Specific leaf area responses to environmental gradients through space and time

Plant communities can respond to environmental changes by altering their species composition and by individuals (within species) adjusting their physiology. These responses can be captured by measuring key functional traits among and within species along important environmental gradients. Some anthropogenic changes (such as fertilizer runoff) are known to induce distinct community responses, but rarely have responses across natural and anthropogenic gradients been compared in the same system. In this study, we used comprehensive specific leaf area (SLA) data from a diverse Australian annual plant system to examine how individual species and whole communities respond to natural and anthropogenic gradients, and to climatically different growing seasons. We also investigated the influence of different leaf-sampling strategies on community-level results. Many species had similar mean SLA values but differed in SLA responses to spatial and temporal environmental variation. At the community scale, we identified distinct SLA responses to natural and anthropogenic gradients. Along anthropogenic gradients, increased mean SLA, coupled with SLA convergence, revealed evidence of competitive exclusion. This was further supported by the dominance of species turnover (vs. intraspecific variation) along these gradients. We also revealed strong temporal changes in SLA distributions in response to increasing growing-season precipitation. These climate-driven changes highlight differences among co-occurring species in their adaptive capacity to exploit abundant water resources during favorable seasons, differences that are likely to be important for species coexistence in this system. In relation to leaf-sampling strategies, we found that using leaves from a climatically different growing season can lead to misleading conclusions at the community scale

Plant clonal morphologies and spatial patterns as self-organized responses to resource-limited environments

We propose here to interpret and model peculiar plant morphologies (cushions, tussocks) observed in the Andean altiplano as localized structures. Such structures resulting in a patchy, aperiodic aspect of the vegetation cover are hypothesized to self-organize thanks to the interplay between facilitation and competition processes occurring at the scale of basic plant components biologically referred to as 'ramets'. (Ramets are often of clonal origin.) To verify this interpretation, we applied a simple, fairly generic model (one integro-differential equation) emphasizing via Gaussian kernels non-local facilitative and competitive feedbacks of the vegetation biomass density on its own dynamics. We show that under realistic assumptions and parameter values relating to ramet scale, the model can reproduce some macroscopic features of the observed systems of patches and predict values for the inter-patch distance that match the distances encountered in the reference area (Sajama National Park in Bolivia). Prediction of the model can be confronted in the future to data on vegetation patterns along environmental gradients as to anticipate the possible effect of global change on those vegetation systems experiencing constraining environmental conditions.Comment: 14 pages, 6figure

Deer browsing and soil disturbance induce cascading effects on plant communities : a multilevel path analysis

Understanding how large herbivores shape plant diversity patterns is an important challenge in community ecology, especially because many ungulate populations in the northern hemisphere have recently expanded. Because species within plant communities can exhibit strong interactions (e.g., competition, facilitation), selective foraging by large herbivores is likely not only to affect the abundance of palatable species, but also to induce cascading effects across entire plant communities. To investigate these possibilities, we first tested the effects of deer browsing and soil disturbance on herbaceous plant diversity patterns in boreal forest, using standard analyses of variance. Second, we evaluated direct and indirect effects of deer browsing and soil disturbance on the small-scale richness of herbaceous taxa using a multilevel path analysis approach. The first set of analyses showed that deer browsing and soil disturbance influenced herb richness. Path analyses revealed that deer browsing and soil disturbance influenced richness via complex chains of interactions, involving dominant (i.e., the most abundant) browsing-tolerant (DBT) taxa and white birch (Betula papyrifera), a species highly preferred by white-tailed deer (Odocoileus virginianus). We found no evidence that an increase of white birch in fenced quadrats was the direct cause of a decrease in herb richness. However, we found strong evidence that a higher abundance of DBT taxa (i.e., graminoids and Circium arvense), both in fenced and unfenced quadrats, increased herb layer richness. We propose an empirical model in which competitive interactions between white birch and DBT taxa regulate the strength of facilitative relationships between the abundance of DBT taxa and herb richness. In this model, deer browsing and the intensity of soil disturbance initiate a complex chain of cascading effects in boreal plant communities by controlling the abundance of white birch

Should Species Distribution Models Account for Spatial Autocorrelation? A Test of Model Projections Across Eight Millennia of Climate Change

Aim: The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of species distributions under climate change. We provide one of the first comparative evaluations of the ability of a purely spatial SDM, a purely non-spatial SDM and a SDM that combines spatial and environmental information to project species distributions across eight millennia of climate change. Location: Eastern North America. Methods: To distinguish between the importance of climatic versus spatial explanatory variables we fit three Bayesian SDMs to modern occurrence data for Fagus and Tsuga, two tree genera whose distributions can be reliably inferred from fossil pollen: a spatially varying intercept model, a non-spatial model with climatic variables and a spatially varying intercept plus climate model. Using palaeoclimate data with a high temporal resolution, we hindcasted the SDMs in 1000-year time steps for 8000 years, and compared model projections with palynological data for the same periods. Results: For both genera, spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation dataset of modern trees and higher variance in current predictions and hindcasted projections than non-spatial SDMs. Performance of non-spatial and spatial SDMs according to the area under the receiver operating curve varied by genus. For both genera, false negative rates between non-spatial and spatial models were similar, but spatial models had lower false positive rates than non-spatial models. Main conclusions: The inclusion of computationally demanding spatial random effects in SDMs may be warranted when ecological or evolutionary processes prevent taxa from shifting their distributions or when the cost of false positives is high.Organismic and Evolutionary Biolog

Accurate forest projections require long-term wood decay experiments because plant trait effects change through time.

Whether global change will drive changing forests from net carbon (C) sinks to sources relates to how quickly deadwood decomposes. Because complete wood mineralization takes years, most experiments focus on how traits, environments and decomposer communities interact as wood decay begins. Few experiments last long enough to test whether drivers change with decay rates through time, with unknown consequences for scaling short-term results up to long-term forest ecosystem projections. Using a 7 year experiment that captured complete mineralization among 21 temperate tree species, we demonstrate that trait effects fade with advancing decay. However, wood density and vessel diameter, which may influence permeability, control how decay rates change through time. Denser wood loses mass more slowly at first but more quickly with advancing decay, which resolves ambiguity about the after-life consequences of this key plant functional trait by demonstrating that its effect on decay depends on experiment duration and sampling frequency. Only long-term data and a time-varying model yielded accurate predictions of both mass loss in a concurrent experiment and naturally recruited deadwood structure in a 32-year-old forest plot. Given the importance of forests in the carbon cycle, and the pivotal role for wood decay, accurate ecosystem projections are critical and they require experiments that go beyond enumerating potential mechanisms by identifying the temporal scale for their effects