Estimating uncertainty in daily weather interpolations: a Bayesian framework for developing climate surfaces

Self Cite

Prolonged periods of extreme heat or drought in the first year after fire affect the resilience and diversity of fire-dependent ecosystems by inhibiting seed germination or increasing mortality of seedlings and resprouting individuals. This interaction between weather and fire is of growing concern as climate changes, particularly in systems subject to stand-replacing crown fires, such as most Mediterranean-type ecosystems. We examined the longest running set of permanent vegetation plots in the Fynbos of South Africa (44 y), finding a significant decline in the diversity of plots driven by increasingly severe postfire summer weather events (number of consecutive days with high temperatures and no rain) and legacy effects of historical woody alien plant densities 30 y after clearing. Species that resprout after fire and/or have graminoid or herb growth forms were particularly affected by postfire weather, whereas all species were sensitive to invasive plants. Observed differences in the response of functional types to extreme postfire weather could drive major shifts in ecosystem structure and function such as altered fire behavior, hydrology, and carbon storage. An estimated 0.5 • C increase in maximum temperature tolerance of the species sets unique to each survey further suggests selection for species adapted to hotter conditions. Taken together, our results show climate change impacts on biodiversity in the hyperdiverse Cape Floristic Region and demonstrate an important interaction between extreme weather and disturbance by fire that may make flammable ecosystems particularly sensitive to climate change.Cape Floristic Region | Fynbos | South Africa | biodiversity | climate change

Section: Ecology Environmental Sciencessupporting

confidence: 84%

Section: Ecology Environmental Sciencesmentioning

confidence: 99%

Intensifying postfire weather and biological invasion drive species loss in a Mediterranean-type biodiversity hotspot

Slingsby

Merow

Aiello‐Lammens

et al. 2017

Self Cite

“…The data included daily mean, maximum, and minimum temperature and daily precipitation with a spatial resolution of 4 km (54), which can be used to summarize a broad range of different weather indices (55). Statistically downscaled climate projection data for one global climate model (GCM, GFDL-ESM2G) with two future scenarios (RCP 4.5 and RCP 8.5) were obtained from Multivariate Adaptive Constructed Analogs group for model predictions (37).…”

Section: Methodsmentioning

confidence: 99%

Deciduous forest responses to temperature, precipitation, and drought imply complex climate change impacts

Xie

Wang

Silander

2015

Self Cite

207

144

Changes in spring and autumn phenology of temperate plants in recent decades have become iconic bio-indicators of rapid climate change. These changes have substantial ecological and economic impacts. However, autumn phenology remains surprisingly little studied. Although the effects of unfavorable environmental conditions (e.g., frost, heat, wetness, and drought) on autumn phenology have been observed for over 60 y, how these factors interact to influence autumn phenological events remain poorly understood. Using remotely sensed phenology data from 2001 to 2012, this study identified and quantified significant effects of a suite of environmental factors on the timing of fall dormancy of deciduous forest communities in New England, United States. Cold, frost, and wet conditions, and high heat-stress tended to induce earlier dormancy of deciduous forests, whereas moderate heat-and drought-stress delayed dormancy. Deciduous forests in two eco-regions showed contrasting, nonlinear responses to variation in these explanatory factors. Based on future climate projection over two periods (2041-2050 and 2090-2099), later dormancy dates were predicted in northern areas. However, in coastal areas earlier dormancy dates were predicted. Our models suggest that besides warming in climate change, changes in frost and moisture conditions as well as extreme weather events (e.g., drought-and heat-stress, and flooding), should also be considered in future predictions of autumn phenology in temperate deciduous forests. This study improves our understanding of how multiple environmental variables interact to affect autumn phenology in temperate deciduous forest ecosystems, and points the way to building more mechanistic and predictive models.Land-surface phenology | dormancy date | frost | New England P lant phenological shifts in recent decades are iconic bio-indicators of climate change (1-4). These phenological changes in turn have cascading ecological effects on species demography, biotic interactions, and ecosystem functions (5-8). Whereas mechanisms of spring phenology (i.e., bud burst, leafing out, and flowering) are well studied (9-13), fall phenology (i.e., leaf senescence and dormancy, indicated by visual signals from leaf coloration and leaf drop) remains little studied (14-16). Changes in timing of autumn phenology play a significant role in growing season length prediction, C and N cycling, and biotic interactions (8,(17)(18)(19). Furthermore, delayed leaf coloration and more muted autumn foliage in response to climate change will likely significantly affect the multibillion dollar fall foliage ecotourism industry (20)(21)(22). Although delayed leaf coloration and leaf drop in deciduous forests have been observed across the northern hemisphere in recent decades (14,23,24), the full range of environmental triggers and how they influence fall phenological changes now or in the future remain poorly understood.Autumn phenology of deciduous woody plant species in temperate regions is the timing of the developmental stages of...

“…We have multidecadal, continuous global measurements from satellites including Moderate Resolution Imaging Spectroradiometer (MODIS) and LANDSAT that can be used to measure key ecosystem properties through time (18). High-resolution historical weather data are also now increasingly available (19,20), as are decades of spatially detailed fire records (3,10). In this paper, we develop a relatively simple yet powerful computational approach to make regional-scale inferences and predictions of ecosystem resilience in response to projected climate change.…”

mentioning

confidence: 99%

Climatic controls on ecosystem resilience: Postfire regeneration in the Cape Floristic Region of South Africa

Wilson

Latimer

Silander

2015

Self Cite

Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil fertility, minimum July (winter) temperature, and mean January (summer) precipitation. Projections using an ensemble of 11 downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) suggest that warmer winter temperatures in 2080-2100 will encourage faster postfire recovery across the region, which could further increase fire frequency due to faster fuel accumulation. However, some models project decreasing precipitation in the western CFR, which would slow recovery rates there, likely reducing fire frequency through lack of fuel and potentially driving local biome shifts from fynbos shrubland to nonburning semidesert vegetation. This simple yet powerful approach to making inferences from large, remotely sensed datasets has potential for wide application to modeling ecosystem resilience in disturbance-prone ecosystems globally.hierarchical Bayesian | ecology | fire | climate | remote sensing I n many ecosystems, climate change will exert its most powerful impacts via disturbance, such as outbreaks of forest pests (1), species invasions (2), and fire (3). A key feature of ecosystems' response to disturbance is the recovery time, the time required for the ecosystem to return to a state similar to its state before disturbance [called "engineering resilience" by Peterson et al. (4)]. Rapid recovery from disturbance is associated with high resilience-that is, the ability of the system to return reliably to its previous state (5). Slow recovery can signal an approaching catastrophic jump to an alternative state (6) or, given frequent disturbance, chronically lower function (7).Fire is one of the most widespread and important forms of disturbance globally, occurring as an important feature in more than 50% of the world's terrestrial ecosystems (8). However, the risk of climate change is difficult to assess because fires are not well represented in global ecosystem models (9). There is growing evidence of increased fire frequency, severity, and/or size in many regions, due in large part to warming and i...