Ditch the niche – is the niche a useful concept in ecology or species distribution modelling?

In this first of three papers we examine the use of niche concepts in ecology and especially in species distribution modelling (SDM). This paper deliberately focuses on the lack of clarity found in the term ‘niche’. Because its meanings are so diverse, the term niche tends to create confusion and requires constant qualification. The literature houses many idiosyncratic ideas of what the niche is, but few examples where niche is more explanatory than the terminologies of population and community ecology or the statistical methods used to implement SDM analyses. In many cases the original (and inspirational) concepts are not directly applicable to our modern applications (e.g. set theory). There are some conceptual limitations found in individual definitions of niche (e.g. the fundamental niche concept), so it is perhaps understandable why more neutral terminology is becoming popular in SDM. An examination of the literature reveals a wide range of uncritical use of niche terminology. Our findings in this paper do not necessarily support the position of niche as a universally useful concept.     

Pitch the niche – taking responsibility for the concepts we use in ecology and species distribution modelling

In a discussion it is often easier to staunchly reject or offer resolute support for an idea. This third paper on the niche concept aims to develop a balanced argument by exploring general principles for determining an appropriate level for pitching the niche concept that will guide better use and less abuse of niche concepts. To do this we first have to accept that niche concepts are not necessarily essential for ecology. Rather than to improve niche concepts, our aim should then be to pitch the niche in terms of ecology. This aim helps us develop an ‘ultimate goal of the niche’ by which we can evaluate the concepts we use. For species distribution modelling, there has been a focus on the niche as an equilibrium outcome that perhaps has less relevance for disequilibrium situations (e.g. climate change projections). As is the case for much of ecology, more causal explanations of species' distributions use alternative terminologies and less frequently use the word ‘niche’. We suggest that niche concepts that are better aligned with the rest of ecology could arise from taking more responsibility for our own implementations, and by explaining our models with terms other than niche. A general, holistic niche concept promotes this view and promotes practical thinking about what we are modelling and how we interpret those models, which in turn should help inspire and support innovative modelling approaches in species distribution modelling.     

提高生态位模型转移能力来模拟入侵物种 的潜在分布

生态位模型利用物种分布点所关联的环境变量去推算物种的生态需求, 模拟物种的分布。在模拟入侵物种分布时, 经典生态位模型包括模型构建于物种本土分布地, 然后将其转移并投射至另一地理区域, 来模拟入侵物种的潜在分布。然而在模型运用时, 出现了模型的转移能力较低、模拟的结果与物种的实际分布不相符的情况, 由此得出了生态位漂移等不恰当的结论。提高生态位模型的转移能力, 可以准确地模拟入侵物种的潜在分布, 为入侵种的风险评估提供参考。作者以入侵种茶翅蝽(Halyomorpha halys)和互花米草(Spartina alterniflora)为例, 从模型的构建材料(即物种分布点和环境变量)入手, 全面阐述提高模型转移能力的策略。在构建模型之前, 需要充分了解入侵物种的生物学特性、种群平衡状态、本土地理分布范围及物种的生物历史地理等方面的知识。在模型构建环节上, 物种分布点不仅要充分覆盖物种的地理分布和生态空间的范围, 同时要降低物种采样点偏差; 环境变量的选择要充分考虑其对物种分布的限制作用、各环境变量之间的空间相关性, 以及不同地理种群间生态空间是否一致, 同时要降低环境变量的空间维度; 模型构建区域要真实地反映物种的地理分布范围, 并考虑种群的平衡状态。作者认为, 在生态位保守的前提下, 如果模型是构建在一个合理方案的基础上, 生态位模型的转移能力是可以保证的, 在以模型转移能力较低的现象来阐述生态位分化时需要引起注意。     

In situ adaptation and ecological release facilitate the occupied niche expansion of a non‐native Madagascan day gecko in Florida

AimTo investigate whether the frequently advocated climate‐matching species distribution modeling approach could predict the well‐characterized colonization of Florida by the Madagascar giant day gecko Phelsuma grandis.LocationMadagascar and Florida, USA.MethodsTo determine the climatic conditions associated with the native range of P. grandis, we used native‐range presence‐only records and Bioclim climatic data to build a Maxent species distribution model and projected the climatic thresholds of the native range onto Florida. We then built an analogous model using Florida presence‐only data and projected it onto Madagascar. We constructed a third model using native‐range presences for both P. grandis and the closely related parapatric species P. kochi.ResultsDespite performing well within the native range, our Madagascar Bioclim model failed to identify suitable climatic habitat currently occupied by P. grandis in Florida. The model constructed using Florida presences also failed to reflect the distribution in Madagascar by overpredicting distribution, especially in western areas occupied by P. kochi. The model built using the combined P. kochi/P. grandis dataset modestly improved the prediction of the range of P. grandis in Florida, thereby implying competitive exclusion of P. grandis by P. kochi from habitat within the former''s fundamental niche. These findings thus suggest ecological release of P. grandis in Florida. However, because ecological release cannot fully explain the divergent occupied niches of P. grandis in Madagascar versus Florida, our findings also demonstrate some degree of in situ adaptation in Florida.Main conclusionsOur models suggest that the discrepancy between the predicted and observed range of P. grandis in Florida is attributable to either in situ adaptation by P. grandis within Florida, or a combination of such in situ adaptation and competition with P. kochi in Madagascar. Our study demonstrates that climate‐matching species distribution models can severely underpredict the establishment risk posed by non‐native herpetofauna.     

Pliocene–Pleistocene ecological niche evolution shapes the phylogeography of a Mediterranean plant group

Estimating species ability to adapt to environmental changes is crucial to understand their past and future response to climate change. The Mediterranean Basin has experienced remarkable climatic changes since the Miocene, which have greatly influenced the evolution of the Mediterranean flora. Here, we examine the evolutionary history and biogeographic patterns of two sedge sister species (Carex, Cyperaceae) restricted to the western Mediterranean Basin, but with Pliocene fossil record in central Europe. In particular, we estimated the evolution of climatic niches through time and its influence in lineage differentiation. We carried out a dated phylogenetic–phylogeographic study based on seven DNA regions (nDNA and ptDNA) and fingerprinting data (AFLPs), and modelled ecological niches and species distributions for the Pliocene, Pleistocene and present. Phylogenetic and divergence time analyses revealed that both species form a monophyletic lineage originated in the late Pliocene–early Pleistocene. We detected clear genetic differentiation between both species with distinct genetic clusters in disjunct areas, indicating the predominant role of geographic barriers limiting gene flow. We found a remarkable shift in the climatic requirements between Pliocene and extant populations, although the niche seems to have been relatively conserved since the Pleistocene split of both species. This study highlights how an integrative approach combining different data sources and analyses, including fossils, allows solid and robust inferences about the evolutionary history of a plant group since the Pliocene.     

A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a “large” number of species into novel environments or in an independent area, the selection of the “best” model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.     

生态过程模型敏感参数最优取值的时空异质性分析——以BIOME-BGC模型为例

生态过程模型是当前研究陆地生态系统水循环、碳循环有力的工具,但此类模型参数众多,参数的合理取值对模型模拟结果有重要影响.以往研究对模型参数的敏感性以及参数的优化取值有诸多的分析和讨论,但有关参数最优取值的时空异质性关注较少.本文以BIOME-BGC模型为例,在常绿阔叶林、落叶阔叶林、C3草地3种植被类型下,通过构建敏感性判别指数,筛选出模型的敏感参数,并在每种植被类型下选取两个试验站点,使用模拟退火算法结合实测通量数据构建目标函数,获取各站点敏感参数逐月的最优取值,然后构建时间异质性判别指数、空间异质性判别指数和时空异质性判别指数对模型敏感参数最优取值的时空异质性进行定量分析.结果表明:BIOME-BGC模型在3种植被类型下遴选出的敏感参数大部分一致,少数有差异,但参数的敏感性强弱在不同植被类型下的表现不尽相同;BIOME-BGC模型敏感参数的最优取值,大都具有不同程度的时空异质性,但不同植被类型下,敏感参数最优取值的时空异质性表现各异;敏感参数中与植被生理、生态相关的参数,其时空异质性相对较小,而与环境、物候相关的参数,其时空异质性普遍较大;在3种植被类型下,模型敏感参数最优取值的时间异质性与空间异质性表现出显著的线性相关性;依据其最优取值的时空异质性,可对BIOME-BGC模型敏感参数进行类型划分,以便在实践应用中采取不同的参数率定策略.本研究结论有助于加深对生态过程模型参数特性及最优取值的理解,可为实践应用中模型参数的合理取值提供一种思路和参考.     

基于PSR模型的耕地生态安全评价及时空格局演变

耕地的生态安全关系到区域的可持续发展, 针对耕地生态安全现状, 以宁波市南郊小尺度区域为研究对象, 基于人与耕地的相互作用机理, 通过改进的PSR模型从而构建出适用于研究区域的评价体系, 采用熵权法和综合评价法, 对2005—2014年间宁波市南郊的耕地生态安全状况进行评价分析, 结果表明: (1)从时间尺度上看, 2005—2014年宁波市南郊耕地生态安全经历了“较安全—临界安全—较不安全—安全”的变化阶段。(2)从空间尺度上看, 各乡镇街道耕地生态安全状况存在着明显的区域性差异。西部地区整体水平较高, 东部地区次之, 其中东部以北最低; 2005—2014年宁波市南郊生态安全指数变化较小; 宁波市南郊生态安全指数呈现显著的空间集聚性, 高—高聚类显著性较弱, 低—低聚类显著性较强。     

基于改进三维生态足迹模型的洞庭湖区生态可持续时空演化研究

洞庭湖是长江流域重要的调蓄湖泊,也是中国重要的农产品主产区。科学评估洞庭湖区生态可持续状态,揭示其时空演化特征,对探究其生态可持续影响因素与作用机理,降低生态赤字具有重要意义。基于碳足迹与生态足迹理论构建了改进的三维生态足迹模型,填补了化石能源无法核算生态承载力的问题,同时调整了不同土地利用类型的参数因子。在对洞庭湖区25个区县进行可持续发展的动态评价分析中,计算了2000—2019年生态赤字、足迹广度与深度的时空分布特征。研究结果表明：(1)近20年来洞庭湖区的人均生态足迹增长速度远高于人均生态承载力,致使人均生态赤字在时间维度上不断增大,空间维度上呈湖滨向周边地区扩散的趋势;(2)洞庭湖区除林地之外的土地利用类型均存在自然资本存量的消耗,且表现为高生态赤字;(3)三次产业结构的分配、土地利用的变化、居民对生活质量的提高以及生态保护政策的有效实施等均会对洞庭湖区的可持续发展产生较大影响;(4)改进后的化石能源账户相较于传统账户的计算方法更能反映真实的碳排量与碳吸收量。研究以期为洞庭湖区土地利用、生态保护以及促进社会经济可持续发展提供科学参考。     

A test of the central–marginal hypothesis using population genetics and ecological niche modelling in an endemic salamander (Ambystoma barbouri)

The central–marginal hypothesis (CMH) predicts that population size, genetic diversity and genetic connectivity are highest at the core and decrease near the edges of species' geographic distributions. We provide a test of the CMH using three replicated core‐to‐edge transects that encompass nearly the entire geographic range of the endemic streamside salamander (Ambystoma barbouri). We confirmed that the mapped core of the distribution was the most suitable habitat using ecological niche modelling (ENM) and via genetic estimates of effective population sizes. As predicted by the CMH, we found statistical support for decreased genetic diversity, effective population size and genetic connectivity from core to edge in western and northern transects, yet not along a southern transect. Based on our niche model, habitat suitability is lower towards the southern range edge, presumably leading to conflicting core‐to‐edge genetic patterns. These results suggest that multiple processes may influence a species' distribution based on the heterogeneity of habitat across a species' range and that replicated sampling may be needed to accurately test the CMH. Our work also emphasizes the importance of identifying the geographic range core with methods other than using the Euclidean centre on a map, which may help to explain discrepancies among other empirical tests of the CMH. Assessing core‐to‐edge population genetic patterns across an entire species' range accompanied with ENM can inform our general understanding of the mechanisms leading to species' geographic range limits.     

Assessment of the stream invertebrate β‐diversity along an elevation gradient using a bidimensional null model analysis

β‐Diversity, commonly defined as the compositional variation among localities that links local diversity (α‐diversity) and regional diversity (γ‐diversity), can arise from two different ecological phenomena, namely the spatial species turnover (i.e., species replacement) and the nestedness of assemblages (i.e., species loss). However, any assessment that does not account for stochasticity in community assembly could be biased and misinform conservation management. In this study, we aimed to provide a better understanding of the overall ecological phenomena underlying stream β‐diversity along elevation gradients and to contribute to the rich debate on null model approaches to identify nonrandom patterns in the distribution of taxa. Based on presence‐absence data of 78 stream invertebrate families from 309 sites located in the Swiss Alpine region, we analyzed the effect size of nonrandom spatial distribution of stream invertebrates on the β‐diversity and its two components (i.e., turnover and nestedness). We used a modeling framework that allows exploring the complete range of existing algorithms used in null model analysis and assessing how distribution patterns vary according to an array of possible ecological assumptions. Overall, the turnover of stream invertebrates and the nestedness of assemblages were significantly lower and higher, respectively, than the ones expected by chance. This pattern increased with elevation, and the consistent trend observed along the altitudinal gradient, even in the most conservative analysis, strengthened our findings. Our study suggests that deterministic distribution of stream invertebrates in the Swiss Alpine region is significantly driven by differential dispersal capacity and environmental stress gradients. As long as the ecological assumptions for constructing the null models and their implications are acknowledged, we believe that they still represent useful tools to measure the effect size of nonrandom spatial distribution of taxa on β‐diversity.     

Without quality presence–absence data,discrimination metrics such as TSS can be misleading measures of model performance

The discriminating capacity (i.e. ability to correctly classify presences and absences) of species distribution models (SDMs) is commonly evaluated with metrics such as the area under the receiving operating characteristic curve (AUC), the Kappa statistic and the true skill statistic (TSS). AUC and Kappa have been repeatedly criticized, but TSS has fared relatively well since its introduction, mainly because it has been considered as independent of prevalence. In addition, discrimination metrics have been contested because they should be calculated on presence–absence data, but are often used on presence‐only or presence‐background data. Here, we investigate TSS and an alternative set of metrics—similarity indices, also known as F‐measures. We first show that even in ideal conditions (i.e. perfectly random presence–absence sampling), TSS can be misleading because of its dependence on prevalence, whereas similarity/F‐measures provide adequate estimations of model discrimination capacity. Second, we show that in real‐world situations where sample prevalence is different from true species prevalence (i.e. biased sampling or presence‐pseudoabsence), no discrimination capacity metric provides adequate estimation of model discrimination capacity, including metrics specifically designed for modelling with presence‐pseudoabsence data. Our conclusions are twofold. First, they unequivocally impel SDM users to understand the potential shortcomings of discrimination metrics when quality presence–absence data are lacking, and we recommend obtaining such data. Second, in the specific case of virtual species, which are increasingly used to develop and test SDM methodologies, we strongly recommend the use of similarity/F‐measures, which were not biased by prevalence, contrary to TSS.     

Mapping the geographic distribution of <Emphasis Type="Italic">Aglaia bourdillonii</Emphasis> Gamble (Meliaceae), an endemic and threatened plant,using ecological niche modeling

Aglaia bourdillonii is a plant narrowly endemic to the southern portion of the Western Ghats (WG), in peninsular India. To understand its ecological and geographic distribution, we used ecological niche modeling (ENM) based on detailed distributional information recently gathered, in relation to detailed climatic data sets. The ENMs successfully reconstructed key features of the species’ geographic distribution, focusing almost entirely on the southern WG. Much of the species’ distributional potential is already under protection, but our analysis allows identification of key zones for additional protection, all of which are adjacent to existing protected areas. ENM provides a useful tool for understanding the natural history of such rare and endangered species.

Temporal dynamics of soil organic carbon after land‐use change in the temperate zone – carbon response functions as a model approach

Land‐use change (LUC) is a major driving factor for the balance of soil organic carbon (SOC) stocks and the global carbon cycle. The temporal dynamic of SOC after LUC is especially important in temperate systems with a long reaction time. On the basis of 95 compiled studies covering 322 sites in the temperate zone, carbon response functions (CRFs) were derived to model the temporal dynamic of SOC after five different LUC types (mean soil depth of 30±6 cm). Grassland establishment caused a long lasting carbon sink with a relative stock change of 128±23% and afforestation on former cropland a sink of 116±54%, 100 years after LUC (mean±95% confidence interval). No new equilibrium was reached within 120 years. In contrast, there was no SOC sink following afforestation of grasslands and 75% of all observations showed SOC losses, even after 100 years. Only in the forest floor, there was carbon accumulation of 0.38±0.04 Mg ha^?1 yr^?1 in afforestations adding up to 38±4 Mg ha^?1 labile carbon after 100 years. Carbon loss after deforestation (?32±20%) and grassland conversion to cropland (?36±5%), was rapid with a new SOC equilibrium being reached after 23 and 17 years, respectively. The change rate of SOC increased with temperature and precipitation but decreased with soil depth and clay content. Subsoil SOC changes followed the trend of the topsoil SOC changes but were smaller (25±5% of the total SOC changes) and with a high uncertainty due to a limited number of datasets. As a simple and robust model approach, the developed CRFs provide an easily applicable tool to estimate SOC stock changes after LUC to improve greenhouse gas reporting in the framework of UNFCCC.