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Biology subjects

Macias-Fauria, M.

Publications and source records attributed to Macias-Fauria, M..

2 recordsLinked to original sources

Increases in Arctic extreme climatic events are linked to negative fitness effects on the local biota

1.The Arctic harbours uniquely adapted biodiversity and plays an important role in climate regulation. Strong warming trends in the terrestrial Arctic have been linked to an increase in aboveground biomass (Arctic greening) and community-wide shifts such as the northwards-expansion of boreal species (borealisation). Whilst considerable efforts have been made to understand the effects of warming trends in average temperatures on Arctic biota, far fewer studies have focused on trends in extreme climate events and their biotic effects, which have been suggested to be particularly impactful during the Arctic winter months. Here, we present an analysis of trends in two ecologically-relevant winter extreme events -extreme winter warming and rain-on-snow, followed by a meta-analysis on the evidence base for their effects on Arctic biota. We show a strong increase in extreme winter warming across the entire Arctic and high variability in rain-on-snow trends, with some regions recently experiencing rain-on-snow for the first time whilst others seeing a decrease in these events. Ultimately, both extreme events show significant changes in their characteristics and patterns of emergence. Our meta-analysis -encompassing 178 effect sizes across 17 studies and 49 species- demonstrates that extreme winter warming and rain-on-snow induce negative impacts on Arctic biota, with certain taxonomic groups -notably angiosperms and chordates (mostly vertebrates)- exhibiting higher sensitivity than others. Our study provides evidence for both emerging trends in Arctic winter extreme climate events and significant negative biotic effects of such events -which calls for attention to winter weather variability under climate change in the conservation of Arctic biodiversity, whilst highlighting important knowledge gaps.

ecology↗

: A tool for modelling ecosystem resilience

AimsA number of modelling frameworks exist to aid in the identification and exploration of stable states and the assessment of resilience from ecological datasets. However, because such models are complex to implement there is a substantial barrier for the application in ecological research. Here we develop a flexible model of ecological resilience based on Bayesian approximation of the "stability landscape". We illustrate its usage on a tropical area where variation in tree cover has been previously interpreted as alternative stable states. MethodsThe stability landscape, from which stable states and resilience parameters are computed, is modelled using a mixture of multiple distributions, each representing a regression between the system state variable and the environmental covariates. Our "mixglm" model allows the mean, precision, and probability parameters of these distributions in the landscape to be dependent on multiple external covariates. "Mixglm" is implemented as a function in R package with the same name, internally using Bayesian inference via NIMBLE. We also conducted a power analysis to provide guidance regarding required sample size. ResultsWe illustrate the use of the "mixglm" on a published case of tree cover in South America which reports a stability landscape with three distinct stable states. Using "mixglm", we were able to replicate the identification of these states. Moreover, we quantified uncertainty of our estimates, and computed resilience of South Americas forests. Conclusions"Mixglm" can be readily used for description of stability landscapes and identification of stable states in most spatial datasets of system state variables, and it is accompanied by tools for calculation of resilience metrics. It can also be further expanded using regression framework to account for more complex data structures such as spatio-temporal data.

ecology↗