the host plant availability and quality models predicted range expansions in both of these regions and, as a result, 14% and 19% increases in distribution (respectively) relative to the climate envelope model. These models do not include other factors affecting monarch persistence. Nevertheless, our findings suggest that accounting for information on host plant availability and response to climate change is necessary to predict future species distributions, but that variation in the quality of those critical resources may be of secondary importance.

In the absence of bioclimatic models, and given the scarcity of information about parameters and other input data, the panel data methodology was applied to establish the magnitude and direction of expected consequences in milk production, which is the main economic activity of Jimaguayú municipality. This study presents a summary about the theory of panel data models. Several specifications are evaluated, obtaining expressions of long-term elasticity of rainfall and temperature and its effect on milk production. Applied empirical models indicate that milk production in Jimaguayú could decrease by approximately 200 thousand L per year, due to the combined effect of the decrease in rainfall and the increase in temperature, with economic losses in the order of constant 100 thousand dollars informed in 2014. [ABSTRACT FROM AUTHOR]

Aim Species distribution models built with geographically restricted data often fail to capture the full range of conditions experienced by species across their entire distribution area. Using such models to predict distribution shifts under future environmental change may, therefore, produce biased projections. However, restricted-scale models have the potential to include a larger sample of taxa for which distribution data are available and to provide finer-resolution projections that are better applied to conservation planning than the forecasts of broad-scale models. We examine the circumstances under which the projected shifts in species richness patterns derived from restricted-scale and broad-scale models are most likely to be similar. Location Europe. Methods The distribution of butterflies in Finland, Belgium/Netherlands and Spain was modelled based on restricted-scale (local) and broad-scale (continental) distribution and climate data. Both types of models were projected under future climate change scenarios to assess potential changes in species richness. Results In Finland, species richness was projected to increase strongly based on restricted-scale models and to decrease slightly with broad-scale models. In Belgium/Netherlands, restricted-scale models projected a larger decrease in richness than broad-scale models. In Spain, both models projected a slight decrease in richness. We obtained similar projections based on restricted-scale and broad-scale models only in Spain because the climatic conditions available here covered the warm part of the distributions of butterflies better than in Finland and Belgium/Netherlands. Main conclusions Restricted-scale models that fail to capture the warm part of species distributions produce biased estimates of future changes in species richness when projected under climatic conditions with no modern analogue in the study area. We recommend the use of distribution data beyond the boundaries of the study area to capture the part of the species response curves reflecting the climatic conditions that will prevail within that area in the future. [ABSTRACT FROM AUTHOR]

The effects of climate change on biodiversity are imminent, and these turn out to be particularly alarming for the tropical montane cloud forest. The disappearance of fragments of this forest is expected, along with some of their most characteristic species, such as Magnolia schiedeana. Mexico, through the National System of Protected Natural Areas, must consider protection strategies for those species distributed within the Protected Natural Areas that will be affected by the climate change. This study delimits the distribution of M. schiedeana in Mexico, through ecological niche and future distribution modeling under two periods: years 2040 and 2080. These distribution models tend to move towards northeastern Mexico. The potential distribution of this species declines by 0.36% and 1.94% in the first and second periods, respectively. From this result, the future role of National System of Protected Natural Areas in the long-term conservation of M. schiedeana was analyzed, prompting a proposal to focus conservation efforts on the following Protected Natural Areas: (1) At the federal level: Biosphere Reserve Sierra Gorda and Cañón de Metztitlán, Cuenca del Río Necaxa and El Potosí, National Park El Chico, Cofre de Perote, Pico de Orizaba and Los Mármoles, and Nevado de Toluca. (2) At the state level: Cerro de las Culebras, Cerro de la Galaxia, Cerro de Macuiltepetl, El Tejar Garnica, Francisco Javier Clavijero, La Martinica, Molino de San Roque, Pacho Nuevo and Predio Barragán.

Previous regional studies in Siberia have demonstrated climate warming and associated changes in distribution of vegetation and forest types, starting at the end of the 20th century. In this study we used two regional bioclimatic envelope models to simulate potential changes in forest types distribution and developed new regression models to simulate changes in stand height in tablelands and southern mountains of central Siberia under warming 21st century climate. Stand height models were based on forest inventory data (2850 plots). The forest type and stand height maps were superimposed to identify how heights would change in different forest types in future climates. Climate projections from the general circulation model Hadley HadCM3 for emission scenarios B1 and A2 for 2080s were paired with the regional bioclimatic models. Under the harsh A2 scenario, simulated changes included: a 80%–90% decrease in forest-tundra and tundra, a 30% decrease in forest area, a ∼400% increase in forest-steppe, and a 2200% increase in steppe, forest-steppe and steppe would cover 55% of central Siberia. Under sufficiently moist conditions, the southern and middle taiga were simulated to benefit from 21st century climate warming. Habitats suitable for highly-productive forests (≥30–40 m stand height) were simulated to increase at the expense of less productive forests (10–20 m). In response to the more extreme A2 climate the area of these highly-productive forests would increase 10%–25%. Stand height increases of 10 m were simulated over 35%–50% of the current forest area in central Siberia. In the extremely warm A2 climate scenario, the tall trees (25–30 m) would occur over 8%–12% of area in all forest types except forest-tundra by the end of the century. In forest-steppe, trees of 30–40 m may cover some 15% of the area under sufficient moisture.

Although the effects of climate change on biodiversity are increasingly evident by the shifts in species ranges across taxonomical groups, the underlying mechanisms affecting individual species are still poorly understood. The power of climate envelopes to predict future ranges has been seriously questioned in recent studies. Amongst others, an improved understanding of the effects of current weather on population trends is required. We analysed the relation between butterfly abundance and the weather experienced during the life cycle for successive years using data collected within the framework of the Dutch Butterfly Monitoring Scheme for 40 species over a 15-year period and corresponding climate data. Both average and extreme temperature and precipitation events were identified, and multiple regression was applied to explain annual changes in population indices. Significant weather effects were obtained for 39 species, with the most frequent effects associated with temperature. However, positive density-dependence suggested climatic independent trends in at least 12 species. Validation of the short-term predictions revealed a good potential for climate-based predictions of population trends in 20 species. Nevertheless, data from the warm and dry year of 2003 indicate that negative effects of climatic extremes are generally underestimated for habitat specialists in drought-susceptible habitats, whereas generalists remain unaffected. Further climatic warming is expected to influence the trends of 13 species, leading to an improvement for nine species, but a continued decline in the majority of species. Expectations from climate envelope models overestimate the positive effects of climate change in northwestern Europe. Our results underline the challenge to include population trends in predicting range shifts in response to climate change. [ABSTRACT FROM AUTHOR]

Given their physiological requirements, limited dispersal abilities, and hydrologically sensitive habitats, amphibians are likely to be highly sensitive to future climatic changes. We used three approaches to map areas in the western hemisphere where amphibians are particularly likely to be affected by climate change. First, we used bioclimatic models to project potential climate-driven shifts in the distribution of 413 amphibian species based on 20 climate simulations for 2071–2100. We summarized these projections to produce estimates of species turnover. Second, we mapped the distribution of 1099 species with restricted geographic ranges. Finally, using the 20 future climate-change simulations, we mapped areas that were consistently projected to receive less seasonal precipitation in the coming century and thus were likely to have altered microclimates and local hydrologies. Species turnover was projected to be highest in the Andes Mountains and parts of Central America and Mexico, where, on average, turnover rates exceeded 60% under the lower of two emissions scenarios. Many of the restricted-range species not included in our range-shift analyses were concentrated in parts of the Andes and Central America and in Brazil's Atlantic Forest. Much of Central America, southwestern North America, and parts of South America were consistently projected to experience decreased precipitation by the end of the century. Combining the results of the three analyses highlighted several areas in which amphibians are likely to be significantly affected by climate change for multiple reasons. Portions of southern Central America were simultaneously projected to experience high species turnover, have many additional restricted-range species, and were consistently projected to receive less precipitation. Together, our three analyses form one potential assessment of the geographic vulnerability of amphibians to climate change and as such provide broad-scale guidance for directing conservation efforts. [ABSTRACT FROM AUTHOR]

Aim We investigated whether accounting for land cover could improve bioclimatic models for eight species of anurans and three species of turtles at a regional scale. We then tested whether accounting for spatial autocorrelation could significantly improve bioclimatic models after statistically controlling for the effects of land cover. Location Nova Scotia, eastern Canada. Methods Species distribution data were taken from a recent (1999–2003) herpetofaunal atlas. Generalized linear models were used to relate the presence or absence of each species to climate and land-cover variables at a 10-km resolution. We then accounted for spatial autocorrelation using an autocovariate or third-order trend surface of the geographical coordinates of each grid square. Finally, variance partitioning was used to explore the independent and joint contributions of climate, land cover and spatial autocorrelation. Results The inclusion of land cover significantly increased the explanatory power of bioclimatic models for 10 of the 11 species. Furthermore, including land cover significantly increased predictive performance for eight of the 11 species. Accounting for spatial autocorrelation improved model fit for rare species but generally did not improve prediction success. Variance partitioning demonstrated that this lack of improvement was a result of the high correlation between climate and trend-surface variables. Main conclusions The results of this study suggest that accounting for the effects of land cover can significantly improve the explanatory and predictive power of bioclimatic models for anurans and turtles at a regional scale. We argue that the integration of climate and land-cover data is likely to produce more accurate spatial predictions of contemporary herpetofaunal diversity. However, the use of land-cover simulations in climate-induced range-shift projections introduces additional uncertainty into the predictions of bioclimatic models. Further research is therefore needed to determine whether accounting for the effects of land cover in range-shift projections is merited. [ABSTRACT FROM AUTHOR]

Aim To relate the dispersal limitation of endemic terrestrial mammals in Mexico to species life-history traits and latitude. Location Mexico. Methods We modelled species ecological niches projected as potential distributions ( P) using point occurrence data and 19 environmental variables for 89 endemic mammal species, and compared the areas covered by these ecological niche models with maps of species actual distributions ( R) based on minimum convex polygons connecting marginal records based on museum specimens. We correlated body mass, food habits (herbivore, omnivore, insectivore, frugivore/granivore), volant and non-volant (fossorial, arboreal, terrestrial) habits and mean latitude to the proportion of occupancy of species potential distributional areas ( R/ P). Results R and P were significantly positively correlated, with an overall average R/ P ratio of 0.49. Less than half of the endemics (41 species) had a high occupancy ( R/ P values ranging from 0.50 to 0.90); a few (four species) showed full occupancy (> 0.90). Body mass and food habits were not correlated with R/ P, but latitude showed significant correlations with R/ P; volant mammals tended to show higher R/ P values than non-volant mammals. Main conclusions Few species filled most of the spatial extent of their ecological niches. Life-history traits were generally poor predictors of proportional occupancy of species potential distributions. Endemics occurring at higher latitudes showed higher occupancy, suggesting that abiotic factors are likely to limit their distributions. Conversely, species at lower latitudes showed lower occupancy, suggesting that their distributions are limited by biotic factors and/or by geographical or historical barriers that prevent dispersal. The dispersal abilities of volant compared with non-volant endemics can explain the higher occupancy in species potential distributions in the former group. These trends provide a baseline for exploring the importance of life-history traits and abiotic versus biotic factors in limiting species distributions. [ABSTRACT FROM AUTHOR]

1. Species distribution models (SDM) are increasingly applied as predictive tools for purposes of conservation planning and management. Such models rely on the concept of the ecological niche and assume that distribution patterns of the modelled species are at some sort of equilibrium with the environment. This assumption contrasts with empirical evidence indicating that distribution patterns of many species are constrained by dispersal limitation. 2. We demonstrate that the performance of SDM based on presence-only data can be significantly enhanced by incorporating distance constraints (functions relating the likelihood of species’ occurrences at a site to the distance of the site from known presence locations) to the modelling procedure. This result is highly consistent for a variety of niche-based models (ENFA, DOMAIN and Mahalanobis distance), distance functions (nearest neighbour distance, cumulative distance and Gaussian filter) and taxonomic groups (plants, snails and birds, a total of 226 species). 3. Distance constraints are expected to enhance the accuracy of niche-based models even in the absence of strong dispersal limitation by accounting for mass effects and spatial autocorrelation in environmental factors for which data are not available. 4. While distance-based methods outperformed niche-based models when all data were used, their accuracy deteriorated sharply with smaller sample sizes. Niche-based methods are shown to cope better with small sample sizes than distance-based methods, demonstrating the potential advantage of niche-based models when calibration data are limited. 5. Synthesis and applications. Incorporating distance constraints in SDM provides a simple yet powerful method to account for spatial autocorrelation in patterns of species distribution, and is shown empirically to improve significantly the performance of such models. We therefore recommend incorporating distance constraints in future applications of SDM. [ABSTRACT FROM AUTHOR]

Aim Although the breeding ranges of most Western Palaearctic migratory passerines are well documented in Europe, their overwintering ranges and patterns of species richness in Africa remain poorly understood. To illustrate potential patterns of species richness despite severely limited data, we extrapolated species ranges from a new and unique data bank of locality records that documents overwintering locations of these birds in Africa. Location Sub-Saharan Africa. Methods We predicted potential geographical distributions of 60 species of passerine birds based on overwintering records using bioclimatic models. We then combined these predictions to estimate potential species richness and explored response shapes using spatial linear regression. We also evaluated the evidence for a mid-domain effect using a one-dimensional null model. Results Spatial linear regression analyses of the species richness pattern revealed non-linear relationships to seasonality in precipitation, minimum net primary productivity, minimum average temperature, habitat heterogeneity, percentage of tree cover, distance from the Sahara Desert and inter-annual variability in net primary productivity. The explanatory power of these variables decreased with geographic range size. The one-dimensional null model of species richness based on distance from the Sahara Desert did not show evidence of a mid-domain effect. Main conclusions Distributions of migrants seem generally strongly determined by distance from the Sahara Desert working in concert with climatic effects, but this cannot adequately explain richness patterns of species with small ranges in Africa, many of which are of substantial conservation concern. [ABSTRACT FROM AUTHOR]

Aim We explored the importance of climate and land cover in bird species distribution models on multiple spatial scales. In particular, we tested whether the integration of land cover data improves the performance of pure bioclimatic models. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were employed in the analyses. Land cover and climatic variables were compiled using the same grid system. The dependent and explanatory variables were resampled to 20-km, 40-km and 80-km resolutions. Generalized additive models (GAM) were constructed for each of the 88 land bird species studied in order to estimate the probability of occurrence as a function of (1) climate and (2) climate and land cover variables. Model accuracy was measured by a cross-validation approach using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Results In general, the accuracies of the 88 bird–climate models were good at all studied resolutions. However, the inclusion of land cover increased the performance of 79 and 78 of the 88 bioclimatic models at 10-km and 20-km resolutions, respectively. There was no significant improvement at the 40-km resolution. In contrast to the finer resolutions, the inclusion of land cover variables decreased the modelling accuracy at 80km resolution. Main conclusions Our results suggest that the determinants of bird species distributions are hierarchically structured: climatic variables are large-scale determinants, followed by land cover at finer resolutions. The majority of the land bird species in Finland are rather clearly correlated with climate, and bioclimate envelope models can provide useful tools for identifying the relationships between these species and the environment at resolutions ranging from 10 km to 80 km. However, the notable contribution of land cover to the accuracy of bioclimatic models at 10–20-km resolutions indicates that the integration of climate and land cover information can improve our understanding and model predictions of biogeographical patterns under global change. [ABSTRACT FROM AUTHOR]

The Random Forests multiple-regression tree was used to model climate profiles of 25 biotic communities of the western United States and nine of their constituent species. Analyses of the communities were based on a gridded sample of ca. 140,000 points, while those for the species used presence-absence data from ca. 120,000 locations. Independent variables included 35 simple expressions of temperature and precipitation and their interactions. Classification errors for community models averaged 19%, but the errors were reduced by half when adjusted for misalignment between geographic data sets. Errors of omission for species-specific models approached 0, while errors of commission were less than 9%. Mapped climate profiles of the species were in solid agreement with range maps. Climate variables of most importance for segregating the communities were those that generally differentiate maritime, continental, and monsoonal climates, while those of importance for predicting the occurrence of species varied among species but consistently implicated the periodicity of precipitation and temperature-precipitation interactions. Projections showed that unmitigated global warming should increase the abundance primarily of the montane forest and grassland community profiles at the expense largely of those of the subalpine, alpine, and tundra communities but also that of the arid woodlands. However, the climate of 47% of the future landscape may be extramural to contemporary community profiles. Effects projected on the spatial distribution of species-specific profiles were varied, but shifts in space and altitude would be extensive. Species-specific projections were not necessarily consistent with those of their communities. [ABSTRACT FROM AUTHOR]

Modelling of climate change-induced species range shifts has generally addressed migration limitations inadequately, often assuming ‘null’ migration or instantaneous ‘full’ migration extremes. We describe methods for incorporating simple migration rate assumptions into multispecies modelling, using the Proteaceae of the Cape Floristic Region. Even with optimistic migration assumptions, range loss projections more closely approximate null migration than full migration assumptions. Full migration results were positively skewed by few species with large range increases, an overestimate eliminated by dispersal-limited migration rate assumptions. Wind- and ant/rodent-dispersed species responded differently to climate change. Initially larger ranges of wind-dispersed species were more strongly reduced by climate change, despite far greater assumed dispersal distances — we suggest that these well-dispersed species populate more marginal areas of potential range, causing lower resilience to climatic changes at range margins. Overall, range loss rate slowed with advancing climate change, possibly because species ranges contracted into core areas most resilient to climate change. Thus, a consideration of simple dynamics of range change (rather than single step, present–future comparisons of range) provide new insights relevant for conservation strategies, in particular, and for guiding monitoring efforts to detect and gauge the impacts of climate change on natural populations. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim We explored the effects of prevalence, latitudinal range and spatial autocorrelation of species distribution patterns on the accuracy of bioclimate envelope models of butterflies. Location Finland, northern Europe. Methods The data of a national butterfly atlas survey (NAFI) carried out in 1991–2003 with a resolution of 10 × 10 km were used in the analyses. Generalized additive models (GAM) were constructed, for each of 98 species, to estimate the probability of occurrence as a function of climate variables. Model performance was measured using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were related to the species’ geographical attributes using multivariate GAM. Results Accuracies of the climate–butterfly models varied from low to very high (AUC values 0.59–0.99), with a mean of 0.79. The modelling performance was related negatively to the latitudinal range and prevalence, and positively to the spatial autocorrelation of the species distribution. These three factors accounted for 75.2% of the variation in the modelling accuracy. Species at the margin of their range or with low prevalence were better predicted than widespread species, and species with clumped distributions better than scattered dispersed species. Main conclusions The results from this study indicate that species’ geographical attributes highly influence the behaviour and uncertainty of species–climate models, which should be taken into account in biogeographical modelling studies and assessments of climate change impacts. [ABSTRACT FROM AUTHOR]

To assess the influence of land cover and climate on species distributions across Europe. To quantify the importance of land cover to describe and predict species distributions after using climate as the main driver. The study area is Europe. (1) A multivariate analysis was applied to describe land-cover distribution across Europe and assess if the land cover is determined by climate at large spatial scales. (2) To evaluate the importance of land cover to predict species distributions, we implemented a spatially explicit iterative procedure to predict species distributions of plants (2603 species), mammals (186 species), breeding birds (440 species), amphibian and reptiles (143 species). First, we ran bioclimatic models using stepwise generalized additive models using bioclimatic variables. Secondly, we carried out a regression of land cover (LC) variables against residuals from the bioclimatic models to select the most relevant LC variables. Finally, we produced mixed models including climatic variables and those LC variables selected as decreasing the residual of bioclimatic models. Then we compared the explanatory and predictive power of the pure bioclimatic against the mixed model. (1) At the European coarse resolution, land cover is mainly driven by climate. Two bioclimatic axes representing a gradient of temperature and a gradient of precipitation explained most variation of land-cover distribution. (2) The inclusion of land cover improved significantly the explanatory power of bioclimatic models and the most relevant variables across groups were those not explained or poorly explained by climate. However, the predictive power of bioclimatic model was not improved by the inclusion of LC variables in the iterative model selection process. Climate is the major driver of both species and land-cover distributions over Europe. Yet, LC variables that are not explained or weakly associated with climate (inland water, sea or arable land) are interesting to describe particular mammal, bird and tree distributions. However, the addition of LC variables to pure bioclimatic models does not improve their predictive accuracy. [ABSTRACT FROM AUTHOR]

Research highlights: We investigated bioclimatic relationships between Scots pine seed mass and seed zones/climatypes across its range in Russia using extensive published data to predict seed zones and seed mass distributions in a changing climate and to reveal ecological and genetic components in the seed mass variation using our 40-year common garden trial data. Introduction: seed productivity issues of the major Siberian conifers in Asian Russia become especially relevant nowadays in order to compensate for significant forest losses due to various disturbances during the 20th and current centuries. Our goals were to construct bioclimatic models that predict the seed mass of major Siberian conifers (Scots pine, one of the major Siberian conifers) in a warming climate during the current century. Methods: Multi-year seed mass data were derived from the literature and were collected during field work. Climate data (January and July data and annual precipitation) were derived from published reference books on climate and climatic websites. Our multiple regression bioclimatic models were constructed based on the climatic indices of growing degree days > 5 °C, negative degree days < 0 °C, and annual moisture index, which were calculated from January and July temperatures and annual precipitation for both contemporary and future climates. The future 2080 (2070–2100) January and July temperatures and annual precipitation anomalies were derived from the ensemble of twenty CMIP5 (the Coupled Model Intercomparison Project, Phase 5) global circulation models (GCMs) and two scenarios using a mild RCP (Representative Concentration Pathway) 2.6 scenario and an extreme RCP 8.5 scenario. Results: Site climate explained about 70% of the seed mass variation across the Scots pine range. Genetic components explained 30% of the seed mass variation, as per the results from our common garden experiment in south central Siberia. Seed mass varied within 3.5 g (min) and 10.5 g (max) with the mean 6.1 g (n = 1150) across Russia. Our bioclimatic seed mass model predicted that a July temperature elevated by 1 °C increased seed mass by 0.56 g, and a January temperature elevated by 5 °C increased seed mass by 0.43 g. The seed mass would increase from 1 g to 4 g in the moderate RCP 2.6 and the extreme RCP 8.5 climates, respectively. Predicted seed zones with heavier seed would shift northwards in a warming climate. However, the permafrost border would halt this shifting due to slower permafrost thawing; thus, our predicted potential for Scots pine seed zones and seed mass would not be realized in the permafrost zone in a warmed climate. Our common garden experiment in central Siberia showed that trees of northerly origins produced lighter seeds than local trees but heavier ones than the trees at the original site. Trees of southerly origins produced heavier seeds than local trees but lighter seeds than the trees at the original site. Conclusions: The findings from this study could serve as blueprints for predicting new landscapes with climatic optima for Pinus sylvestris to produce better quality seeds to adjust to a warming climate. [ABSTRACT FROM AUTHOR]

SummaryAims (1) To define the physical correlates of indigenous forest in KwaZulu-Natal province and develop a model, based on climatic parameters, to predict the potential distribution of forest subtypes in the province. (2) To explore the impact of palaeoclimatic change on forest distribution, providing an insight into the regional-scale/historical forces shaping the pattern and composition of present-day forest communities. (3) To investigate potential future shifts in forest distribution associated with projected climate change. Location KwaZulu-Natal province, South Africa. Methods A BIOCLIM-type approach is adopted. Bioclimatic ‘profiles’ for eight different forest subtypes are defined from a series of grid overlays of current forest distribution against nineteen climatic and geographical variables, using ArcInfo GIS grid-based processing. A principal components analysis is performed on a selection of individual forests to identify those variables most significant in distinguishing different forest subtypes. Five models are developed to predict the distribution of forest subtypes from their bioclimatic profiles. Maps of the potential distribution of forest subtypes predicted by these models under current climatic conditions are produced, and model accuracy assessed. One model is applied to two palaeoclimatic scenarios, the Last Glacial Maximum (LGM) (≈18,000 BP) and the Holocene altithermal (≈7000 BP), and to projected future climate under a doubling in global atmospheric carbon dioxide. Results Seven variables; altitude, mean annual temperature, annual rainfall range, potential evaporation, annual temperature range, mean annual precipitation and mean winter rainfall, are most important in distinguishing different forest subtypes. Under the most accurate model, the potential present-day distribution of all forest subtypes is more extensive than is actually observed, but is supported by recent historical evidence. During the LGM, Afromontane forest occupied a much reduced and highly fragmented area in the mid-altitude region currently occupied by scarp forest. During the Holocene altithermal, forest expanded in area, with a mixing of Afromontane and Indian Ocean coastal belt forest elements along the present-day scarp forest belt. Under projected climatic conditions, forest shifts in altitude and latitude and occupies an area similar to its current potential and more extensive than its actual current distribution. Main conclusions Biogeographical history and present physical diversity play a major role in the evolution and persistence of the diversity of forest in KwaZulu-Natal. It is important to adopt a long-term and regional perspective to forest ecology, biogeography, conservation and management. The area and altitudinal and latitudinal distribution of forest subtypes show considerable sensitivity to climate change. The isolation of forest by anthropogenic landscape change has limited its radiation potential and ability to track environmental change. Long-term forest preservation requires reserves in climatically stable areas, or spanning altitudinal or latitudinal gradients allowing for forest migration, along with innovative matrix management strategies. Dune, sand, swamp, riverine and lowland forest subtypes are most at risk. Scarp forests are highlighted as former refugia and important for the future conservation of forest biodiversity. [ABSTRACT FROM AUTHOR]

Focuses on the threats to ecosystems and biodiversity in the wet tropics bioregion of Australia. Impact of climate change on biodiversity conservation in the region; Discussion on global warming; Information on the reduction in the magnitude of climate change through mitigation.