Assessing the vulnerability of plant functional trait strategies to climate change

Aim: Our ability to understand how species may respond to changing climate conditions is hampered by a lack of high-quality data on the adaptive capacity of species. Plant functional traits are linked to many aspects of species life history and adaptation to environment, with different combinations of trait values reflecting alternate strategies for adapting to varied conditions. If the realized climate limits of species can be partially explained by plant functional trait combinations, then a new approach of using trait combinations to predict the expected climate limits of species trait combinations may offer considerable benefits. Location: Australia. Time period: Current and future. Methods: Using trait data for leaf size, seed mass and plant height for 6,747 Australian native species from 27 plant families, we model the expected climate limits of trait combinations and use future climate scenarios to estimate climate change impacts based on plant functional trait strategies. Results: Functional trait combinations were a significant predictor of species climate niche metrics with potentially meaningful relationships with two rainfall variables (R2 =.36 &.45) and three temperature variables (R2 =.21,.28,.30). Using this method, the proportion of species exposed to conditions across their range that are beyond the expected climate limits of their trait strategies will increase under climate change. Main conclusions: Our new approach, called trait strategy vulnerability, includes three new metrics. For example, the climate change vulnerability (CCV) metric identified a small but important proportion of species (4.3%) that will on average be exposed to conditions beyond their expected limits for summer temperature in the future. These potentially vulnerable species could be high priority targets for deeper assessment of adaptive capacity at the genomic or physiological level. Our methods can be applied to any suite of co-occurring plants globally

Integrating modelling of biodiversity composition and ecosystem function

There is increasing reliance on ecological models to improve our understanding of how ecological systems work, to project likely outcomes under alternative global change scenarios and to help develop robust management strategies. Two common types of spatiotemporally explicit ecological models are those focussed on biodiversity composition and those focussed on ecosystem function. These modelling disciplines are largely practiced separately, with separate literature, despite growing evidence that natural systems are shaped by the interaction of composition and function. Here we call for the development of new modelling approaches that integrate composition and function, accounting for the important interactions between these two dimensions, particularly under rapid global change. We examine existing modelling approaches that have begun to combine elements of composition and function, identifying their potential contribution to fully integrated modelling approaches. The development and application of integrated models of composition and function face a number of important challenges, including biological data limitations, system knowledge and computational constraints. We suggest a range of promising avenues that could help researchers overcome these challenges, including the use of virtual species, macroecological relationships and hybrid correlative-mechanistic modelling. Explicitly accounting for the interactions between composition and function within integrated modelling approaches has the potential to improve our understanding of ecological systems, provide more accurate predictions of their future states and transform their management

Patterns and drivers of plant diversity across Australia

Biodiversity analyses across continental extents are important in providing comprehensive information on patterns and likely drivers of diversity. For vascular plants in Australia, community-level diversity analyses have been restricted by the lack of a consistent plot-based survey dataset across the continent. To overcome these challenges, we collated and harmonised plot-based vegetation survey data from the major data sources across Australia and used them as the basis for modelling species richness (α-diversity) and community compositional dissimilarity (β-diversity), standardised to 400 m2, with the aim of mapping diversity patterns and identifying potential environmental drivers. The harmonised Australian vegetation plot (HAVPlot) dataset includes 219 552 plots, of which we used 115 083 to analyse plant diversity. Models of species richness and compositional dissimilarity both explained approximately one-third of the variation in plant diversity across Australia (D2 = 33.0% and 32.7%, respectively). The strongest environmental predictors for both aspects of diversity were a combination of temperature and precipitation, with soil texture and topographic heterogeneity also important. The fine-resolution (≈ 90 m) spatial predictions of species richness and compositional dissimilarity identify areas expected to be of particular importance for plant diversity, including south-western Australia, rainforests of eastern Australia and the Australian Alps. Arid areas of central and western Australia are predicted to support assemblages that are less speciose or unique; however, these areas are most in need of additional survey data to fill the spatial, environmental and taxonomic gaps in the HAVPlot dataset. The harmonised data and model predictions presented here provide new insight into plant diversity patterns across Australia, enabling a wide variety of future research, such as exploring changes in species abundances, linking compositional patterns to functional traits or undertaking conservation assessments for selected components of the flora

The role of climate and plant functional trade-offs in shaping global biome and biodiversity patterns

Aim: Two of the oldest observations in plant geography are the increase in plant diversity from the poles towards the tropics and the global geographic distribution of vegetation physiognomy (biomes). The objective of this paper is to use a process-based vegetation model to evaluate the relationship between modelled and observed global patterns of plant diversity and the geographic distribution of biomes.Location: The global terrestrial biosphere.Methods: We implemented and tested a novel vegetation model aimed at identifying strategies that enable plants to grow and reproduce within particular climatic conditions across the globe. Our model simulates plant survival according to the fundamental ecophysiological processes of water uptake, photosynthesis, reproduction and phenology. We evaluated the survival of an ensemble of 10,000 plant growth strategies across the range of global climatic conditions. For the simulated regional plant assemblages we quantified functional richness, functional diversity and functional identity.Results: A strong relationship was found (correlation coefficient of 0.75) between the modelled and the observed plant diversity. Our approach demonstrates that plant functional dissimilarity increases and then saturates with increasing plant diversity. Six of the major Earth biomes were reproduced by clustering grid cells according to their functional identity (mean functional traits of a regional plant assemblage). These biome clusters were in fair agreement with two other global vegetation schemes: a satellite image classification and a biogeography model (kappa statistics around 0.4).Main conclusions: Our model reproduces the observed global patterns of plant diversity and vegetation physiognomy from the number and identity of simulated plant growth strategies. These plant growth strategies emerge from the first principles of climatic constraints and plant functional trade-offs. Our study makes important contributions to furthering the understanding of how climate affects patterns of plant diversity and vegetation physiognomy from a process-based rather than a phenomenological perspective

The capacity of refugia for conservation planning under climate change

Refugia – areas that may facilitate the persistence of species during large-scale, long-term climatic change – are increasingly important for conservation planning. There are many methods for identifying refugia, but the ability to quantify their potential for facilitating species persistence (ie their “capacity”) remains elusive. We propose a flexible framework for prioritizing future refugia, based on their capacity. This framework can be applied through various modeling approaches and consists of three steps: (1) definition of scope, scale, and resolution; (2) identification and quantification; and (3) prioritization for conservation. Capacity is quantified by multiple indicators, including environmental stability, microclimatic heterogeneity, size, and accessibility of the refugium. Using an integrated, semi-mechanistic modeling technique, we illustrate how this approach can be implemented to identify refugia for the plant diversity of Tasmania, Australia. The highest- capacity climate-change refugia were found primarily in cool, wet, and topographically complex environments, several of which we identify as high priorities for biodiversity conservation and management

Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon

Peru has the fourth largest area of peatlands in the Tropics. Its most representative land cover on peat is a Mauritia flexuosa dominated palm swamp (thereafter called dense PS), which has been under human pressure over decades due to the high demand for the M. flexuosa fruit often collected by cutting down the entire palm. Degradation of these carbon dense forests can substantially affect emissions of greenhouse gases and contribute to climate change. The first objective of this research was to assess the impact of dense PS degradation on forest structure and biomass carbon stocks. The second one was to explore the potential of mapping the distribution of dense PS with different degradation levels using remote sensing data and methods. Biomass stocks were measured in 0.25 ha plots established in areas of dense PS with low (n = 2 plots), medium (n = 2) and high degradation (n = 4). We combined field and remote sensing data from the satellites Landsat TM and ALOS/PALSAR to discriminate between areas typifying dense PS with low, medium and high degradation and terra firme, restinga and mixed PS (not M. flexuosa dominated) forests. For this we used a Random Forest machine learning classification algorithm. Results suggest a shift in forest composition from palm to woody tree dominated forest following degradation. We also found that human intervention in dense PS translates into significant reductions in tree carbon stocks with initial (above and below-ground) biomass stocks (135.4 ± 4.8 Mg C ha−1) decreased by 11 and 17% following medium and high degradation. The remote sensing analysis indicates a high separability between dense PS with low degradation from all other categories. Dense PS with medium and high degradation were highly separable from most categories except for restinga forests and mixed PS. Results also showed that data from both active and passive remote sensing sensors are important for the mapping of dense PS degradation. Overall land cover classification accuracy was high (91%). Results from this pilot analysis are encouraging to further explore the use of remote sensing data and methods for monitoring dense PS degradation at broader scales in the Peruvian Amazon. Providing precise estimates on the spatial extent of dense PS degradation and on biomass and peat derived emissions is required for assessing national emissions from forest degradation in Peru and is essential for supporting initiatives aiming at reducing degradation activities

Predicting richness and composition in mountain insect communities at high resolution: a new test of the SESAM framework

Aim The aim of this study was to test different modelling approaches, including a new framework, for predicting the spatial distribution of richness and composition of two insect groups. Location The western Swiss Alps. Methods We compared two community modelling approaches: the classical method of stacking binary prediction obtained fromindividual species distribution models (binary stacked species distribution models, bS-SDMs), and various implementations of a recent framework (spatially explicit species assemblage modelling, SESAM) based on four steps that integrate the different drivers of the assembly process in a unique modelling procedure. We used: (1) five methods to create bS-SDM predictions; (2) two approaches for predicting species richness, by summing individual SDM probabilities or by modelling the number of species (i.e. richness) directly; and (3) five different biotic rules based either on ranking probabilities from SDMs or on community co-occurrence patterns. Combining these various options resulted in 47 implementations for each taxon. Results Species richness of the two taxonomic groups was predicted with good accuracy overall, and in most cases bS-SDM did not produce a biased prediction exceeding the actual number of species in each unit. In the prediction of community composition bS-SDM often also yielded the best evaluation score. In the case of poor performance of bS-SDM (i.e. when bS-SDM overestimated the prediction of richness) the SESAM framework improved predictions of species composition. Main conclusions Our results differed from previous findings using community-level models. First, we show that overprediction of richness by bS-SDM is not a general rule, thus highlighting the relevance of producing good individual SDMs to capture the ecological filters that are important for the assembly process. Second, we confirm the potential of SESAM when richness is overpredicted by bS-SDM; limiting the number of species for each unit and applying biotic rules (here using the ranking of SDM probabilities) can improve predictions of species compositio

EDITOR'S CHOICE: REVIEW: Trait matching of flower visitors and crops predicts fruit set better than trait diversity

Understanding the relationships between trait diversity, species diversity and ecosystem functioning is essential for sustainable management. For functions comprising two trophic levels, trait matching between interacting partners should also drive functioning. However, the predictive ability of trait diversity and matching is unclear for most functions, particularly for crop pollination, where interacting partners did not necessarily co-evolve. World-wide, we collected data on traits of flower visitors and crops, visitation rates to crop flowers per insect species and fruit set in 469 fields of 33 crop systems. Through hierarchical mixed-effects models, we tested whether flower visitor trait diversity and/or trait matching between flower visitors and crops improve the prediction of crop fruit set (functioning) beyond flower visitor species diversity and abundance. Flower visitor trait diversity was positively related to fruit set, but surprisingly did not explain more variation than flower visitor species diversity. The best prediction of fruit set was obtained by matching traits of flower visitors (body size and mouthpart length) and crops (nectar accessibility of flowers) in addition to flower visitor abundance, species richness and species evenness. Fruit set increased with species richness, and more so in assemblages with high evenness, indicating that additional species of flower visitors contribute more to crop pollination when species abundances are similar. Synthesis and applications. Despite contrasting floral traits for crops world-wide, only the abundance of a few pollinator species is commonly managed for greater yield. Our results suggest that the identification and enhancement of pollinator species with traits matching those of the focal crop, as well as the enhancement of pollinator richness and evenness, will increase crop yield beyond current practices. Furthermore, we show that field practitioners can predict and manage agroecosystems for pollination services based on knowledge of just a few traits that are known for a wide range of flower visitor species

Functional diversity underlies demographic responses to environmental variation in European forests: Tree diversity and demography in European forests

Aim  Biodiversity loss and climate-driven ecosystem modification are leading to substantial changes in forest structure and function. However, the effects of diversity on demographic responses to the environment are poorly understood. We tested the diversity hypothesis (measured through functional diversity) and the mass ratio hypothesis (measured through functional identity) in relation to tree growth, tree mortality and sapling abundance. We sought to determine whether functional diversity underlies demographic responses to environmental variation in European forests.  Location  Europe (Spain, Germany, Wallonia, Finland and Sweden).  Methods  We used data from five European national forest inventories from boreal to Mediterranean biomes (c. 700,000 trees in 54,000 plots and 143 tree species) and the main forest types across Europe (i.e. from needle-leaved evergreen forests to broad-leaved deciduous forests). For each forest type, we applied maximum likelihood techniques to quantify the relative importance of stand structure, climate and diversity (i.e. functional diversity and functional identity) as determinants of growth, mortality and sapling abundance. We also tested whether demographic responses to environmental conditions (including stand density, evapotranspiration and temperature anomalies) varied with functional diversity.  Results  Our results suggest that functional diversity has a positive effect on sapling abundance and growth rates in forests across Europe, while no effect was observed on tree mortality. Functional identity has a strong effect on mortality and sapling abundance, with greater mortality rates in forests dominated by needle-leaved individuals and a greater abundance of saplings in forests dominated by broad-leaved individuals. Furthermore, we observed that functional diversity modified the effects of stand density on demographic responses in Mediterranean forests and the influence of evapotranspiration and temperature anomalies in forests widely distributed across Europe.  Main conclusion  Our results suggest that functional diversity may play a key role in forest dynamics through complementarity mechanisms, as well as by modulating demographic responses to environmental variation