Climatic Control on Forests and Tree Species Distribution in the Forest Region of Northeast China

North-east （NE） China covers considerable climatic gradients and all major forests types of NE Asia. in the present study, 10 major forest types across the forest region of NE China were sampled to Investigate forest distribution in relation to climate. Canonical correspondence analysis （CCA） revealed that growing season precipitation and energy availability were primary climatic factors for the overall forest pattern of NE China, accounting for 66% of the explanatory power of CCA. Conversely, annual precipitation and winter coldness had minor effects. Generalized additive models revealed that tree species responded to climatic gradients differently and showed three types of response curve： （i） monotonous decline; （ii） monotonous Increase; and （iii） a unimodai pattern. Furthermore, tree species showed remarkable differences in limiting climatic factors for their distribution. The power of climate in explaining species distribution declined significantly with decreasing species dominance, suggesting that the distribution of dominant species was primarily controlled by climate, whereas that of subordinate species was more affected by competition from other species.     

Climatic controls on distribution of Fagus crenata forests in Japan

Abstract. We used classification tree analysis to develop a climate‐based distribution model for Fagus crenata forests in Japan. Four climatic variables judged likely to affect the distribution of the species (summer and winter precipitation, minimum temperature of the coldest month and Kira's warmth index) were chosen as independent variables for the model. Latitudinal and longitudinal information was also used to examine effects of spatial autocorrelation on the model. The climatic factors associated with the distribution of the forests were analysed using a classification tree to devise prediction rules. Predicted areas of high probability for forest occurrence lay mainly on the Sea of Japan side of northern Honshu and southern Hokkaido. This is consistent with actual forest distribution. Some areas with high predicted probabilities of F. crenata forest occurrence were beyond the current natural northern range limits of these forests. Since these areas were widely scattered, it was assumed that the species has been hindered from colonizing them due to dispersal limitations. Deviance‐weighted scores, used to compare magnitudes of the contributions of predictor variables, revealed winter precipitation as the most influential factor, followed by the warmth index, the minimum temperature of the coldest month and summer precipitation. Attempts were made to generate ecological explanations for the effects of the four climatic factors on the distribution of F. crenata forests.     

Understorey vegetation in boreal Picea mariana and Populus tremuloides stands in British Columbia

Abstract. We compared the species composition and species density of vascular plants in the understorey vegetation of boreal forest between Picea mariana (Black spruce) and Populus tremuloides (Trembling aspen) stands in British Columbia, Canada, and related differences in species composition and species density between the two forest types to dominant canopy tree species as well as a wide variety of environmental factors. We analysed 231 stands, distributed in three different climatic regions representing drier, wetter, and milder variations of montane boreal climate. Of these stands 118 were dominated by P. mariana and 113 by P. tremuloides. P. tremuloides stands had higher species density than P. mariana stands in all climatic regions, but species density of each dominance type varied among climatic regions. The floristic composition of the understorey vegetation was markedly different for P. mariana and P. tremuloides dominated stands. A detailed study on the effect of canopy dominance and local environmental factors on the understorey vegetation of the boreal forest was conducted using 88 stands from one of the three climatic regions. Using a combination of ordination and variation partitioning by constrained ordination we demonstrated a small but unique effect of canopy dominance type on the understorey vegetation, while a larger amount of compositional variation was shared with other factors. Our results accord with a scenario in which differences in primary environmental factors and humus form properties, the latter accentuated by the canopy dominants themselves, are the most important causes of higher species density in P. tremuloides stands than in P. mariana stands, as well as differences in species composition among the two canopy dominance types. Processes and time scales involved in the small but significant direct and indirect effects of the canopy dominant on understo‐ rey species composition are discussed.     

Climatic and biophysical controls on conifer species distributions in mountain forests of Washington State, USA

Abstract Aim The purpose of this study was to quantify relationships between conifer species distributions and climatic and biophysical variables, in order to provide better insight into the potential for redistribution of species on the landscape in response to climatic change. Location Data are from 10,653 georeferenced sites in Washington State, USA, along a longitudinal gradient from west of the crest of the Cascade Range to the beginnings of the western slope of the Rocky Mountains, and across two physiographic provinces, the Northern Cascades, characterized by steep, rugged topography, and the Okanogan Highlands, presenting moderate slopes and broad rounded summits. Methods Tree data were drawn from the USDA Forest Service Area Ecology Program database, collected in mature, undisturbed stands. We compared simple climatic variables (annual temperature, growing‐degree days, annual and seasonal precipitation) to biophysical variables (soil, hydrologic, and solar radiation) derived from climatic variables. Climatic and biophysical variables were taken from the output of climatological and hydrological simulation models and estimated for each plot in the tree database. Generalized linear models were used, for each of fourteen tree species, at multiple spatial extents, to estimate the probability of occurrence of that species as a function of climatic and biophysical predictors. Models were validated by a combination of bootstrapping and estimating receiver operating characteristic (ROC) curves. Results For the majority of species, we were able to fit variables representing both moisture and temperature gradients, and in all but a few cases these models identified a unimodal response of species occurrence to these gradients. In some cases the ecological/environmental niche of a species had been clearly captured by the model, whereas in others a longer gradient in the predictor variable(s) would be needed. Responses of most species were consistent across three spatial scales. Main conclusions By identifying the ecological niches of multiple species, we can forecast their redistribution on the landscape in response to climatic change, evaluate the predictions of simulation models, and alert managers to particularly sensitive or vulnerable ecosystems and landscapes.     

Forest pattern,climate and vulcanism in central North Island,New Zealand

Abstract. A quantitative study of relationships between forest pattern and environment in the central North Island, New Zealand, is based on forest composition data from ca. 2000 existing plots distributed throughout the forests of the region. Estimates of mean annual temperature, rainfall, and solar radiation are derived for each plot from mathematical surfaces fitted to climate station data. Estimates of the depth of the last major rhyolitic eruption, (Taupo Pumice, ca. 130 AD) are derived from isopach maps. A classification procedure is used to identify broad compositional groups. Generalised linear models are used to examine relationships between major species and climatic and other physical factors. Significant relationships are identified between the distributions of both plot groups and species, and climate, vulcanism, topography and drainage. Among these factors, temperature and/or solar radiation are indicated as major determinants of the regional forest pattern, with rainfall, topography, and drainage acting at a secondary level. The role of the Taupo Pumice eruption is more difficult to interpret, and its effects seem to have been greatly influenced by topography. Deep extensive deposits of tephra on flat-to-rolling sites close to the eruption centre have probably favoured the current dominance of these sites by more rapidly dispersing conifers. In contrast, on adjacent steep sites where forest destruction was likely to be less severe, slow-dispersing Nothofagus species are largely dominant. Further work is needed to understand the factors favouring conifer dominance of the central basins and the degree to which Nothofagus species might expand their range in the future.     

Dendroclimatic signals deduced from riparian versus upland forest interior pines in North Karelia, Finland

Radial growth of boreal tree species is only rarely studied in riparian habitats. Here we investigated chronologies of earlywood, latewood, and annual ring widths and blue intensity (BI; a surrogate to latewood density) from riparian lake shore and upland forest interior pines (Pinus sylvestris L.) growing in boreal forest in eastern Finland. Riparian and upland chronologies were compared to examine differences in the pine growth variability and growth response to climatic variation in the two habitats. It was found that the climatic variables showing statistically significant correlations with the tree-ring chronologies were related to snow conditions at the start of the growing season. Deeper snowpack led to reduced upland pine growth, possibly due to delayed snowmelt and thus postponed onset of the growing season. Warm late winters were followed by increased riparian pine growth because of earlier start of the snow-melt season and thus a lower maximum early summer lake level. Moreover, riparian pines reacted negatively to increased rainfall in June, whereas the upland pines showed a positive response. Latewood growth reacted significantly to summer temperatures. The BI chronology showed a strong correlation with warm-season temperatures, indicating an encouraging possibility of summer temperature reconstruction using middle/south boreal pine tree-ring archives.     

Assessing forest growth across southwestern Oregon under a range of current and future global change scenarios using a process model, 3‐PG

With improvements in mapping regional distributions of vegetation using satellite‐derived information, there is an increasing interest in the assessment of current limitations on forest growth and in making projections of how productivity may be altered in response to changing climatic conditions and management policies. We utilised a simplified physiologically based process model (3‐PG) across a 54 000 km² mountainous region of southwestern Oregon, USA, to evaluate the degree to which maximum periodic mean annual increment (PAI) of forests could be predicted at a set of 448 forest inventory plots. The survey data were pooled into six broad forest types (coastal rain forest, interior coast range forest, mixed conifer, dry‐site Douglas‐fir, subalpine forest, and pine forest) and compared to the 3‐PG predictions at a spatial resolution of 1 km². We found good agreement (r² = 0.84) between mean PAI values of forest productivity for the six forest types with those obtained from field surveys. With confidence at this broader level of integration, we then ran model simulations to evaluate the constraints imposed by (i) soil fertility under current climatic conditions, (ii) the effect of doubling monthly precipitation across the region, and (iii) a widely used climatic change scenario that involves modifications in monthly mean temperatures and precipitation, as well as a doubling in atmospheric CO₂ concentrations. These analyses showed that optimum soil fertility would more than double growth, with the greatest response in the subalpine type and the least increase in the coastal rain forests. Doubling the precipitation increased productivity in the pine type (> 50%) with reduced responses elsewhere. The climate change scenario with doubled atmospheric CO₂ increased growth by 50% on average across all forest types, primarily as a result of a projected 33% increase in photosynthetic capacity. This modelling exercise indicates that, at a regional scale, a general relationship exists between simulated maximum leaf area index and maximum aboveground growth, supporting the contention that satellite‐derived estimates of leaf area index may be good measures of the potential productivity of temperate evergreen forests.     

Climate and vegetation hierarchically structure patterns of songbird distribution in the Canadian boreal region

Environmental factors controlling the distribution and abundance of boreal avifauna are not fully understood, limiting our ability to predict the consequences of a changing climate and industrial development activities underway. We used a compilation of avian point‐count data, collected over 1990–2008 from nearly 36 000 locations, to model the abundance of individual forest songbird species within the Canadian boreal forest. We evaluated 30 vegetation and 101 climatic variables, representing most of the widely‐used dimensions of climate space, along with less usual measures of inter‐annual variability. Regression tree models allowed us to calculate the relative importance of climate and vegetation variable classes according to avian migration strategy without the need for a priori variable selection or dimension reduction. We tested for hierarchical habitat selection by formulating hypotheses on the locations of variables within the model tree structures. Climate variables explained the majority (77%) of deviance explained over 98 species modelled. As may be expected at high latitudes, we found energy availability (temperature, 65%) to be more important than moisture availability (precipitation, 12%). The contributions of inter‐ and intra‐annual climate variability (28%) were about half that of mean conditions. The relatively large contribution of remotely‐sensed vegetation metrics (23%) highlighted the importance of local vegetation heterogeneity controlled by non‐climatic factors. The two most important vegetation variables were landcover type and April leaf area index. When selected, these generally occurred in a model's right subtree, consistent with predictions from hierarchical habitat selection theory. When occupying the root node, landcover effectively delineated the historical forest‐prairie ecotone, reflecting the current disequilibrium between climate and vegetation due to human land use. Our findings suggest a large potential for avian distributional shifts in response to climate change, but also demonstrate the importance of finer scale vegetation heterogeneity in the spatial distribution of boreal birds.     

A PCA-based modelling technique for predicting environmental suitability for organisms from presence records

We present a correlative modelling technique that uses locality records (associated with species presence) and a set of predictor variables to produce a statistically justifiable probability response surface for a target species. The probability response surface indicates the suitability of each grid cell in a map for the target species in terms of the suite of predictor variables. The technique constructs a hyperspace for the target species using principal component axes derived from a principal components analysis performed on a training dataset. The training dataset comprises the values of the predictor variables associated with the localities where the species has been recorded as present. The origin of this hyperspace is taken to characterize the centre of the niche of the organism. All the localities (grid-cells) in the map region are then fitted into this hyperspace using the values of the predictor variables at these localities (the prediction dataset). The Euclidean distance from any locality to the origin of the hyperspace gives a measure of the 'centrality' of that locality in the hyperspace. These distances are used to derive probability values for each grid cell in the map region. The modelling technique was applied to bioclimatic data to predict bioclimatic suitability for three alien invasive plant species ( Lantana camara L., Ricinus communis L. and Solanum mauritianum Scop.) in South Africa, Lesotho and Swaziland. The models were tested against independent test records by calculating area under the curve (AUC) values of receiver operator characteristic (ROC) curves and kappa statistics. There was good agreement between the models and the independent test records. The pre-processing of climatic variable data to reduce the deleterious effects of multicollinearity, and the use of stopping rules to prevent overfitting of the models are important aspects of the modelling process.     

Climatic effects on litter decomposition from arctic tundra to tropical rainforest

Climatic effects on the decomposition rates of various litter types in different environments must be known to predict how climatic changes would affect key functions of terrestrial ecosystems, such as nutrient and carbon cycling and plant growth. We developed regression models of the climatic effects on the first‐year mass loss of Scots pine needle litter in boreal and temperate forests across Europe (34 sites), and tested the applicability of these models for other litter types in different ecosystems from arctic tundra to tropical rainforest in Canada (average three year mass loss of 11 litter types at 18 sites), the USA and Central America (four litter types at 26 sites). A temperature variable (annual mean temperature, effective temperature sum or its logarithm) combined with a summer drought indicator (precipitation minus potential evapotranspiration between May and September) explained the first‐year mass loss of the Scots pine needle litter across Europe with a higher R² value than actual evapotranspiration (0.68–0.74 vs. 0.51) and with less systematic error for any sub‐region. The model with temperature sum and the summer drought indicator appeared best suited to the other litter types and environments. It predicted the climatic effects on the decomposition rates in North and Central America with least systematic error and highest R² values (0.72–0.80). Compared with Europe, the decomposition rate was significantly less sensitive to annual mean temperature in Canada, and to changes in actual evapotranspiration in the USA and Central America. A simple model distinguishing temperature and drought effects was able to explain the majority of climatic effects on the decomposition rates of the various litter types tested in the varying environments over the large geographical areas. Actual evapotranspiration summarizing the temperature and drought effects was not as general climatic predictor of the decomposition rate.     

中国北方林生产力变化趋势及其影响因子分析

森林生产力是反映森林固碳能力的重要指标,是进行碳循环研究的重要环节。用模拟生态系统生物地球化学循环的CENTURY模型,模拟中国北方林(兴安落叶松林)近35a来的生产力动态,用3种趋势分析方法,检验了其变化趋势,并用多元线性回归模型分析了中国北方林生产力的年际波动与气温降水年际波动的关系,以及气温和降水对我国北方林生产力的影响程度。结果表明：中国北方林生产力呈增加的趋势,平均年增长率为0．34％;气温与森林生产力呈显著负相关,对森林生产力的贡献因子为4．0977;降水与森林生产力呈弱的正相关,其对森林生产力的贡献因子为0．3902。从而说明近35a来森林生产力的增加除了受气温降水等非生物因素的影响外,还受其它因素的影响;另外说明以气候变暖为标志的全球变化会对森林生产力产生重要的影响。     

Potential effects of interaction between CO2 and temperature on forest landscape response to global warming

Projected temperature increases under global warming could benefit southern tree species by providing them the optimal growing temperature and could be detrimental to northern species by exposing them to the supra optimal growing temperatures. This benefit-detriment trade-off could increase the competitive advantage of southern species in the northern species range and cause the increase or even dominance of southern species in the northern domain. However, the optimum temperature for photosynthesis of C3 plants may increase due to CO₂ enrichment. An increase in the optimum temperature could greatly reduce the benefit-detriment effect. In this study, we coupled a forest ecosystem process model (PnET-II) and a forest GAP model (LINKAGES) with a spatially dynamic forest landscape model (LANDIS-II) to study how an optimum temperature increase could affect forest landscape response due to global warming. We simulated 360 years of forest landscape change in the Boundary Water Canoe Area (BWCA) in northern Minnesota, which is transitional between boreal and temperate forest. Our results showed that, under the control scenario of continuing the historic 1984–1993 mean climate (mainly temperature, precipitation and CO₂), the BWCA will become a spruce-fir dominated boreal forest. However, under the scenario of predicted climatic change [the 2000–2099 climates are predicted by Canadian Climate Center (CCC), followed by 200 years of continuing the predicted 2090–2099 mean climate], the BWCA will become a pine-dominated mixed forest. If the optimum temperature increases gradually with [CO₂] (the increase in optimum temperature is assumed to change gradually from 0 °C in year 2000 to 5 °C in year 2099 when [CO₂] reaches 711 ppm and stabilizes at 5 °C after year 2099), the BWCA would remain a fir-dominated boreal forest in areas with relatively high water-holding capacity, but not in areas with relatively low water-holding capacity. Our results suggest that the [CO₂] induced increases in optimum temperature could substantially reduce forest landscape change caused by global warming. However, not all tree species would be able to successfully adapt to future warming as predicted by CCC, regardless of optimum temperature acclimations.     

An ecoregion assessment of projected tree species vulnerabilities in western North America through the 21st century

Forest ecosystems across western North America will likely see shifts in both tree species dominance and composition over the rest of this century in response to climate change. Our objective in this study was to identify which ecological regions might expect the greatest changes to occur. We used the process‐based growth model 3‐PG, to provide estimates of tree species responses to changes in environmental conditions and to evaluate the extent that species are resilient to shifts in climate over the rest of this century. We assessed the vulnerability of 20 tree species in western North America using the Canadian global circulation model under three different emission scenarios. We provided detailed projections of species shifts by including soil maps that account for the spatial variation in soil water availability and soil fertility as well as by utilizing annual climate projections of monthly changes in air temperature, precipitation, solar radiation, vapor pressure deficit and frost at a spatial resolution of one km. Projected suitable areas for tree species were compared to their current ranges based on observations at >40 000 field survey plots. Tree species were classified as vulnerable if environmental conditions projected in the future appear outside that of their current distribution ≥70% of the time. We added a migration constraint that limits species dispersal to <200 m yr^?1 to provide more realistic projections on species distributions. Based on these combinations of constraints, we predicted the greatest changes in the distribution of dominant tree species to occur within the Northwest Forested Mountains and the highest number of tree species stressed will likely be in the North American Deserts. Projected climatic changes appear especially unfavorable for species in the subalpine zone, where major shifts in composition may lead to the emergence of new forest types.     

中国森林生产力对气候变化响应的预测研究

根据不同地理区森林生产力和气候环境变量的数据构建了中国林林气候生产力模型,以此为基础研究了气候变化对中国森要生产力的影响。结果表明：在所构建的模型中,除海拔高度与净生产力的相关模型外,其它模型均有较高的实用价值,模型的似合曲线变化,基本反映了中国森林现实力的地理分布格局;中国森林生产力的分布格局主要取决于气候环境中的水热条件,水分条件是决定中国大部分地区森林生产力水平和地理分布格局的主导因素;根据     

Perennial polypores as indicators of annual and red-listed polypores

Many polypores are specialized in their requirements for substrate and environment, and they have been suggested to indicate the continuity of coarse woody debris or naturalness of a forest stand. However, the use of polypores as indicators of conservation value is restricted by the temporally limited appearance of annual fruit bodies. We studied whether the species richness of perennial polypores (perennials) can be used to predict the species richness of annual or annual red-listed polypores (annuals). Our data included 1471 separate datasets (sample plots or larger inventoried areas) in different parts of Finland and Russian Karelia, ranging from the southern to northern boreal zone. At the large scale (the whole area) the number of perennials explained about 70% of the variation in the number of annuals, and about 67% in the number of red-listed annuals. A minimum set of 40–60 perennial occurrences gave a reliable estimate on the species richness of annuals, and 60–80 occurrences on the species richness of red-listed annuals. The richness of perennials predicted the richness of annuals and, in particular, richness of red-listed annuals, better than the size of inventoried area. According to our results, perennial polypores can be used as a surrogate for overall polypore species richness in natural and seminatural boreal forests, but the predictive power is weaker in managed forests. In addition, the relationship between the perennial and annual species seems to differ in different vegetation zones, management types and forest types. Due to this variation direct application of the indicator values derived from different vegetation zones and management or forest types are not recommended. Since perennials are easier to identify than annuals, detectable throughout the year, and have much smaller year-to-year variation, their use as an indicator group seems to offer advantages regarding the timing and cost-efficiency of inventories.     

Mountain landscapes offer few opportunities for high‐elevation tree species migration

Climate change is anticipated to alter plant species distributions. Regional context, notably the spatial complexity of climatic gradients, may influence species migration potential. While high‐elevation species may benefit from steep climate gradients in mountain regions, their persistence may be threatened by limited suitable habitat as land area decreases with elevation. To untangle these apparently contradictory predictions for mountainous regions, we evaluated the climatic suitability of four coniferous forest tree species of the western United States based on species distribution modeling (SDM) and examined changes in climatically suitable areas under predicted climate change. We used forest structural information relating to tree species dominance, productivity, and demography from an extensive forest inventory system to assess the strength of inferences made with a SDM approach. We found that tree species dominance, productivity, and recruitment were highest where climatic suitability (i.e., probability of species occurrence under certain climate conditions) was high, supporting the use of predicted climatic suitability in examining species risk to climate change. By predicting changes in climatic suitability over the next century, we found that climatic suitability will likely decline, both in areas currently occupied by each tree species and in nearby unoccupied areas to which species might migrate in the future. These trends were most dramatic for high elevation species. Climatic changes predicted over the next century will dramatically reduce climatically suitable areas for high‐elevation tree species while a lower elevation species, Pinus ponderosa, will be well positioned to shift upslope across the region. Reductions in suitable area for high‐elevation species imply that even unlimited migration would be insufficient to offset predicted habitat loss, underscoring the vulnerability of these high‐elevation species to climatic changes.     

Quantitative prediction of the distribution and abundance of Vaccinium myrtillus with climatic and edaphic factors

Question: Can the distribution and abundance of Vaccinium myrtillus be reasonably predicted with soil nutritional and climatic factors? Location: Forests of France. Methods: We used Braun‐Blanquet abundance/dominance information for Vaccinium myrtillus on 2905 forest sites extracted from the phyto‐ecological database EcoPlant, to characterize the species ecological response to climatic and edaphic factors and to predict its cover/abundance at the national scale. The link between cover/abundance of the species and climatic (65 monthly and annual predictors concerning temperature, precipitation, radiation, potential evapotranspiration, water balance) and edaphic (two predictors: soil pH and C:N ratio) factors was investigated with proportional odds models. We evaluated the quality of our model with 9830 independent relevés extracted from Sophy, a large phytosociological database for France. Results: In France, Vaccinium myrtillus is at the southern limit of its European geographic range and three environmental factors (mean annual temperature, soil pH and C:N ratio) allow prediction of its distribution and abundance in forests with high success rates. The species reveals a preference for colder sites (especially mountains) and nutritionally poor soils (low pH and high C:N ratio). A predictive map of its geographic range reveals that the main potential habitats are mountains and northwestern France. The potential habitats with maximal expected abundance are the Vosges and the Massif central mountains, which are both acidic mountains. Conclusions: Complete niche models including climate and soil nutritional conditions allow an improvement of the spatial prediction of plant species abundance at a broad scale. The use of soil nutritional variables in distribution models further leads to an improvement in the prediction of plant species habitats within their geographical range.     

Geographical and climatic gradients of evergreen versus deciduous broad‐leaved tree species in subtropical China: Implications for the definition of the mixed forest

Understanding climatic influences on the proportion of evergreen versus deciduous broad‐leaved tree species in forests is of crucial importance when predicting the impact of climate change on broad‐leaved forests. Here, we quantified the geographical distribution of evergreen versus deciduous broad‐leaved tree species in subtropical China. The Relative Importance Value index (RIV ) was used to examine regional patterns in tree species dominance and was related to three key climatic variables: mean annual temperature (MAT ), minimum temperature of the coldest month (MinT), and mean annual precipitation (MAP ). We found the RIV of evergreen species to decrease with latitude at a lapse rate of 10% per degree between 23.5 and 25°N, 1% per degree at 25–29.1°N, and 15% per degree at 29.1–34°N. The RIV of evergreen species increased with: MinT at a lapse rate of 10% per °C between ?4.5 and 2.5°C and 2% per °C at 2.5–10.5°C; MAP at a lapse rate of 10% per 100 mm between 900 and 1,600 mm and 4% per 100 mm between 1,600 and 2,250 mm. All selected climatic variables cumulatively explained 71% of the geographical variation in dominance of evergreen and deciduous broad‐leaved tree species and the climatic variables, ranked in order of decreasing effects were as follows: MinT > MAP  > MAT . We further proposed that the latitudinal limit of evergreen and deciduous broad‐leaved mixed forests was 29.1–32°N, corresponding with MAT of 11–18.1°C, MinT of ?2.5 to 2.51°C, and MAP of 1,000–1,630 mm. This study is the first quantitative assessment of climatic correlates with the evergreenness and deciduousness of broad‐leaved forests in subtropical China and underscores that extreme cold temperature is the most important climatic determinant of evergreen and deciduous broad‐leaved tree species’ distributions, a finding that confirms earlier qualitative studies. Our findings also offer new insight into the definition and distribution of the mixed forest and an accurate assessment of vulnerability of mixed forests to future climate change.     

Investigating the relationships between the distribution of British ground beetle species (Coleoptera, Carabidae) and temperature, precipitation and altitude

Aims We examine the relationships between the distribution of British ground beetle species and climatic and altitude variables with a view to developing models for evaluating the impact of climate change. Location Data from 1684 10‐km squares in Britain were used to model species–climate/altitude relationships. A validation data set was composed of data from 326 British 10‐km squares not used in the model data set. Methods The relationships between incidence and climate and altitude variables for 137 ground beetle species were investigated using logistic regression. The models produced were subjected to a validation exercise using the Kappa statistic with a second data set of 30 species. Distribution patterns for four species were predicted for Britain using the regression equations generated. Results As many as 136 ground beetle species showed significant relationships with one or more of the altitude and climatic variables but the amount of variation explained by the models was generally poor. Models explaining 20% or more of the variation in species incidence were generated for only 10 species. Mean summer temperature and mean annual temperature were the best predictors for eight and six of these 10 species respectively. Few models based on altitude, annual precipitation and mean winter temperature were good predictors of ground beetle species distribution. The results of the validation exercise were mixed, with models for four species showing good or moderate fits whilst the remainder were poor. Main conclusions Whilst there were many significant relationships between British ground beetle species distributions and altitude and climatic variables, these variables did not appear to be good predictors of ground beetle species distribution. The poor model performance appears to be related to the coarse nature of the response and predictor data sets and the absence of key predictors from the models.