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Abstract Biodiversity collections are experiencing a renaissance fueled by the intersection of informatics, emerging technologies, and the extended use and interpretation of specimens and archived databases. In this article, we explore the potential for transformative research in ecology integrating biodiversity collections, stable isotope analysis (SIA), and environmental informatics. Like genomic DNA, SIA provides a common currency interpreted in the context of biogeochemical principles. Integration of SIA data across collections allows for evaluation of long-term ecological change at local to continental scales. Challenges including the analysis of sparse samples, a lack of information about baseline isotopic composition, and the effects of preservation remain, but none of these challenges is insurmountable. The proposed research framework interfaces with existing databases and observatories to provide benchmarks for retrospective studies and ecological forecasting. Collections and SIA add historical context to fundamental questions in freshwater ecological research, reference points for ecosystem monitoring, and a means of quantitative assessment for ecosystem restoration. 

Abstract Food web ecology has revolutionized our understanding of ecological processes, but the drivers of food web properties like trophic position (TP) and food chain length are notoriously enigmatic. In terrestrial ecosystems, above‐ and belowground systems were historically compartmentalized into “green” and “brown” food webs, but the coupling of these systems by animal consumers is increasingly recognized, with potential consequences for trophic structure. We used stable isotope analysis (δ¹³C, δ¹⁵N) of individual amino acids to trace the flow of essential biomolecules and jointly measure multichannel feeding, food web coupling, and TP in a guild of small mammals. We then tested the hypothesis that brown energy fluxes to aboveground consumers increase terrestrial food chain length via cryptic trophic transfers during microbial decomposition. We found that the average small mammal consumer acquired nearly 70% of their essential amino acids (69.0% ± 7.6%) from brown food webs, leading to significant increases in TP across species and functional groups. Fungi were the primary conduit of brown energy to aboveground consumers, providing nearly half the amino acid budget for small mammals on average (44.3% ± 12.0%). These findings illustrate the tightly coupled nature of green and brown food webs and show that microbially mediated energy flow ultimately regulates food web structure in aboveground consumers. Consequently, we propose that the integration of green and brown energy channels is a cryptic driver of food chain length in terrestrial ecosystems. 

Abstract Compound‐specific stable isotope analysis of individual amino acids (CSIA‐AA) has emerged as a transformative approach to estimate consumer trophic positions (TP_CSIA) that are internally indexed to primary producer nitrogen isotope baselines. Central to accurate TP_CSIAestimation is an understanding of beta (β) values—the differences between trophic and source AA δ¹⁵N values in the primary producers at the base of a consumers’ food web. Growing evidence suggests higher taxonomic and tissue‐specificβvalue variability than typically appreciated.This meta‐analysis fulfils a pressing need to comprehensively evaluate relevant sources ofβvalue variability and its contribution to TP_CSIAuncertainty. We first synthesized all published primary producer AA δ¹⁵N data to investigate ecologically relevant sources of variability (e.g. taxonomy, tissue type, habitat type, mode of photosynthesis). We then reviewed the biogeochemical mechanisms underpinning AA δ¹⁵N andβvalue variability. Lastly, we evaluated the sensitivity of TP_CSIAestimates to uncertainty in meanβ_Glx‐Phevalues and Glx‐Phe trophic discrimination factors (TDF_Glx‐Phe).We show that variation inβ_Glx‐Phevalues is two times greater than previously considered, with degree of vascularization, not habitat type (terrestrial vs. aquatic), providing the greatest source of variability (vascular autotroph = −6.6 ± 3.4‰; non‐vascular autotroph = +3.3 ± 1.8‰). Within vascular plants, tissue type secondarily contributed toβ_Glx‐Phevalue variability, but we found no clear distinction among C₃, C₄and CAM plantβ_Glx‐Phevalues. Notably, we found that vascular plantβ_Glx‐Lysvalues (+2.5 ± 1.6‰) are considerably less variable thanβ_Glx‐Phevalues, making Lys a useful AA tracer of primary production sources in terrestrial systems. Our multi‐trophic level sensitivity analyses demonstrate that TP_CSIAestimates are highly sensitive to changes in bothβ_Glx‐Pheand TDF_Glx‐Phevalues but that the relative influence ofβvalues dissipates at higher trophic levels.Our results highlight that primary producerβvalues are integral to accurate trophic position estimation. We outline four key recommendations for identifying, constraining and accounting forβvalue variability to improve TP_CSIAestimation accuracy and precision moving forward. We must ultimately expand libraries of primary producer AA δ¹⁵N values to better understand the mechanistic drivers ofβvalue variation. 

Abstract Extensive ecological research has investigated extreme climate events or long‐term changes in average climate variables, but changes in year‐to‐year (interannual) variability may also cause important biological responses, even if the mean climate is stable. The environmental stochasticity that is a hallmark of climate variability can trigger unexpected biological responses that include tipping points and state transitions, and large differences in weather between consecutive years can also propagate antecedent effects, in which current biological responses depend on responsiveness to past perturbations. However, most studies to date cannot predict ecological responses to rising variance because the study of interannual variance requires empirical platforms that generate long time series. Furthermore, the ecological consequences of increases in climate variance could depend on the mean climate in complex ways; therefore, effective ecological predictions will require determining responses to both nonstationary components of climate distributions: the mean and the variance. We introduce a new design to resolve the relative importance of, and interactions between, a drier mean climate and greater climate variance, which are dual components of ongoing climate change in the southwestern United States. The Mean × Variance Experiment (MVE) adds two novel elements to prior field infrastructure methods: (1) factorial manipulation of variance together with the climate mean and (2) the creation of realistic, stochastic precipitation regimes. Here, we demonstrate the efficacy of the experimental design, including sensor networks and PhenoCams to automate monitoring. We replicated MVE across ecosystem types at the northern edge of the Chihuahuan Desert biome as a central component of the Sevilleta Long‐Term Ecological Research Program. Soil sensors detected significant treatment effects on both the mean and interannual variability in soil moisture, and PhenoCam imagery captured change in vegetation cover. Our design advances field methods to newly compare the sensitivities of populations, communities, and ecosystem processes to climate mean × variance interactions.