Metamorphosis in river ecology: from reaches to macrosystems

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Integrating broad‐scale data to assess demographic and climatic contributions to population change in a declining songbird

Climate variation and trends affect species distribution and abundance across large spatial extents. However, most studies that predict species response to climate are implemented at small spatial scales or are based on occurrence‐environment relationships that lack mechanistic detail. Here, we develop an integrated population model (IPM) for multi‐site count and capture‐recapture data for a declining migratory songbird, Wilson's warbler (Cardellina pusilla), in three genetically distinct breeding populations in western North America. We include climate covariates of vital rates, including spring temperatures on the breeding grounds, drought on the wintering range in northwest Mexico, and wind conditions during spring migration. Spring temperatures were positively related to productivity in Sierra Nevada and Pacific Northwest genetic groups, and annual changes in productivity were important predictors of changes in growth rate in these populations. Drought condition on the wintering grounds was a strong predictor of adult survival for coastal California and Sierra Nevada populations; however, adult survival played a relatively minor role in explaining annual variation in population change. A latent parameter representing a mixture of first‐year survival and immigration was the largest contributor to variation in population change; however, this parameter was estimated imprecisely, and its importance likely reflects, in part, differences in spatio‐temporal distribution of samples between count and capture‐recapture data sets. Our modeling approach represents a novel and flexible framework for linking broad‐scale multi‐site monitoring data sets. Our results highlight both the potential of the approach for extension to additional species and systems, as well as needs for additional data and/or model development.     

A network approach for inferring species associations from co‐occurrence data

Positive and negative associations between species are a key outcome of community assembly from regional species pools. These associations are difficult to detect and can be caused by a range of processes such as species interactions, local environmental constraints and dispersal. We integrate new ideas around species distribution modeling, covariance matrix estimation, and network analysis to provide an approach to inferring non‐random species associations from local‐ and regional‐scale occurrence data. Specifically, we provide a novel framework for identifying species associations that overcomes three challenges: 1) correcting for indirect effects from other species, 2) avoiding spurious associations driven by regional‐scale distributions, and 3) describing these associations in a multi‐species context. We highlight a range of research questions and analyses that this framework is able to address. We show that the approach is statistically robust using simulated data. In addition, we present an empirical analysis of > 1000 North American tree communities that gives evidence for weak positive associations among small groups of species. Finally, we discuss several possible extensions for identifying drivers of associations, predicting community assembly, and better linking biogeography and community ecology.     

Species and community responses to short‐term climate manipulation: Microarthropods in the sub‐Antarctic

Abstract: Both species and community‐level investigations are important for understanding the biotic impacts of climate change, because current evidence suggests that individual species responses are idiosyncratic. However, few studies of climate change impacts have been conducted on entire terrestrial arthropod communities living in the same habitat in the southern Hemisphere, and the effects of precipitation changes on them are particularly poorly understood. Here we investigate the species‐ and community‐level responses of microarthropods inhabiting a keystone plant species, on sub‐Antarctic Marion Island, to experimental reduction in precipitation, warming and shading. These climate manipulations were chosen based on observed climate trends and predicted indirect climate change impacts on this system. The dry‐warm and shade inducing treatments that were imposed effected significant species‐ and community‐level responses after a single year. Although the strongest community‐level trends included a dramatic decline in springtail abundance and total biomass under the dry‐warm and shade treatments, species responses were generally individualistic, that is, springtails responded differently to mites, and particular mite and springtail species responded differently to each other. Our results therefore provide additional support for the dynamic rather than static model for community responses to climate change, in the first such experiment in the sub‐Antarctic. In conclusion, these results show that an ongoing decline in precipitation and increase in temperature is likely to have dramatic direct and indirect effects on this microarthropod community. Moreover, they indicate that while at a broad scale it may be possible to make generalizations regarding species responses to climate change, these generalizations are unlikely to translate into predictable effects at the community level.     

Metabolic approaches to understanding climate change impacts on seasonal host‐macroparasite dynamics

Climate change is expected to alter the dynamics of infectious diseases around the globe. Predictive models remain elusive due to the complexity of host–parasite systems and insufficient data describing how environmental conditions affect various system components. Here, we link host–macroparasite models with the Metabolic Theory of Ecology, providing a mechanistic framework that allows integrating multiple nonlinear environmental effects to estimate parasite fitness under novel conditions. The models allow determining the fundamental thermal niche of a parasite, and thus, whether climate change leads to range contraction or may permit a range expansion. Applying the models to seasonal environments, and using an arctic nematode with an endotherm host for illustration, we show that climate warming can split a continuous spring‐to‐fall transmission season into two separate transmission seasons with altered timings. Although the models are strategic and most suitable to evaluate broad‐scale patterns of climate change impacts, close correspondence between model predictions and empirical data indicates model applicability also at the species level. As the application of Metabolic Theory considerably aids the a priori estimation of model parameters, even in data‐sparse systems, we suggest that the presented approach could provide a framework for understanding and predicting climatic impacts for many host–parasite systems worldwide.     

Differences in shifts of wintering and breeding ranges lead to changing migration distances in European birds

Studies on the impact of climate change on the distributions of bird species in Europe have largely focused on one season at a time, especially concerning summer breeding ranges. We investigated whether migratory bird species show consistent range shifts over the past 55 yr in both breeding and wintering areas or if these shifts are independent. We then analyzed whether patterns in changing migration distances of Finnish breeding birds could be explained by habitat use, phylogeny or body size. We used long‐term datasets from the Finnish ringing centre to analyze the mean wintering latitudes of 29 species of Finnish breeding birds, then used breeding distribution data to make predictions as to whether certain species were migrating shorter or longer distances based on the comparative shifts in the wintering and breeding grounds. Our data reveal species‐specific differences in changing migration distances. We show that for many species, long‐term shifts in wintering ranges have not followed the same patterns as those in the breeding range, leading to differences in migration distances over time. We conclude that species are not adjusting predictably to climate change in their wintering grounds, leading to changing migration distances in some, but not all, species breeding in Finland. This research fills an important gap in the current climate change biology literature, focusing on individuals’ entire life histories and revealing new complexities in range shift patterns.     

Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data

Increasing tree mortality from global change drivers such as drought and biotic infestations is a widespread phenomenon, including in the boreal zone where climate changes and feedbacks to the Earth system are relatively large. Despite the importance for science and management communities, our ability to forecast tree mortality at landscape to continental scales is limited. However, two independent information streams have the potential to inform and improve mortality forecasts: repeat forest inventories and satellite remote sensing. Time series of tree‐level growth patterns indicate that productivity declines and related temporal dynamics often precede mortality years to decades before death. Plot‐level productivity, in turn, has been related to satellite‐based indices such as the Normalized difference vegetation index (NDVI). Here we link these two data sources to show that early warning signals of mortality are evident in several NDVI‐based metrics up to 24 years before death. We focus on two repeat forest inventories and three NDVI products across western boreal North America where productivity and mortality dynamics are influenced by periodic drought. These data sources capture a range of forest conditions and spatial resolution to highlight the sensitivity and limitations of our approach. Overall, results indicate potential to use satellite NDVI for early warning signals of mortality. Relationships are broadly consistent across inventories, species, and spatial resolutions, although the utility of coarse‐scale imagery in the heterogeneous aspen parkland was limited. Longer‐term NDVI data and annually remeasured sites with high mortality levels generate the strongest signals, although we still found robust relationships at sites remeasured at a typical 5 year frequency. The approach and relationships developed here can be used as a basis for improving forest mortality models and monitoring systems.     

Sequence capture using AFLP‐generated baits: A cost‐effective method for high‐throughput phylogenetic and phylogeographic analysis

Target sequence capture is an efficient technique to enrich specific genomic regions for high‐throughput sequencing in ecological and evolutionary studies. In recent years, many sequence capture approaches have been proposed, but most of them rely on commercial synthetic baits which make the experiment expensive. Here, we present a novel sequence capture approach called AFLP‐based genome sequence capture (AFLP Capture). This method uses the AFLP (amplified fragment length polymorphism) technique to generate homemade capture baits without the need for prior genome information, thus is applicable to any organisms. In this approach, biotinylated AFLP fragments representing a random fraction of the genome are used as baits to capture the homologous fragments from genomic shotgun sequencing libraries. In a trial study, by using AFLP Capture, we successfully obtained 511 orthologous loci (>700,000 bp in total length) from 11 Odorrana species and more than 100,000 single nucleotide polymorphisms (SNPs) in four analyzed individuals of an Odorrana species. This result shows that our method can be used to address questions of various evolutionary depths (from interspecies level to intraspecies level). We also discuss the flexibility in bait preparation and how the sequencing data are analyzed. In summary, AFLP Capture is a rapid and flexible tool and can significantly reduce the experimental cost for phylogenetic studies that require analyzing genome‐scale data (hundreds or thousands of loci).     

A trait‐based approach for predicting species responses to environmental change from sparse data: how well might terrestrial mammals track climate change?

Estimating population spread rates across multiple species is vital for projecting biodiversity responses to climate change. A major challenge is to parameterise spread models for many species. We introduce an approach that addresses this challenge, coupling a trait‐based analysis with spatial population modelling to project spread rates for 15 000 virtual mammals with life histories that reflect those seen in the real world. Covariances among life‐history traits are estimated from an extensive terrestrial mammal data set using Bayesian inference. We elucidate the relative roles of different life‐history traits in driving modelled spread rates, demonstrating that any one alone will be a poor predictor. We also estimate that around 30% of mammal species have potential spread rates slower than the global mean velocity of climate change. This novel trait‐space‐demographic modelling approach has broad applicability for tackling many key ecological questions for which we have the models but are hindered by data availability.     

Changing the way we think about global change research: scaling up in experimental ecosystem science

Scaling is a naturally iterative and bi‐directional component of problem solving in ecology and in climate science. Ecosystems and climate systems are unquestionably the sum of all their parts, to the smallest imaginable scale, in genomic processes or in the laws of fluid dynamics. However, in the process of scaling‐up, for practical purposes thewhole usually has to be construed as a good deal less than this. This essay demonstrates how controlled large‐scale experiments can be used to deduce key mechanisms and thereby reduce much of the detail needed for the process of scaling‐up. Collection of the relevant experimental evidence depends on controlling the environment and complexity of experiments, and on applications of technologies that report on, and integrate, small‐scale processes. As the role of biological feedbacks in the behavior of climate systems is better appreciated, so the need grows for experimentally based understanding of ecosystem processes. We argue that we cannot continue as we are doing, simply observing the progress of the greenhouse gas‐driven experiment in global change, and modeling its future outcomes. We have to change the way we think about climate system and ecosystem science, and in the process move to experimental modes at larger scales than previously thought achievable.     

How landscape dynamics link individual‐ to population‐level movement patterns: a multispecies comparison of ungulate relocation data

Aim To demonstrate how the interrelations of individual movements form large‐scale population‐level movement patterns and how these patterns are associated with the underlying landscape dynamics by comparing ungulate movements across species. Locations Arctic tundra in Alaska and Canada, temperate forests in Massachusetts, Patagonian Steppes in Argentina, Eastern Steppes in Mongolia. Methods We used relocation data from four ungulate species (barren‐ground caribou, Mongolian gazelle, guanaco and moose) to examine individual movements and the interrelation of movements among individuals. We applied and developed a suite of spatial metrics that measure variation in movement among individuals as population dispersion, movement coordination and realized mobility. Taken together, these metrics allowed us to quantify and distinguish among different large‐scale population‐level movement patterns such as migration, range residency and nomadism. We then related the population‐level movement patterns to the underlying landscape vegetation dynamics via long‐term remote sensing measurements of the temporal variability, spatial variability and unpredictability of vegetation productivity. Results Moose, which remained in sedentary home ranges, and guanacos, which were partially migratory, exhibited relatively short annual movements associated with landscapes having very little broad‐scale variability in vegetation. Caribou and gazelle performed extreme long‐distance movements that were associated with broad‐scale variability in vegetation productivity during the peak of the growing season. Caribou exhibited regular seasonal migration in which individuals were clustered for most of the year and exhibited coordinated movements. In contrast, gazelle were nomadic, as individuals were independently distributed and moved in an uncoordinated manner that relates to the comparatively unpredictable (yet broad‐scale) vegetation dynamics of their landscape. Main conclusions We show how broad‐scale landscape unpredictability may lead to nomadism, an understudied type of long‐distance movement. In contrast to classical migration where landscapes may vary at broad scales but in a predictable manner, long‐distance movements of nomadic individuals are uncoordinated and independent from other such individuals. Landscapes with little broad‐scale variability in vegetation productivity feature smaller‐scale movements and allow for range residency. Nomadism requires distinct integrative conservation strategies that facilitate long‐distance movements across the entire landscape and are not limited to certain migration corridors.     

Spatial modelling of species turnover identifies climate ecotones,climate change tipping points and vulnerable taxonomic groups

There is an expectation that climate change will drive turnover in the composition of ecological communities. Established methods for predicting the degree of turnover and spatial areas and taxonomic groups that will be most affected from real data are lacking. We tested a combination of spatial modelling tools to make these predictions. Using data from systematic vegetation survey plots from the Adelaide Geosyncline region, southern Australia, we modelled species turnover as a function of bioclimatic and geographic distances and predicted turnover using future climate change scenarios for 2030 and 2070. We conducted bioclimatic gradient analysis (CCA) on species composition data and mapped zones of higher turnover. The method for detecting these zones was tested using a simulation of continuous turnover. A phylogeny was generated for recorded species and correlations of occurrences of phylogenetic groups with species turnover were calculated. Significant turnover was predicted for the least severe climate change scenarios and near‐complete species turnover for the most severe scenario. Gradient analysis revealed discrete transitional zones with more rapid turnover, which were interpreted as a mesic–arid ecotone. Turnover occurred at family level and with increasing temperature and decreasing rainfall there was a shift from the prevalence of Ericaceae, Myrtaceae, Haloragaceae, Cyperaceae, and Xanthorrhoeaceae to that of Amaranthaceae, Malvaceae, Scrophulariaceae, Sapindaceae, and Solanaceae. The mesic end of this climate gradient had relatively low rates of turnover and was interpreted as a refugium with a tipping point. The translation of spatial patterns to temporal change is dependent partly upon scales at which community assembly processes operate and predicts relative vulnerability, but not rates of change, which can only be measured through monitoring. The approach can be applied at any spatial or taxonomic scale subject to sufficient data resolution and can inform management decisions as to biases in climate change risks.     

Palaeolimnological insights for biodiversity science: an emerging field

1. Decreases in biodiversity are so widespread that they are now considered a form of global change in their own right. Given the grave nature of this issue, rapid advances in understanding are needed to mitigate further impacts. In this Opinion paper, we argue that palaeolimnological studies have important contributions to make to biodiversity science. 2. Given that long‐term community data are sparse in their geographic coverage and tend to span no more than 5 years, greater insight into biodiversity dynamics can be obtained from palaeoecological analyses. One such approach is palaeolimnology, which is a field that can provide long‐term data on changes in both physico‐chemical and biological components of lake ecosystems. 3. To date, a handful of quantitative palaeolimnological studies have addressed biodiversity questions, focussing primarily on defining the drivers of change in species richness or identifying functional traits that best capture ecosystem processes. Several studies have also quantified the role of spatial variables in determining assemblage structure, a necessary first step in addressing how metacommunity interactions influence biodiversity–ecosystem processes. Overall, these early studies show that palaeolimnological approaches can address both similar and novel questions compared with contemporary ecological studies. However, palaeolimnology allows for a great expansion of the temporal scale of investigation, the quantification of rates of change to stressors and the possibility of conducting experiments by applying resurrection techniques. 4. As an emerging field, there are numerous exciting applications of palaeolimnology to biodiversity science. It is an opportune time to create synergy between contemporary aquatic ecologists and palaeolimnologists.     

Land use and life history limit migration capacity of eastern tree species

Aim

Temperate tree species overwhelmingly responded to past climate change by migrating rather than adapting. However, past climate change did not have the modern human‐driven patterns of land use and fragmentation, raising questions of whether tree migration will still be able to keep pace with climate. Previous studies using coarse‐grained or randomized landscapes suggest that dispersal may be delayed but have not identified outright barriers to migration. Here, we use real‐world fragmented landscapes at the scale of forest stands to assess the migration capacity of eastern tree species.

Location

Eastern U.S.A.

Time period

Present day to 2100.

Major taxa studied

Eastern U.S. trees.

Methods

We simulated dispersal over 100 years for 15 species common to the mid‐Atlantic region and that are predicted to gain suitable habitat in the northeast. In contrast to previous studies, we incorporated greater realism with species‐specific life histories and real‐world spatial configurations of anthropogenic land use. We used simulation results to calculate dispersal rates for each species and related these to predicted rates of species habitat shift.

Results

Our simulations suggest that land use in the human‐dominated east‐coast corridor slows species dispersal rates by 12–40% and may prevent keeping pace with climate. Species most impacted by anthropogenic land use were often those with the highest predicted species habitat shifts. We identified two major dispersal barriers, the Washington DC metropolitan area and central NY, that severely impeded tree migration.

Main conclusions

Patterns of anthropogenic land use not only slowed migration but also resulted in effective barriers to dispersal. These impacts were exacerbated by tree life histories, such as long ages to maturity and narrow dispersal kernels. Without intervention, the migration lags predicted here may lead to loss in biodiversity and ecosystem functions as current forest species decline, and may contribute to formation of novel communities.     

US forest response to projected climate‐related stress: a tolerance perspective

Although it is widely recognized that climate change will require a major spatial reorganization of forests, our ability to predict exactly how and where forest characteristics and distributions will change has been rather limited. Current efforts to predict future distribution of forested ecosystems as a function of climate include species distribution models (for fine‐scale predictions) and potential vegetation climate envelope models (for coarse‐grained, large‐scale predictions). Here, we develop and apply an intermediate approach wherein we use stand‐level tolerances of environmental stressors to understand forest distributions and vulnerabilities to anticipated climate change. In contrast to other existing models, this approach can be applied at a continental scale while maintaining a direct link to ecologically relevant, climate‐related stressors. We first demonstrate that shade, drought, and waterlogging tolerances of forest stands are strongly correlated with climate and edaphic conditions in the conterminous United States. This discovery allows the development of a tolerance distribution model (TDM), a novel quantitative tool to assess landscape level impacts of climate change. We then focus on evaluating the implications of the drought TDM. Using an ensemble of 17 climate change models to drive this TDM, we estimate that 18% of US ecosystems are vulnerable to drought‐related stress over the coming century. Vulnerable areas include mostly the Midwest United States and Northeast United States, as well as high‐elevation areas of the Rocky Mountains. We also infer stress incurred by shifting climate should create an opening for the establishment of forest types not currently seen in the conterminous United States.     

Individual‐based ecology of coastal birds

Conservation objectives for non‐breeding coastal birds (shorebirds and wildfowl) are determined from their population size at coastal sites. To advise coastal managers, models must predict quantitatively the effects of environmental change on population size or the demographic rates (mortality and reproduction) that determine it. As habitat association models and depletion models are not able to do this, we developed an approach that has produced such predictions thereby enabling policy makers to make evidence‐based decisions. Our conceptual framework is individual‐based ecology, in which populations are viewed as having properties (e.g. size) that arise from the traits (e.g. behaviour, physiology) and interactions of their constituent individuals. The link between individuals and populations is made through individual‐based models (IBMs) that follow the fitness‐maximising decisions of individuals and predict population‐level consequences (e.g. mortality rate) from the fates of these individuals. Our first IBM was for oystercatchers Haematopus ostralegus and accurately predicted their density‐dependent mortality. Subsequently, IBMs were developed for several shorebird and wildfowl species at several European sites, and were shown to predict accurately overwinter mortality, and the foraging behaviour from which predictions are derived. They have been used to predict the effect on survival in coastal birds of sea level rise, habitat loss, wind farm development, shellfishing and human disturbance. This review emphasises the wider applicability of the approach, and identifies other systems to which it could be applied. We view the IBM approach as a very useful contribution to the general problem of how to advance ecology to the point where we can routinely make meaningful predictions of how populations respond to environmental change.     

A meta‐analysis of dispersal in butterflies

Dispersal has recently gained much attention because of its crucial role in the conservation and evolution of species facing major environmental changes such as habitat loss and fragmentation, climate change, and their interactions. Butterflies have long been recognized as ideal model systems for the study of dispersal and a huge amount of data on their ability to disperse has been collected under various conditions. However, no single ‘best’ method seems to exist leading to the co‐occurrence of various approaches to study butterfly mobility, and therefore a high heterogeneity among data on dispersal across this group. Accordingly, we here reviewed the knowledge accumulated on dispersal and mobility in butterflies, to detect general patterns. This meta‐analysis specifically addressed two questions. Firstly, do the various methods provide a congruent picture of how dispersal ability is distributed across species? Secondly, is dispersal species‐specific? Five sources of data were analysed: multisite mark‐recapture experiments, genetic studies, experimental assessments, expert opinions, and transect surveys. We accounted for potential biases due to variation in genetic markers, sample sizes, spatial scales or the level of habitat fragmentation. We showed that the various dispersal estimates generally converged, and that the relative dispersal ability of species could reliably be predicted from their relative vagrancy (records of butterflies outside their normal habitat). Expert opinions gave much less reliable estimates of realized dispersal but instead reflected migration propensity of butterflies. Within‐species comparisons showed that genetic estimates were relatively invariable, while other dispersal estimates were highly variable. This latter point questions dispersal as a species‐specific, invariant trait.     

Evolution of natural history information in the 21st century – developing an integrated framework for biological and geographical data

Threats to marine and estuarine species operate over many spatial scales, from nutrient enrichment at the watershed/estuarine scale to invasive species and climate change at regional and global scales. To help address research questions across these scales, we provide here a standardized framework for a biogeographical information system containing queriable biological data that allows extraction of information on multiple species, across a variety of spatial scales based on species distributions, natural history attributes and habitat requirements. As scientists shift from research on localized impacts on individual species to regional and global scale threats, macroecological approaches of studying multiple species over broad geographical areas are becoming increasingly important. The standardized framework described here for capturing and integrating biological and geographical data is a critical first step towards addressing these macroecological questions and we urge organizations capturing biogeoinformatics data to consider adopting this framework.     

Using intra‐individual variation in shrub architecture to explain population cover

Plant architecture is related to the performance of long‐lived plants; its role in promoting species coexistence and in successional patterns is now widely recognized. However, because plant architecture involves branching processes, it is highly variable at the intra‐specific level. In this paper, we address two questions: what is the best way to describe plant architecture to obtain meaningful information for explaining population cover: at the whole‐plant level, or at the level of its unitary constituent parts? Further, are there architectural designs related to populations’ success? We evaluated the relative impact of ontogeny and whole‐plant traits on the cover achieved by the populations of five shrub species developing on 25 abandoned farmlands in southwestern Québec (Canada). We compared four ways of analyzing plant architecture: 1–2) using morphological traits described at the scale of a module (an elementary architectural unit made up of all the different types of shoots), with or without taking into account the ontogeny of the whole organism, 3) using the rate of changes during ontogeny as traits, and 4) using whole‐plant traits describing branching processes at a scale larger than modules. We then used variation partitioning to discriminate the actual effects of these traits on percent cover of the species from hidden effects due to plant ontogenesis and population spatial structure. Our results suggest that the predominant variables that effectively describe population cover vary from one species to another. At the same time, whole‐plant architectural traits and the rate of change of morphological traits during ontogeny both have an important effect on population cover. These findings suggest that acknowledging the developmental pattern of woody species can clarify the impact of intra‐specific trait variation on population cover.