An official website of the United States government
Summary To what degree plant ecosystems thermoregulate their canopy temperature (Tc) is critical to assess ecosystems' metabolisms and resilience with climate change, but remains controversial, with opinions from no to moderate thermoregulation capability.With global datasets ofTc, air temperature (Ta), and other environmental and biotic variables from FLUXNET and satellites, we tested the ‘limited homeothermy’ hypothesis (indicated byTc&Taregression slope < 1 orTc < Taaround midday) across global extratropics, including temporal and spatial dimensions.Across daily to weekly and monthly timescales, over 80% of sites/ecosystems have slopes ≥1 orTc > Taaround midday, rejecting the above hypothesis. For those sites unsupporting the hypothesis, theirTc–Tadifference (ΔT) exhibits considerable seasonality that shows negative, partial correlations with leaf area index, implying a certain degree of thermoregulation capability. Spatially, site‐mean ΔTexhibits larger variations than the slope indicator, suggesting ΔTis a more sensitive indicator for detecting thermoregulatory differences across biomes. Furthermore, this large spatial‐wide ΔTvariation (0–6°C) is primarily explained by environmental variables (38%) and secondarily by biotic factors (15%).These results demonstrate diverse thermoregulation patterns across global extratropics, with most ecosystems negating the ‘limited homeothermy’ hypothesis, but their thermoregulation still occurs, implying that slope < 1 orTc < Taare not necessary conditions for plant thermoregulation.
more »
« less
Proximate determinants of Taylor's law slopes
Abstract Taylor's law (TL), a commonly observed and applied pattern in ecology, describes variances of population densities as related to mean densities via log(variance) = log(a) + b*log(mean). Variations among datasets in the slope,b, have been associated with multiple factors of central importance in ecology, including strength of competitive interactions and demographic rates. But these associations are not transparent, and the relative importance of these and other factors for TL slope variation is poorly studied. TL is thus a ubiquitously used indicator in ecology, the understanding of which is still opaque.The goal of this study was to provide tools to help fill this gap in understanding by providingproximate determinants of TL slopes, statistical quantities that are correlated to TL slopes but are simpler than the slope itself and are more readily linked to ecological factors.Using numeric simulations and 82 multi‐decadal population datasets, we here propose, test and apply two proximate statistical determinants of TL slopes which we argue can become key tools for understanding the nature and ecological causes of TL slope variation.We find that measures based on population skewness, coefficient of variation and synchrony are effective proximate determinants. We demonstrate their potential for application by using them to help explain covariation in slopes of spatial and temporal TL (two common types of TL).This study provides tools for understanding TL, and demonstrates their usefulness.
more »
« less
- PAR ID:
- 10371039
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Journal of Animal Ecology
- Volume:
- 88
- Issue:
- 3
- ISSN:
- 0021-8790
- Page Range / eLocation ID:
- p. 484-494
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract The extent and magnitude of parasitism often vary among closely related host species and across populations within species. Determining the ecological basis for this species and population‐level variation in parasitism is critical for understanding infection dynamics in multi‐host–parasite systems. To investigate such ecological underpinnings of variation in parasitism, we studiedEnallagmadamselfly host species and their water mite (Arrenurusspp.) ectoparasites in lakes.We first evaluated how host identity and density could shape parasitism. To test the effects of con‐ and heterospecific host density on parasitism, we used a field experiment withEnallagma basidensandE. signatum. We found that parasitism did not vary with con‐ or heterospecific density and was determined by host identity alone, with no spillover effects.We also evaluated the potential role of local adaptation and resource availability in shaping parasitism. To do so, we usedE. signatumin a reciprocal transplant experiment crossed with a prey resource‐level manipulation. This experiment revealed that parasitism declined sharply for one host population in its non‐local lake, but not the other source population, with no effects of prey levels. This asymmetry implies that damselflies express enhanced defences against parasitism that are neither population‐specific nor dependent on resource abundance, or that mites developed heightened local host specificity.The results of multivariate modeling from an observational study generally supported these experimental findings: neither host density nor resource abundance strongly explained among‐population variation in parasitism. Instead, local abiotic conditions (pH) had the strongest relationship with parasitism, with minimal associations with predator density, temperature and a measure of immune function.Collectively, our findings suggest a crucial role for the local environment in shaping host–parasite interactions within multi‐host–parasite systems. More generally, these results show that research at the intersection of community ecology and disease ecology is critical for understanding host–parasite dynamics within natural communities.more » « less
-
Abstract Fire is a powerful ecological and evolutionary force that regulates organismal traits, population sizes, species interactions, community composition, carbon and nutrient cycling and ecosystem function. It also presents a rapidly growing societal challenge, due to both increasingly destructive wildfires and fire exclusion in fire‐dependent ecosystems. As an ecological process, fire integrates complex feedbacks among biological, social and geophysical processes, requiring coordination across several fields and scales of study.Here, we describe the diversity of ways in which fire operates as a fundamental ecological and evolutionary process on Earth. We explore research priorities in six categories of fire ecology: (a) characteristics of fire regimes, (b) changing fire regimes, (c) fire effects on above‐ground ecology, (d) fire effects on below‐ground ecology, (e) fire behaviour and (f) fire ecology modelling.We identify three emergent themes: the need to study fire across temporal scales, to assess the mechanisms underlying a variety of ecological feedbacks involving fire and to improve representation of fire in a range of modelling contexts.Synthesis: As fire regimes and our relationships with fire continue to change, prioritizing these research areas will facilitate understanding of the ecological causes and consequences of future fires and rethinking fire management alternatives.more » « less
-
Abstract Individual body size distributions (ISD) within communities are remarkably consistent across habitats and spatiotemporal scales and can be represented by size spectra, which are described by a power law. The focus of size spectra analysis is to estimate the exponent () of the power law. A common application of size spectra studies is to detect anthropogenic pressures.Many methods have been proposed for estimating most of which involve binning the data, counting the abundance within bins, and then fitting an ordinary least squares regression in log–log space. However, recent work has shown that binning procedures return biased estimates of compared to procedures that directly estimate using maximum likelihood estimation (MLE). While it is clear that MLE produces less biased estimates of site‐specificλ's, it is less clear how this bias affects the ability to test for changes inλacross space and time, a common question in the ecological literature.Here, we used simulation to compare the ability of two normalised binning methods (equal logarithmic and log2bins) and MLE to (1) recapture known values of , and (2) recapture parameters in a linear regression measuring the change in across a hypothetical environmental gradient. We also compared the methods using two previously published body size datasets across a natural temperature gradient and an anthropogenic pollution gradient.Maximum likelihood methods always performed better than common binning methods, which demonstrated consistent bias depending on the simulated values of . This bias carried over to the regressions, which were more accurate when was estimated using MLE compared to the binning procedures. Additionally, the variance in estimates using MLE methods is markedly reduced when compared to binning methods.The error induced by binning methods can be of similar magnitudes as the variation previously published in experimental and observational studies, bringing into question the effect sizes of previously published results. However, while the methods produced different regression slope estimates, they were in qualitative agreement on the sign of those slopes (i.e. all negative or all positive). Our results provide further support for the direct estimation of and its relative variation across environmental gradients using MLE over the more common methods of binning.more » « less
-
Abstract Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species' geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes.We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity, genotype‐environment associations and genomic offset) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RAD‐seq, ddRAD‐seq, whole genome sequencing data) representing environmental variation across the species geographic range.To demonstrate multi‐species applicability, we apply our toolbox to three georeferenced genomic datasets for co‐occurring East African spiny reed frogs (Afrixalus fornasini, A. delicatusandA. sylvaticus) to predict their population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced from a previous study using data for two European bat species (Myotis escaleraiandM. crypticus).Our framework sets the stage for large scale, multi‐species genomic datasets to be leveraged in a novel climate change vulnerability framework to quantify intraspecific differences in genetic diversity, local adaptation, range shifts and population vulnerability based on exposure, sensitivity and landscape barriers.more » « less
