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Abstract Although dispersal is generally viewed as a crucial determinant for the fitness of any organism, our understanding of its role in the persistence and spread of plant populations remains incomplete. Generalizing and predicting dispersal processes are challenging due to context dependence of seed dispersal, environmental heterogeneity and interdependent processes occurring over multiple spatial and temporal scales. Current population models often use simple phenomenological descriptions of dispersal processes, limiting their ability to examine the role of population persistence and spread, especially under global change. To move seed dispersal ecology forward, we need to evaluate the impact of any single seed dispersal event within the full spatial and temporal context of a plant’s life history and environmental variability that ultimately influences a population’s ability to persist and spread. In this perspective, we provide guidance on integrating empirical and theoretical approaches that account for the context dependency of seed dispersal to improve our ability to generalize and predict the consequences of dispersal, and its anthropogenic alteration, across systems. We synthesize suitable theoretical frameworks for this work and discuss concepts, approaches and available data from diverse subdisciplines to help operationalize concepts, highlight recent breakthroughs across research areas and discuss ongoing challenges and open questions. We address knowledge gaps in the movement ecology of seeds and the integration of dispersal and demography that could benefit from such a synthesis. With an interdisciplinary perspective, we will be able to better understand how global change will impact seed dispersal processes, and potential cascading effects on plant population persistence, spread and biodiversity. 

Abstract Species often respond to human‐caused climate change by shifting where they occur on the landscape. To anticipate these shifts, we need to understand the forces that determine where species currently occur. We tested whether a long‐hypothesised trade‐off between climate and competitive constraints explains where tree species grow on mountain slopes. Using tree rings, we reconstructed growth sensitivity to climate and competition in range centre and range margin tree populations in three climatically distinct regions. We found that climate often constrains growth at environmentally harsh elevational range boundaries, and that climatic and competitive constraints trade‐off at large spatial scales. However, there was less evidence that competition consistently constrained growth at benign elevational range boundaries; thus, local‐scale climate‐competition trade‐offs were infrequent. Our work underscores the difficulty of predicting local‐scale range dynamics, but suggests that the constraints on tree performance at a large‐scale (e.g. latitudinal) may be predicted from ecological theory. 

Abstract Seed dispersal enables plants to reach hospitable germination sites and escape natural enemies. Understanding when and how much seed dispersal matters to plant fitness is critical for understanding plant population and community dynamics. At the same time, the complexity of factors that determine if a seed will be successfully dispersed and subsequently develop into a reproductive plant is daunting. Quantifying all factors that may influence seed dispersal effectiveness for any potential seed-vector relationship would require an unrealistically large amount of time, materials and financial resources. On the other hand, being able to make dispersal predictions is critical for predicting whether single species and entire ecosystems will be resilient to global change. Building on current frameworks, we here posit that seed dispersal ecology should adopt plant functional groups as analytical units to reduce this complexity to manageable levels. Functional groups can be used to distinguish, for their constituent species, whether it matters (i) if seeds are dispersed, (ii) into what context they are dispersed and (iii) what vectors disperse them. To avoid overgeneralization, we propose that the utility of these functional groups may be assessed by generating predictions based on the groups and then testing those predictions against species-specific data. We suggest that data collection and analysis can then be guided by robust functional group definitions. Generalizing across similar species in this way could help us to better understand the population and community dynamics of plants and tackle the complexity of seed dispersal as well as its disruption. 

Summary Large intraspecific functional trait variation strongly impacts many aspects of communities and ecosystems, and is the medium upon which evolution works. Yet intraspecific trait variation is inconsistent and hard to predict across traits, species and locations.We measured within‐species variation in leaf mass per area (LMA), leaf dry matter content (LDMC), branch wood density (WD), and allocation to stem area vs leaf area in branches (branch Huber value (HV)) across the aridity range of seven Australian eucalypts and a co‐occurringAcaciaspecies to explore how traits and their variances change with aridity.Within species, we found consistent increases in LMA, LDMC and WD and HV with increasing aridity, resulting in consistent trait coordination across leaves and branches. However, this coordination only emerged across sites with large climate differences. Unlike trait means, patterns of trait variance with aridity were mixed across populations and species. Only LDMC showed constrained trait variation in more xeric species and drier populations that could indicate limits to plasticity or heritable trait variation.Our results highlight that climate can drive consistent within‐species trait patterns, but that patterns might often be obscured by the complex nature of morphological traits, sampling incomplete species ranges or sampling confounded stress gradients. 

Abstract Indirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates. 

Abstract AimThe International Tree‐Ring Data Bank (ITRDB) is the most comprehensive database of tree growth. To evaluate its usefulness and improve its accessibility to the broad scientific community, we aimed to: (a) quantify its biases, (b) assess how well it represents global forests, (c) develop tools to identify priority areas to improve its representativity, and d) make available the corrected database. LocationWorldwide. Time periodContributed datasets between 1974 and 2017. Major taxa studiedTrees. MethodsWe identified and corrected formatting issues in all individual datasets of theITRDB. We then calculated the representativity of theITRDBwith respect to species, spatial coverage, climatic regions, elevations, need for data update, climatic limitations on growth, vascular plant diversity, and associated animal diversity. We combined these metrics into a global Priority Sampling Index (PSI) to highlight ways to improveITRDBrepresentativity. ResultsOur refined dataset provides access to a network of >52 million growth data points worldwide. We found, however, that the database is dominated by trees from forests with low diversity, in semi‐arid climates, coniferous species, and in western North America. Conifers represented 81% of theITRDBand even in well‐sampled areas, broadleaves were poorly represented. OurPSIstressed the need to increase the database diversity in terms of broadleaf species and identified poorly represented regions that require scientific attention. Great gains will be made by increasing research and data sharing in African, Asian, and South American forests. Main conclusionsThe extensive data and coverage of theITRDBshow great promise to address macroecological questions. To achieve this, however, we have to overcome the significant gaps in the representativity of theITRDB. A strategic and organized group effort is required, and we hope the tools and data provided here can guide the efforts to improve this invaluable database. 

Abstract The utility of plant functional traits for predictive ecology relies on our ability to interpret trait variation across multiple taxonomic and ecological scales. Using extensive data sets of trait variation within species, across species and across communities, we analysed whether and at what scales leaf economics spectrum (LES) traits show predicted trait–trait covariation. We found that most variation inLEStraits is often, but not universally, at high taxonomic levels (between families or genera in a family). However, we found that trait covariation shows distinct taxonomic scale dependence, with some trait correlations showing opposite signs within vs. across species.LEStraits responded independently to environmental gradients within species, with few shared environmental responses across traits or across scales. We conclude that, at small taxonomic scales, plasticity may obscure or reverse the broad evolutionary linkages between leaf traits, meaning that variation inLEStraits cannot always be interpreted as differences in resource use strategy.