Biotic interactions improve prediction of boreal bird distributions at macro-scales

Aim The role of biotic interactions in influencing species distributions at macro‐scales remains poorly understood. Here we test whether predictions of distributions for four boreal owl species at two macro‐scales (10 × 10 km and 40 × 40 km grid resolutions) are improved by incorporating interactions with woodpeckers into climate envelope models. Location Finland, northern Europe. Methods Distribution data for four owl and six woodpecker species, along with data for six land cover and three climatic variables, were collated from 2861 10 × 10 km grid cells. Generalized additive models were calibrated using a 50% random sample of the species data from western Finland, and by repeating this procedure 20 times for each of the four owl species. Models were fitted using three sets of explanatory variables: (1) climate only; (2) climate and land cover; and (3) climate, land cover and two woodpecker interaction variables. Models were evaluated using three approaches: (1) examination of explained deviance; (2) four‐fold cross‐validation using the model calibration data; and (3) comparison of predicted and observed values for independent grid cells in eastern Finland. The model accuracy for approaches (2) and (3) was measured using the area under the curve of a receiver operating characteristic plot. Results At 10‐km resolution, inclusion of the distribution of woodpeckers as a predictor variable significantly improved the explanatory power, cross‐validation statistics and the predictive accuracy of the models. Inclusion of land cover led to similar improvements at 10‐km resolution, although these improvements were less apparent at 40‐km resolution for both land cover and biotic interactions. Main conclusions Predictions of species distributions at macro‐scales may be significantly improved by incorporating biotic interactions and land cover variables into models. Our results are important for models used to predict the impacts of climate change, and emphasize the need for comprehensive evaluation of the reliability of species–climate impact models.     

A multispecies approach to manage effects of land cover and weather on upland game birds

Loss and degradation of grasslands in the Great Plains region have resulted in major declines in abundance of grassland bird species. To ensure future viability of grassland bird populations, it is crucial to evaluate specific effects of environmental factors among species to determine drivers of population decline and develop effective conservation strategies. We used threshold models to quantify the effects of land cover and weather changes in "lesser prairie‐chicken" and "greater prairie‐chicken" (Tympanuchus pallidicinctus and T. cupido, respectively), northern bobwhites (Colinus virginianus), and ring‐necked pheasants (Phasianus colchicus). We demonstrated a novel approach for estimating landscape conditions needed to optimize abundance across multiple species at a variety of spatial scales. Abundance of all four species was highest following wet summers and dry winters. Prairie chicken and ring‐necked pheasant abundance was highest following cool winters, while northern bobwhite abundance was highest following warm winters. Greater prairie chicken and northern bobwhite abundance was also highest following cooler summers. Optimal abundance of each species occurred in landscapes that represented a grassland and cropland mosaic, though prairie chicken abundance was optimized in landscapes with more grassland and less edge habitat than northern bobwhites and ring‐necked pheasants. Because these effects differed among species, managing for an optimal landscape for multiple species may not be the optimal scenario for any one species.     

华北平原土地利用/覆盖对CO空间分布的调控作用

CO不仅是中国主要的空气污染物之一,还是温室效应的贡献者。农业用地每年消耗了大量的CO通量,土地利用/覆盖格局对于调控CO空间分布发挥了较大的作用。针对土地利用/覆盖调控CO空间分布开展研究,以华北平原为例揭示人类活动对CO空间异质性的影响。研究发现2010至2020年华北平原CO排放量由4964×10⁴ t降低至2683×10⁴ t,大部分耕地CO浓度由90 t/km²下降至45 t/km²以下。CO浓度空间集聚程度呈现先降低后升高趋势,Moran′s I指数由0.25增加至0.41。经济发展迅速的地区CO污染较为严重,北京和周边城市形成了CO污染高-高集聚区,周口和淮北等城市则形成了低-低集聚区。总体来看,CO浓度呈低-低集聚分布的区域不断扩大,反映出CO减排措施已经初见效果。研究表明土地利用/覆盖在类型与结构方面的差异影响了CO的排放、扩散以及氧化消耗,增加了大气CO收支的不确定性,对CO空间分布具有一定的调控作用。通过分析土地利用/覆盖与CO空间分布的关联性,探究土地利用/覆盖及景观格局对区...     

The role of protected areas in land use/land cover change and the carbon cycle in the conterminous United States

Protected areas (PAs) cover about 22% of the conterminous United States. Understanding their role on historical land use and land cover change (LULCC) and on the carbon cycle is essential to provide guidance for environmental policies. In this study, we compiled historical LULCC and PAs data to explore these interactions within the terrestrial ecosystem model (TEM). We found that intensive LULCC occurred in the conterminous United States from 1700 to 2005. More than 3 million km² of forest, grassland and shrublands were converted into agricultural lands, which caused 10,607 Tg C release from land ecosystems to atmosphere. PAs had experienced little LULCC as they were generally established in the 20th century after most of the agricultural expansion had occurred. PAs initially acted as a carbon source due to land use legacies, but their accumulated carbon budget switched to a carbon sink in the 1960s, sequestering an estimated 1,642 Tg C over 1700–2005, or 13.4% of carbon losses in non‐PAs. We also find that PAs maintain larger carbon stocks and continue sequestering carbon in recent years (2001–2005), but at a lower rate due to increased heterotrophic respiration as well as lower productivity associated to aging ecosystems. It is essential to continue efforts to maintain resilient, biodiverse ecosystems and avoid large‐scale disturbances that would release large amounts of carbon in PAs.     

A review of influences of land use and land cover change on ecosystems

随着人口的增长和人类社会的发展, 土地利用与土地覆盖变化已经是不可避免。土地利用与土地覆盖变化不仅对生态系统的要素、结构和功能产生深远的影响, 也会对全球变化产生反馈作用。针对土地利用与土地覆盖变化的过程、驱动机制以及在各个方面可能产生的生态环境效应的科学研究已经全面开展。该文综述了土地利用与土地覆盖变化对气候、土壤、生物地球化学循环、生物多样性以及区域生态环境等影响方面的研究进展, 并提出了相关研究的前沿方向展望。随着新技术的不断发展, 学者们将更多地侧重预测未来全球变化背景下的土地利用与土地覆盖变化趋势、合理性以及适应性, 为可持续发展提供基础资料和理论依据。     

Predicting range expansion of the map butterfly in Northern Europe using bioclimatic models

The two main goals of this study are: (i) to examine the range shifts of a currently northwards expanding species, the map butterfly (Araschnia levana), in relation to annual variation in weather, and (ii) to test the capability of a bioclimatic envelope model, based on broad-scale European distribution data, to predict recent distributional changes (2000–2004) of the species in Finland. A significant relationship between annual maximum dispersal distance of the species and late summer temperature was detected. This suggests that the map butterfly has dispersed more actively in warmer rather than cooler summers, the most notable dispersal events being promoted by periods of exceptionally warm weather and southerly winds. The accuracy of the broad-scale bioclimatic model built for the species with European data using Generalized Additive Models (GAM) was good based on split-sample evaluation for a single period. However, the model’s performance was poor when applied to predict range shifts in Finland. Among the many potential explanations for the poor success of the transferred bioclimatic model, is the fact that bioclimatic envelope models do not generally account for species dispersal. This and other uncertainties support the view that bioclimatic models should be applied with caution when they are used to project future range shifts of species.     

土地利用与土地覆盖变化对生态系统的影响

随着人口的增长和人类社会的发展, 土地利用与土地覆盖变化已经是不可避免。土地利用与土地覆盖变化不仅对生态系统的要素、结构和功能产生深远的影响, 也会对全球变化产生反馈作用。针对土地利用与土地覆盖变化的过程、驱动机制以及在各个方面可能产生的生态环境效应的科学研究已经全面开展。该文综述了土地利用与土地覆盖变化对气候、土壤、生物地球化学循环、生物多样性以及区域生态环境等影响方面的研究进展, 并提出了相关研究的前沿方向展望。随着新技术的不断发展, 学者们将更多地侧重预测未来全球变化背景下的土地利用与土地覆盖变化趋势、合理性以及适应性, 为可持续发展提供基础资料和理论依据。     

Ensemble models predict Important Bird Areas in southern Africa will become less effective for conserving endemic birds under climate change

Aim  To examine the impacts of climate change on endemic birds, which are of global significance for conservation, on a continent with few such assessments. We specifically assess projected range changes in relation to the Important Bird Areas (IBAs) network and assess the possible consequences for conservation.
Location  South Africa, Lesotho and Swaziland.
Methods  The newly emerging ensemble modelling approach is used with 50 species, four climate change models for the period 2070–2100 and eight bioclimatic niche models in the statistical package biomod . Model evaluation is done using the receiver operating characteristic and the recently introduced true skill statistic. Future projections are made considering two extreme assumptions: species have full dispersal ability and species have no dispersal ability. A consensus forecast is identified using principal components analysis. This forecast is interpreted in terms of the IBA network. An irreplaceability analysis is used to highlight priority IBAs for conservation attention in terms of climate change.
Results  The majority of species (62%) are predicted to lose climatically suitable space. Five species lose at least 85% of their climatically suitable space. Many IBAs lose species (41%; 47 IBAs) and show high rates of species turnover of more than 50% (77%; 95 IBAs). Highly irreplaceable regions for endemic species become highly localized under climate change, meaning that the endemic species analysed here experience similar range contractions to maintain climate niches.
Main conclusions  The South African IBAs network is likely to become less effective for conserving endemic birds under climate change. The irreplaceability analysis identified key refugia for endemic species under climate change, but many of these areas are not currently IBAs. In addition, many of these high-priority areas that are IBAs fall outside the current formal protected areas network.     

Challenges in using land use and land cover data for global change studies

Land use and land cover data play a central role in climate change assessments. These data originate from different sources and inventory techniques. Each source of land use/cover data has its own domain of applicability and quality standards. Often data are selected without explicitly considering the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment. Uncertainties due to data selection and handling can be in the same order of magnitude as uncertainties related to the representation of the processes under investigation. While acknowledging the differences in data sources and the causes of inconsistencies, several methods have been developed to optimally extract information from the data and document the uncertainties. These methods include data integration, improved validation techniques and harmonization of classification systems. Based on the data needs of global change studies and the data availability, recommendations are formulated aimed at optimal use of current data and focused efforts for additional data collection. These include: improved documentation using classification systems for land use/cover data; careful selection of data given the specific application and the use of appropriate scaling and aggregation methods. In addition, the data availability may be improved by the combination of different data sources to optimize information content while collection of additional data must focus on validation of available data sets and improved coverage of regions and land cover types with a high level of uncertainty. Specific attention in data collection should be given to the representation of land management (systems) and mosaic landscapes.     

Land-cover data improve bioclimatic models for anurans and turtles at a regional scale

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.     

艾比湖区域水质空间分布特征及其与土地利用/覆被类型的关系

为深入了解艾比湖周边水质空间格局的变化,利用2014年5月及2014年10月实测水样采样点研究了艾比湖区域4个水质指标的空间变化趋势,包括p H、电导率、矿化度及全盐。结果表明:(1)探讨2014年内干湿季水质的空间分布趋势,从总体上看,p H表现出明显的交错模式;电导率、矿化度及全盐在干湿季变化差异较大,高值均聚集在艾比湖周围,而在耕种土地、天然梭梭林附近值则较低;(2)分析各水质的空间自相关性,p H的HH主要分布于艾比湖北部,LL则主要集中于艾比湖南部。HL的分布,说明有较高p H值的点被有较低p H值的点所包围,表明了水质在一定程度上存在空间异质性。电导率、矿化度及全盐在沿着艾比湖周边到甘家湖梭梭林自然保护区随着地势的增高呈现出由HH-LH-LL的转变。(3)建立水质参数与各土地利用/覆盖类型之间的关系,研究表明研究区内林草地、盐渍地及耕地对水质的影响相对较为显著。(4)为了研究水质变化因素,选取耕地、林草地、盐渍地和未利用地与水质参数分别建立优选多元线性回归模型,所得到的相关系数R分别为0.58、0.72、0.74、0.71,结果表明优选拟合模型与数据的拟合程度较好。总之,开展艾比湖区域水质空间格局的变化趋势研究,对于干旱区水质的时空分布具有重要的理论和实际意义。     

藏北高原地表覆盖时空动态及其对气候变化的响应

利用2001—2008年逐年的MODIS地表覆盖类型产品,根据藏北高原地表覆被特征对原始数据进行合并处理,得到每年藏北高原地表覆盖类型图;运用分类统计、动态转移矩阵、景观格局指数方法分析藏北高原地表覆盖类型的变化,并结合研究区内气象台站观测数据分析地表覆盖类型转化对气候变化的响应特征.结果表明:研究期间,由于气候变暖速率的加快,研究区冰川雪被消融加速,冰川面积迅速萎缩,融化的雪水汇集到高原湖盆,使湖面水位上升,湖泊面积增加,部分被淹没的草地形成湿地;植被覆盖状况没有表现出明显的变好或退化趋势,2001—2004年为气候暖湿化阶段,荒漠裸地减少、稀疏草地和草地覆盖面积增加,2006—2007年为气候暖干化阶段,荒漠面积增加、稀疏草地面积减小;2001—2008年,藏北高原景观破碎度减小,地表覆盖异质性降低,且各类型所占比例的差异有所加大.     

土地利用/覆盖变化对中国不同季节气温的影响

近几十年中国地区土地利用/覆盖变化(LUCC)较大,在区域气候模拟中尤其需要使用更加准确的土地利用/覆盖数据。基于模式原有的USGS和新开发的LUC90两种土地利用/覆盖资料,利用区域环境集成模拟系统(RIEMS2.0)分别进行连续10a模拟,分析LUCC对中国不同季节气温的影响。结果表明:1)采用LUC90资料后,中国及东北、华北、华南夏季平均气温增加,但只有东北模拟与观测值的偏差减小,且通过显著性检验(P0.01)。中国及东北、华南冬季平均气温增加,并且模拟与观测值的偏差减少。中国及华北和华南对冬季气温年际变率的模拟改善好于夏季。2)土地利用/覆盖变化通过影响潜热通量的变化和净吸收辐射通量的变化来影响不同季节气温的变化。冬季净辐射通量变化对气温变化的贡献较夏季大,而夏季潜热通量变化对气温变化的贡献较冬季大。雨养农田转变森林、草地、灌溉农田过程造成通量变化,其对气温变化的影响也存在不同分区季节的差异。     

Climate and satellite-derived land cover for predicting breeding bird distribution in the Great Lakes Basin

Aim We examined relationships between breeding bird distribution of 10 forest songbirds in the Great Lakes Basin, large‐scale climate and the distribution of land cover types as estimated by advanced very high resolution radiometer (AVHRR) and multi‐spectral scanner (MSS) land cover classifications. Our objective was to examine the ability of regional climate, AVHRR (1 km resolution) land cover and MSS (200 m resolution) land cover to predict the distribution of breeding forest birds at the scale of the Great Lakes Basin and at the resolution of Breeding Bird Atlas data (5–10 km²). Specifically we addressed the following questions. (1) How well do AVHRR or MSS classifications capture the variation in distribution of bird species? (2) Is one land cover classification more useful than the other for predicting distribution? (3) How do models based on climate compare with models based on land cover? (4) Can the combination of both climate and land cover improve the predictive ability of these models. Location Modelling was conducted over the area of the Great Lakes Basin including parts of Ontario, Canada and parts of Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania Wisconsin, and Minnesota, USA. Methods We conducted single variable logistic regression with the forest classes of AVHRR and MSS land cover using evidence of breeding as the response variable. We conducted multiple logistic regression with stepwise selection to select models from five sets of explanatory variables (AVHRR, MSS, climate, AVHRR + climate, MSS + climate). Results Generally, species were related to both AVHRR and MSS land cover types in the direction expected based on the known local habitat use of the species. Neither land cover classification appeared to produce consistently more intuitive results. Good models were generated using each of the explanatory data sets examined here. And at least one but usually all five variable sets produced acceptable or excellent models for each species. Main conclusions Both climate and large scale land cover were effective predictors of the distribution of the 10 forest bird species examined here. Models generated from these data had good classification accuracy of independent validation data. Good models were produced from all explanatory data sets or combinations suggesting that the distribution of climate, AVHRR land cover, and MSS land cover all captured similar variance in the distribution of the birds. It is difficult to separate the effects of climate and vegetation on the species’ distributions at this scale.     

中国西南地区土地覆盖情景的时空模拟

气候植被类型的空间分布与土地覆盖类型的空间分布在时空层次上具有很好的相关性和一致性。在运用HLZ生态系统模型获得CMIP5的3种气候情景RCP26、RCP45、RCP85情景下西南地区未来90a(2011—2100年)HLZ生态系统时空分布情景数据的基础上,结合2010年土地覆盖现状数据,构建了土地覆盖情景的空间分析模型,并在此基础上,实现了西南地区未来90a土地覆盖情景的时空模拟分析。模拟结果表明:3种气候情景下,西南地区未来90a的落叶针叶林、落叶阔叶林、草地、耕地、冰雪、荒漠及裸岩石砾地等土地覆盖类型面积将呈逐渐减少趋势;常绿针叶林、常绿阔叶林、混交林、灌丛、湿地、建设用地、水体等土地覆盖类型面积则呈逐渐增加趋势。其中,湿地增加速度最快(平均每10a增加5.28%),荒漠及裸岩石砾地减少速度最快(平均每10a减少2.34%)。     

Predicted future distribution of the African skimmer in response to a changing climate,land cover and distance from water in the mid-Zambezi Valley

Climate models project a hot and dry future for Southern Africa. In this research, Maximum Entropy was used to model the extent to which climate change, land cover and distance from water edges may influence current and future distribution of the African skimmer in the mid-Zambezi Valley. Global Biodiversity Information Facility data collected between the years 2000–2019 were used to develop the models. Three models were built: one for current distribution and two for future distribution under Representative Concentration Pathways (RCPs) 2.6 and 6.0. Results revealed that annual precipitation and distance from water edges were the most important predictors of habitat suitability for the African skimmer under current and future climate. Temperature and land cover were least important in explaining current and future distribution of the species. The RCP 2.6 predicted future decrease in suitable habitat for the African skimmer in the mid-Zambezi Valley, while RCP 6.0 predicted future increase in suitable habitat for the species. This research conclusively revealed that precipitation and distance from water edges were consistently key predictors of suitable habitat for the African skimmer.     

Distribution patterns of boreal marshland birds: modelling the relationships to land cover and climate

Aim The aims of this work were (1) to study how well land‐cover and climatic data are capable of explaining distribution patterns of ten bird species breeding and/or feeding primarily on marshes and other wetlands and (2) to compare the differences between red‐listed and common marshland species in explanatory variables, and to study the predictability of their distribution patterns. 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 used in the analyses. Land‐cover data based on CORINE (Coordination of Information on the Environment) classification and climatic variables were compiled using the same 10 × 10 km grid. Generalized additive models (GAM) with a stepwise selection procedure were used to select relevant explanatory variables and to examine the complexity of the response shapes of the different species to each variable. The original data set was randomly divided into model training (70%) and model evaluation (30%) sets. The final models of common and red‐listed bird species richness were validated by fitting them to the model evaluation set, and the correlation between observed and predicted species richness was calculated. We assessed the discrimination ability of the binary models (single species) with the area under the curve (AUC) of a receiver operating characteristic (ROC) plot and the Kappa coefficient. Results Cover of marshland, shoreline length and mean temperature in April–June were significantly (P < 0.01) related to the common marshland species richness. Cover and clumping of marshland and mean temperature and precipitation in April–June were selected in the model of red‐listed marshland species richness. The level of discrimination in our single species models varied in ROC from fair to excellent (AUC values 0.70–0.95). Cover of marshland was included in all GAM models built for the target species, but clumping of marshland, shoreline length and cover of mires also appeared as important predictors in single species models. Seven species had statistically significant relationships with climatic variables in the multivariate GAMs. Cover of marshland was highest in squares in which the red‐listed bittern Botaurus stellaris, marsh harrier Circus aeruginosus and great reed warbler Acrocephalus arundinaceus and the water rail Rallus aquaticus were observed. Main conclusions Cover of marshland was the only variable which was included in all the models, reinforcing the close connection between the studied species and marshlands. Broad‐scale clumping of marshlands was important for the red‐listed species, probably due to the much lower population sizes of red‐listed species than those of common species. Land‐cover data produced in CORINE seems to be well suited for modelling the distribution patterns of marshland birds. Although climatic variables also strongly affect the studied marshland birds, habitat availability plays a crucial role in their occurrence. The distribution patterns of marshland birds at the scale of 10 × 10 km reflect the interplay between habitat availability and direct climatic variables.     

Inclusion of soil data improves the performance of bioclimatic envelope models for insect species distributions in temperate Europe

Aim To analyse the effect of the inclusion of soil and land‐cover data on the performance of bioclimatic envelope models for the regional‐scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well‐surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land‐cover, (3) climate and soil, and (4) climate, land‐cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land‐cover classes or predominant soil types led to similar improvements in the performance relative to the climate‐only models for both taxonomic groups. In addition, the joint inclusion of land‐cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land‐cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land‐cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional‐scale species–climate impact models, particularly when predicting future range shifts of species under climate change.