Biology subjects

Kleinhesselink, A. R.

Publications and source records attributed to Kleinhesselink, A. R..

3 recordsLinked to original sources

bioRxiv2021

Where there's smoke, there's fuel: predicting Great Basin rangeland wildfire

Wildfires are a growing management concern in western US rangelands, where invasive annual grasses have altered fire regimes and contributed to an increased incidence of catastrophic large wildfires. Fire activity in arid, non-forested ecosystems is thought to be largely controlled by interannual variation in fuel amount, which in turn is controlled by antecedent weather. Thus, long-range forecasting of fire activity in rangelands should be feasible given annual estimates of fuel quantity. Using a 32 yr time series of spatial data, we employed machine learning algorithms to predict the relative probability of large (>405 ha) wildfire in the Great Basin based on fine-scale annual and 16-day estimates of cover and production of vegetation functional groups, weather, and multitemporal scale drought indices. We evaluated the predictive utility of these models with a leave-one-year-out cross-validation, building spatial hindcasts of fire probability for each year that we compared against actual footprints of large wildfires. Herbaceous aboveground biomass production, bare ground cover, and long-term drought indices were the most important predictors of burning. Across 32 fire seasons, 88% of the area burned in large wildfires coincided with the upper 3 deciles of predicted fire probabilities. At the scale of the Great Basin, several metrics of fire activity were moderately to strongly correlated with average fire probability, including total area burned in large wildfires, number of large wildfires, and maximum fire size. Our findings show that recent years of exceptional fire activity in the Great Basin were predictable based on antecedent weather-driven growth of fine fuels and reveal a significant increasing trend in fire probability over the last three decades driven by widespread changes in fine fuel characteristics.

ecology↗

bioRxiv2020

Functional traits predict species responses to environmental variation in a California grassland annual plant community

O_LITurnover in species composition and community-wide functional traits across environmental gradients is a ubiquitous pattern in ecology, and is generally assumed to reflect shifts in trait optima across these gradients. However, the demographic processes that give rise to these trait turnover patterns at the community level remain unclear. C_LIO_LIWe asked whether shifts in the community-weighted means of three key functional traits across an environmental gradient in a southern California grassland reflect variation in the trait-performance relationship across the landscape. C_LIO_LIWe planted seeds of 17 annual plant species in cleared patches with no competitors, and quantified the lifetime seed production of 1360 individuals. We then asked whether models that included trait-environment interactions help explain interspecific variation in demographic responses to the environment. This allowed us to evaluate whether observed shifts in community-weighted mean traits matched the direction of any trait-environment interactions detected in the plant performance experiment. C_LIO_LIOur results indicate that commonly-measured plant functional traits help explain variation in species responses to the environment - for example, high-SLA species had a demographic advantage in soils with high soil Ca:Mg levels, while low-SLA species had an advantage in low Ca:Mg soils. We also found that shifts in community-weighted mean traits often reflect the direction of these trait-environment interactions, though not all trait-environment relationships at the community level reflect interactive effects of traits and environment on species performance. C_LIO_LIOur results support the value of plant functional traits for predicting species responses to environmental variation, and highlight a need for more detailed evaluation of how trait-performance relationships change across environments to improve such predictions. C_LI

ecology↗

bioRxiv2019

Mechanisms underlying higher order interactions: from quantitative definitions to ecological processes

When species simultaneously compete with two or more species of competitor, higher order interactions (HOIs) can lead to emergent properties not present when species interact in isolated pairs. In order to extend ecological theory to multi-competitor communities, ecologists must develop a practical and general definition for HOIs that can be applied to a wide range of competition models. In this paper we propose a definition for HOIs and outline a set of criteria for testing whether a model has or does not have HOIs. These criteria are valuable for empirical ecologists in need of clarity when discussing HOIs in empirical data. We also provide thorough discussion of how our definition compares with previous definitions of HOIs and interaction modification in the literature. In the second part of the paper we demonstrate the steps required for a rigorous test of HOIs in empirical data. To do this we simulate resource competition between three annual plant species which differ in phenology. We then fit phenomenological competition models to the outcome of simulated competition and use these to test for the presence of HOIs. In our simulations, we find the strength of HOIs varies with phenology: species that grow later experience stronger HOIs than earlier growing species. Our simulation shows how HOIs could emerge in ecosystems where resource availability and individual size change rapidly throughout the course of the growing season and where there are differences in the timing of resource acquisition between competitors.

ecology↗