Spatio–temporal variation of vegetation heterogeneity in groundwater dependent ecosystems within arid environments

Climate change, land cover change and the over–abstraction of groundwater threaten the existence of Groundwater-Dependent Ecosystems (GDE), despite these environments being regarded as biodiversity hotspots. The vegetation heterogeneity in GDEs requires routine monitoring in order to conserve and preserve the ecosystem services in these environments. However, in–situ monitoring of vegetation heterogeneity in extensive, or transboundary, groundwater resources remain a challenge. Inherently, the Spectral Variation Hypothesis (SVH) and remotely-sensed data provide a unique way to monitor the response of GDEs to seasonal or intra–annual environmental stressors, which is the key for achieving the national and regional biodiversity targets. This study presents the first attempt at monitoring the intra–annual, spatio–temporal variations in vegetation heterogeneity in the Khakea–Bray Transboundary Aquifer, which is located between Botswana and South Africa, by using the coefficient of variation derived from the Landsat 8 OLI Operational Land Imager (OLI). The coefficient of variation was used to measure spectral heterogeneity, which is a function of environmental heterogeneity. Heterogenous environments are more diverse, compared to homogenous environments, and the vegetation heterogeneity can be inferred from the heterogeneity of a landscape. The coefficient of variation was used to calculate the α- and β measures of vegetation heterogeneity (the Shannon–Weiner Index and the Rao's Q, respectively), whilst the monotonic trends in the spatio–temporal variation (January–December) of vegetation heterogeneity were derived by using the Mann–Kendall non–parametric test. Lastly, to explain the spatio–temporal variations of vegetation heterogeneity, a set of environmental variables were used, along with a machine-learning algorithm (random forest). The vegetation heterogeneity was observed to be relatively high during the wet season and low during the dry season, and these changes were mainly driven by landcover- and climate–related variables. More specifically, significant changes in vegetation heterogeneity were observed around natural water pans, along roads and rivers, as well as in cropping areas. Furthermore, these changes were better predicted by the Rao's Q (MAE = 5.81, RMSE = 6.63 and %RMSE = 42.41), than by the Shannon–Weiner Index (MAE = 30.37, RMSE = 33.25 and %RMSE = 63.94). These observations on the drivers and changes in vegetation heterogeneity provide new insights into the possible effects of future landcover changes and climate variability on GDEs. This information is imperative, considering that these environments are biodiversity hotspots that are capable of supporting many livelihoods. More importantly, this work provides a spatially explicit framework on how GDEs can be monitored to achieve Sustainable Development Goal (SDG) Number 15.     

The influence of habitat heterogeneity and latitude on gamma diversity of the Nearctic Simuliidae,a ubiquitous group of stream‐dwelling insects

Among the most prominent, large‐scale patterns of species richness are the increases in richness with decreasing latitude and with increasing habitat heterogeneity. Using the stream‐dwelling larval and pupal stages of North American black flies (Diptera: Simuliidae), we address 3 broad questions about species richness: (i) Does a significant latitude–richness relationship exist? (ii) How does habitat heterogeneity influence gamma diversity? (iii) What is the sign (positive or negative) of the latitude–richness and the heterogeneity–richness relationships? We found no evidence that habitat heterogeneity influences gamma diversity. The estimated peak species richness for black flies in North America was at 50–53°N, which also corresponds with peak generic richness. All plesiomorphic, extant lineages of the Simuliidae in the Western Hemisphere are found in cool mountainous environments of North America, suggesting that peak richness at 50–53°N might be a signature of this phylogenetic pattern and a reflection of underlying historical processes.     

Global patterns of species richness of the holarctic alpine herb Saxifraga: the role of temperature and habitat heterogeneity

北半球高山和极地虎耳草属物种丰富度的地理格局：温度和生境异质性的作用 现代气候、生境异质性和长期气候变化对森林生态系统中分布的木本植物的物种丰富度格局的影响在以往研究中受到广泛关注,但对高寒-极地生态系统中的草本植物物种丰富度格局及其影响因素的研究仍较少。本研究旨在检验以往研究中基于物种丰富度和环境因子关系提出的假说是否能够解释高寒-极地地区典型草本植物-虎耳草属(Saxifraga)的物种丰富度格局。本研究利用全球437种虎耳草属物种分布数据,探讨了全部物种、广域和狭域物种丰富度格局的影响因素。采用广义线性模型和空间自回归模型,评估了现代气候、生境异质性和历史气候对虎耳草属物种丰富度格局的影响。采用偏回归分析了不同变量对物种丰富度的独立解释率和共同解释率,并检验了4种广泛使用的物种丰富度与环境关系模型对物种丰富度格局的解释能力。研究结果表明,温度与虎耳草属所有物种和广域物种的物种丰富度格局呈显著负相关关系,是影响物种丰富度格局最重要的环境因子,这可能反映了虎耳草属对其祖先温带生态位的保守性。生境异质性和末次冰期以来的气候变化是虎耳草属狭域物种丰富度空间变异的最佳预测因子。总体而言,包含5个预测变量的组合模型可以解释大约40%–50%的虎耳草属物种丰富度的空间变异。此外,进化和生物地理过程在虎耳草属物种丰富度格局形成方面可能发挥了重要作用,这有待进一步研究。     

Environmental heterogeneity as a universal driver of species richness across taxa,biomes and spatial scales

Environmental heterogeneity is regarded as one of the most important factors governing species richness gradients. An increase in available niche space, provision of refuges and opportunities for isolation and divergent adaptation are thought to enhance species coexistence, persistence and diversification. However, the extent and generality of positive heterogeneity–richness relationships are still debated. Apart from widespread evidence supporting positive relationships, negative and hump‐shaped relationships have also been reported. In a meta‐analysis of 1148 data points from 192 studies worldwide, we examine the strength and direction of the relationship between spatial environmental heterogeneity and species richness of terrestrial plants and animals. We find that separate effects of heterogeneity in land cover, vegetation, climate, soil and topography are significantly positive, with vegetation and topographic heterogeneity showing particularly strong associations with species richness. The use of equal‐area study units, spatial grain and spatial extent emerge as key factors influencing the strength of heterogeneity–richness relationships, highlighting the pervasive influence of spatial scale in heterogeneity–richness studies. We provide the first quantitative support for the generality of positive heterogeneity–richness relationships across heterogeneity components, habitat types, taxa and spatial scales from landscape to global extents, and identify specific needs for future comparative heterogeneity–richness research.     

Habitat heterogeneity and mammalian predator–prey interactions

• 1 In predator–prey theory, habitat heterogeneity can affect the relationship between kill rates and prey or predator density through its effect on the predator's ability to search for, encounter, kill and consume its prey. Many studies of predator–prey interactions include the effect of spatial heterogeneity, but these are mostly based on species with restricted mobility or conducted in experimental settings.
• 2 Here, we aim to identify the patterns through which spatial heterogeneity affects predator–prey dynamics and to review the literature on the effect of spatial heterogeneity on predator–prey interactions in terrestrial mammalian systems, i.e. in freely moving species with high mobility, in non‐experimental settings. We also review current methodologies that allow the study of the predation process within a spatial context.
• 3 When the functional response includes the effect of spatial heterogeneity, it usually takes the form of predator‐dependent or ratio‐dependent models and has wide applicability.
• 4 The analysis of the predation process through its different stages may further contribute towards identifying the spatial scale of interest and the specific spatial mechanism affecting predator–prey interactions.
• 5 Analyzing the predation process based on the functional response theory, but separating the stages of predation and applying a multiscale approach, is likely to increase our insight into how spatial heterogeneity affects predator–prey dynamics. This may increase our ability to forecast the consequences of landscape transformations on predator–prey dynamics.

     

Comparative power law analysis for the spatial heterogeneity scaling of the hot‐spring microbiomes

Spatial heterogeneity is a fundamental property of any natural ecosystems, including hot spring and human microbiomes. Two important scales that spatial heterogeneity exhibits are population and community scales, and Taylor's power law (PL) and its extensions (PLEs) offer ideal quantitative models to assess population‐ and community‐level heterogeneities. Here we analyse 165 hot spring microbiome samples at the global scale that cover a wide range of temperatures (7.5–99°C) and pH levels (3.3–9). We explore a question of fundamental importance for measuring the spatial heterogeneity of the hot‐spring microbiome and further discuss their ecological implications: How do critical environmental factors such as temperature and pH influence the scaling of community spatial heterogeneity? We are particularly interested in the existence of a universal scaling model that is independent of environmental gradients. By applying PL and PLEs, we were able to obtain such scaling parameters of the hot spring at both community and population levels, which are temperature‐ and pH‐invariant. These findings suggest that while the hot‐spring microbiomes located at different regions may have different environmental conditions, they share a fundamental heterogeneity scaling parameter, analogically similar to the gravitational acceleration on Earth, which may vary slightly depending on altitude and latitude, but is invariant overall. In contrast, similar to the physics of the Moon and Earth, which have different gravitational accelerations, the hot spring and human microbiomes can have different scaling parameters as demonstrated in this study.     

Diversity patterns from sequentially restored grasslands support the ‘environmental heterogeneity hypothesis’

The ‘environmental heterogeneity hypothesis’ (EHH) has been proposed as a mechanism that enables species coexistence through resource partitioning. In accordance with this hypothesis, plant diversity is predicted to increase with variability in resources, but there has been weak support for this hypothesis from experimental studies. The objectives of this research were to 1) characterize how resource availability and heterogeneity (coefficient of variation) change as plant communities develop using sequentially restored grasslands, 2) determine if resource heterogeneity relates to plant diversity (effective number of species, richness and evenness) and 3) reveal if the strength of resource heterogeneity–diversity relationships is different among levels of resource availability. We quantified means and coefficients of variation in soil nitrate and light availability in grasslands established on former agricultural lands for different times and their relationship to plant diversity using a geostatistically‐informed design. Nitrate availability decreased exponentially with restoration age, but no directional change in nitrate heterogeneity across the chronosequence occurred due to high resource variability in some restorations. Light availability also decreased exponentially across the chronosequence, but there was no directional change in light heterogeneity. Nitrate heterogeneity was positively correlated with both plant richness and plant effective number of species at high levels of nitrate availability. However, no nitrate heterogeneity correlation was detected at low levels of nitrate availability. Light heterogeneity was positively correlated with plant effective number of species at low levels of light availability. However, no light heterogeneity correlation was detected at high levels of light availability. Plant evenness was not correlated with resource heterogeneity at any resource availability level. These results support the positive heterogeneity–diversity relationship predicted by EHH, and uniquely that this relationship develops within a decade of plant community development, but can be obscured by resource availability.     

Habitat heterogeneity overrides the species–area relationship

Aim The most obvious, although not exclusive, explanation for the increase of species richness with increasing sample area (the species–area relationship) is that species richness is ultimately linked to area-based increases in habitat heterogeneity. The aim of this paper is to examine the relative importance of area and habitat heterogeneity in determining species richness in nature reserves. Specifically, the work tests the hypothesis that species–area relationships are not positive if habitat heterogeneity does not increase with area. Location Sixteen nature reserves (area range 89–11,030 ha) in central Hungary. Methods Four-year faunistic inventories were conducted in the reserves involving c. 70 fieldworkers and 65 taxonomists. CORINE 50,000 land-cover maps were used for calculating the heterogeneity of the reserve landscape (number of habitat types, number of habitat patches and total length of edges). Results Large reserves were less heterogeneous than small reserves, probably because large reserves were established in large blocks of unproductive land whereas small reserves tended to be in more fertile land. In total, 3975 arthropod species were included in the analysis. The slope of the species–area relationship was positive only for Neuroptera and Trichoptera. There was no significant relationship in the other nine taxa examined (Collembola, Acari, Orthoptera, Thysanoptera, Coleoptera, Araneae, Diplopoda, Chilopoda, Diptera). The density (number of species ha⁻¹) of all species, however, showed a positive correlation with heterogeneity. Main conclusions The general lack of fit of species–area relationships in this study is inconsistent with most previous published studies. Importantly, and unlike many other studies, habitat heterogeneity was not correlated with reserve area in the studied system. In the absence of this source of covariation, stronger relationships were identified that suggested a fundamental link between species richness and habitat heterogeneity. The results indicate that habitat heterogeneity rather than area per se is the most important predictor of species richness in the studied system.     

Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures

Aim In a selected literature survey we reviewed studies on the habitat heterogeneity–animal species diversity relationship and evaluated whether there are uncertainties and biases in its empirical support. Location World-wide. Methods We reviewed 85 publications for the period 1960–2003. We screened each publication for terms that were used to define habitat heterogeneity, the animal species group and ecosystem studied, the definition of the structural variable, the measurement of vegetation structure and the temporal and spatial scale of the study. Main conclusions The majority of studies found a positive correlation between habitat heterogeneity/diversity and animal species diversity. However, empirical support for this relationship is drastically biased towards studies of vertebrates and habitats under anthropogenic influence. In this paper, we show that ecological effects of habitat heterogeneity may vary considerably between species groups depending on whether structural attributes are perceived as heterogeneity or fragmentation. Possible effects may also vary relative to the structural variable measured. Based upon this, we introduce a classification framework that may be used for across-studies comparisons. Moreover, the effect of habitat heterogeneity for one species group may differ in relation to the spatial scale. In several studies, however, different species groups are closely linked to ‘keystone structures’ that determine animal species diversity by their presence. Detecting crucial keystone structures of the vegetation has profound implications for nature conservation and biodiversity management.     

Individual heterogeneity and capture–recapture models: what,why and how?

Variation between and within individuals in life history traits is ubiquitous in natural populations. When affecting fitness‐related traits such as survival or reproduction, individual heterogeneity plays a key role in population dynamics and life history evolution. However, it is only recently that properly accounting for individual heterogeneity when studying population dynamics of free‐ranging populations has been made possible through the development of appropriate statistical models. We aim here to review case studies of individual heterogeneity in the context of capture–recapture models for the estimation of population size and demographic parameters with imperfect detection. First, we define what individual heterogeneity means and clarify the terminology used in the literature. Second, we review the literature and illustrate why individual heterogeneity is used in capture–recapture studies by focusing on the detection of life‐history tradeoffs, including senescence. Third, we explain how to model individual heterogeneity in capture–recapture models and provide the code to fit these models ( https://github.com/oliviergimenez/indhet_in_CRmodels ). The distinction is made between situations in which heterogeneity is actually measured and situations in which part of the heterogeneity remains unobserved. Regarding the latter, we outline recent developments of random‐effect models and finite‐mixture models. Finally, we discuss several avenues for future research.     

Water–energy,land‐cover and heterogeneity drivers of the distribution of plant species richness in a mountain region of the European Alps

Aim To evaluate the relative importance of water–energy, land‐cover, environmental heterogeneity and spatial variables on the regional distribution of Red‐Listed and common vascular plant species richness. Location Trento Province (c. 6200 km²) on the southern border of the European Alps (Italy), subdivided regularly into 228 3′ × 5′ quadrants. Methods Data from a floristic inventory were separated into two subsets, representing Red‐Listed and common (i.e. all except Red‐Listed) plant species richness. Both subsets were separately related to water–energy, land‐cover and environmental heterogeneity variables. We simultaneously applied ordinary least squares regression with variation partitioning and hierarchical partitioning, attempting to identify the most important factors controlling species richness. We combined the analysis of environmental variables with a trend surface analysis and a spatial autocorrelation analysis. Results At the regional scale, plant species richness of both Red‐Listed and common species was primarily related to energy availability and land cover, whereas environmental heterogeneity had a lesser effect. The greatest number of species of both subsets was found in quadrants with the largest energy availability and the greatest degree of urbanization. These findings suggest that the elevation range within our study region imposes an energy‐driven control on the distribution of species richness, which resembles that of the broader latitude gradient. Overall, the two species subsets had similar trends concerning the relative importance of water–energy, land cover and environmental heterogeneity, showing a few differences regarding the selection of some predictors of secondary importance. The incorporation of spatial variables did not improve the explanatory power of the environmental models and the high original spatial autocorrelation in the response variables was reduced drastically by including the selected environmental variables. Main conclusions Water–energy and land cover showed significant pure effects in explaining plant species richness, indicating that climate and land cover should both be included as explanatory variables in modelling species richness in human‐affected landscapes. However, the high degree of shared variation between the two groups made the relative effects difficult to separate. The relatively low range of variation in the environmental heterogeneity variables within our sampling domain might have caused the low importance of this complex factor.     

Tree Diversity,Environmental Heterogeneity,and Productivity in a Mexican Tropical Dry Forest1

Species diversity–environmental heterogeneity (D–EH) and species diversity–productivity (D–P) relationships have seldom been analyzed simultaneously even though such analyses could help to understand the processes underlying contrasts in species diversity among sites. Here we analyzed both relationships at a local scale for a highly diverse tropical dry forest of Mexico. We posed the following questions: (1) are environmental heterogeneity and productivity related?; (2) what are the shapes of D–EH and D–P relationships?; (3) what are individual, and interactive, contributions of these two variables to the observed variance in species diversity?; and (4) are patterns affected by sample size, or by partitioning into average local diversity and spatial species turnover? All trees (diameter at breast height ≥5 cm) within twenty‐six 0.2‐ha transects were censused; four environmental variables associated with water availability were combined into an environmental heterogeneity index; aboveground standing biomass was used as a productivity estimator. Simple and multiple linear and nonlinear regression models were run. Environmental heterogeneity and productivity were not correlated. We found consistently positive log‐linear D–EH and D–P relationships. Productivity explained a larger fraction of among‐transect variance in species diversity than did environmental heterogeneity. No effects of sample size were found. Different components of diversity varied in sensitivity to environmental heterogeneity and productivity. Our results suggest that species' differentiation along water availability gradients and species exclusion at the lowest productivity (driest) sites occur simultaneously, independently, and in a scale‐dependent fashion on the tree community of this forest.     

The richness–heterogeneity relationship differs between heterogeneity measures within and among habitats

The positive monotonic relationship between habitat heterogeneity and species richness is a cornerstone of ecology. Recently, it was suggested that this relationship should be unimodal rather than monotonic due to a tradeoff between environmental heterogeneity and population sizes, which increases local species extinctions at high heterogeneity levels. Here, we studied the richness–heterogeneity relationship for an avian community using two different environmental variables, foliage‐height diversity and cover type diversity. We analyzed the richness–heterogeneity within different habitat types (grasslands, savannas, or woodlands) and at the landscape scale. We found strong evidence that both positive and unimodal relationships exist at the landscape scale. Within habitats we found positive relationships between richness and heterogeneity in grasslands and woodlands, and unimodal relationships in savannas. We suggest that the length of the environmental heterogeneity gradient (which is affected by both spatial scale and the environmental variable being analyzed) affects the type of the richness–heterogeneity relationship. We conclude that the type of the relationship between species richness and environmental heterogeneity is non‐ubiquitous, and varies both within and among habitats and environmental variables.     

Beyond the proportional frailty model: Bayesian estimation of individual heterogeneity on mortality parameters

Today, we know that demographic rates can be greatly influenced by differences among individuals in their capacity to survive and reproduce. These intrinsic differences, commonly known as individual heterogeneity, can rarely be measured and are thus treated as latent variables when modeling mortality. Finite mixture models and mixed effects models have been proposed as alternative approaches for inference on individual heterogeneity in mortality. However, in general models assume that individual heterogeneity influences mortality proportionally, which limits the possibility to test hypotheses on the effect of individual heterogeneity on other aspects of mortality such as ageing rates. Here, we propose a Bayesian model that builds upon the mixture models previously developed, but that facilitates making inferences on the effect of individual heterogeneity on mortality parameters other than the baseline mortality. As an illustration, we apply this framework to the Gompertz–Makeham mortality model, commonly used in human and wildlife studies, by assuming that the Gompertz rate parameter is affected by individual heterogeneity. We provide results of a simulation study where we show that the model appropriately retrieves the parameters used for simulation, even for low variances in the heterogeneous parameter. We then apply the model to a dataset on captive chimpanzees and on a cohort life table of 1751 Swedish men, and show how model selection against a null model (i.e., without heterogeneity) can be carried out.     

Effect of migration and environmental heterogeneity on the maintenance of quantitative genetic variation: a simulation study

The paradox of high genetic variation observed in traits under stabilizing selection is a long‐standing problem in evolutionary theory, as mutation rates appear too low to explain observed levels of standing genetic variation under classic models of mutation–selection balance. Spatially or temporally heterogeneous environments can maintain more standing genetic variation within populations than homogeneous environments, but it is unclear whether such conditions can resolve the above discrepancy between theory and observation. Here, we use individual‐based simulations to explore the effect of various types of environmental heterogeneity on the maintenance of genetic variation (V_A) for a quantitative trait under stabilizing selection. We find that V_A is maximized at intermediate migration rates in spatially heterogeneous environments and that the observed patterns are robust to changes in population size. Spatial environmental heterogeneity increased variation by as much as 10‐fold over mutation–selection balance alone, whereas pure temporal environmental heterogeneity increased variance by only 45% at max. Our results show that some combinations of spatial heterogeneity and migration can maintain considerably more variation than mutation–selection balance, potentially reconciling the discrepancy between theoretical predictions and empirical observations. However, given the narrow regions of parameter space required for this effect, this is unlikely to provide a general explanation for the maintenance of variation. Nonetheless, our results suggest that habitat fragmentation may affect the maintenance of V_A and thereby reduce the adaptive capacity of populations.     

Understanding trait diversity: the role of geodiversity

Geodiversity – the abiotic heterogeneity of Earth’s (sub)surface – is gaining recognition for its ecological links to biodiversity. However, theoretical and conceptual knowledge of geodiversity–trait diversity relationships is currently lacking and can improve understanding of abiotic drivers of community assembly. Here we synthesise the state of knowledge of these relationships. We find that some components of geodiversity (e.g., topographic heterogeneity) elicit strong trait responses, whereas other components (e.g., substrate heterogeneity) have marginal effects in driving trait distributions. However, current knowledge is lacking in key aspects, including geodiversity’s effect on trait-specific diversity and intraspecific variation. We call for the explicit inclusion of geodiversity when relating environmental drivers to trait diversity, taking advantage of the increasing availability of trait and geodiversity data.     

Similar Bird Communities Across Grazing Systems in the Nebraska Sandhills

Much of the remaining native rangeland in the Great Plains in the United States is privately owned and managed for beef production, and this single priority for land use may be contributing to declining avian biodiversity through a loss of structural heterogeneity. One proposed solution is to use multiple grazing systems across ranches, under the assumption that this approach will increase heterogeneity of vegetation structure and avian diversity across the landscape. We tested the relationship between grazing systems and avian diversity in the Nebraska Sandhills during 2014 and 2015 on a landscape that included 11 management units containing 5 different grazing systems. We used multivariate models to examine the relationship of bird diversity and communities to grazing systems at the management unit scale, and we used simulations to combine empirical data from ≥1 grazing system into virtual landscapes to test the hypothesis that multiple grazing systems would result in greater heterogeneity. The 5 most common avian species made up 84% of observations (28 species), and songbird richness was 5–6 species/7.06 ha at 53% of our plots. Variation in each of the diversity measures (Shannon diversity range = 0.41–2.2, Simpson's diversity range = 0.24–0.88) was best explained by the previous dormant season's stocking rate, and richness declined by about 1 species/plot with an increase in 1 animal unit month (AUM)/ha. Songbird community structure showed the most variance between management unit, but grazing system explained little community variation. None of the simulated landscapes consistently had greater structural heterogeneity of visual obstruction reading, litter depth, and cover of bare ground than others, and there was a limited level of heterogeneity overall in the simulated landscapes. In contrast to our predictions, a variety of grazing systems did not increase heterogeneity of vegetation structure across the landscape. Thus, conservation practitioners should encourage the use of other strategies to create structural heterogeneity, such as prescribed fires and extreme stocking rates, which will support a diverse grassland songbird community (i.e., a greater variety of bird species) across the landscape. © 2020 The Wildlife Society.     

Plio-Pleistocene climate change and geographic heterogeneity in plant diversity–environment relationships

Plio‐Pleistocene climate change may have induced geographic heterogeneity in plant species richness–environment relationships in Europe due to greater in situ species survival and speciation rates in southern Europe. We formulate distinct hypotheses on how Plio‐Pleistocene climate change may have affected richness–topographic heterogeneity and richness–water‐energy availability relationships, causing steeper relationships in southern Europe. We investigated these hypotheses using data from Atlas Florae Europaeae on the distribution of 3069 species and geographically weighted regression (GWR). Our analyses showed that plant species richness generally increased with topographic heterogeneity (ln‐transformed altitudinal range) and actual evapotranspiration (AET). We also found evidence for strong geographic heterogeneity in the species richness–environment relationship, with a greater increase in species richness with increasing topographic heterogeneity in southern Europe (mean standardized local slope 0.610±0.245 SD in southern Europe, but only 0.270±0.175 SD in northern Europe). However, the local AET slopes were, at most, weakly different between the two regions, and their pattern did not conform to predictions, as there was a band of high local slopes across southern‐central northern Europe. This band broadly matches the transition between the temperate and boreal zones and may simply reflect the fact that few species tolerate the boreal climate. We discuss the potential explanations for the contrasting findings for the two richness–environment relationships. In conclusion, we find support for the idea that Plio‐Pleistocene climate change may sometimes affect current species richness–environment relationships via its effects on regional species pools. However, further studies integrating information on species ages and clade differentiation rates will be needed to substantiate this interpretation. On a general level, our results indicate that although strong richness–environment relationships are often found in macroecological studies, these can be contingent upon the historical constraints on the species pool.     

Capture–recapture models with heterogeneity to study survival senescence in the wild

Detecting senescence in wild populations and estimating its strength raise three challenges. First, in the presence of individual heterogeneity in survival probability, the proportion of high‐survival individuals increases with age. This increase can mask a senescence‐related decrease in survival probability when the probability is estimated at the population level. To accommodate individual heterogeneity we use a mixture model structure (discrete classes of individuals). Second, the study individuals can elude the observers in the field, and their detection rate can be heterogeneous. To account for detectability issues we use capture–mark–recapture (CMR) methodology, mixture models and data that provide information on individuals’ detectability. Last, emigration to non‐monitored sites can bias survival estimates, because it can occur at the end of the individuals’ histories and mimic earlier death. To model emigration we use Markovian transitions to and from an unobservable state. These different model structures are merged together using hidden Markov chain CMR models, or multievent models. Simulation studies illustrate that reliable evidence for survival senescence can be obtained using highly heterogeneous data from non site‐faithful individuals. We then design a tailored application for a dataset from a colony of black‐headed gull Chroicocephalus ridibundus. Survival probabilities do not appear individually variable, but evidence for survival senescence becomes significant only when accounting for other sources of heterogeneity. This result suggests that not accounting for heterogeneity leads to flawed inference and/or that emigration heterogeneity mimics survival heterogeneity and biases senescence estimates.     

Hydrologic sources of carbon cycling uncertainty throughout the terrestrial–aquatic continuum

Linking hydrologic interactions with global carbon cycling will reduce the uncertainty associated with scaling-up empirical studies and facilitate the incorporation of terrestrial–aquatic linkages within global and regional change models. Much of the uncertainty in estimates of carbon fluxes associated with precipitation and hydrologic transport results from the extensive spatial and temporal heterogeneity in both intrinsic functioning and anthropogenic modification of hydrological cycles. To better understand this variation we developed a landscape ecological approach to coupled hydrologic–carbon cycling that merges local mechanisms with multiple-scale spatial heterogeneity. This spatially explicit framework is applied to examine variability in hydrologic influences on carbon cycling along a continental scale water availability gradient with an explicit consideration of human sources of variability. Hydrologic variation is an important component of the uncertainty in carbon cycling; accounting for this variation will improve understanding of current conditions and projections of future ecosystem responses to global change.