bioRxiv ScienceSearch

Biology subjects

Beumer, L.

Publications and source records attributed to Beumer, L..

2 recordsLinked to original sources

Towards rainy Arctic winters: experimental icing impacts tundra plant productivity and reproduction

The Arctic is warming rapidly, with winters warming up to seven times as fast as summers in some regions. Warm spells in winter lead to more frequent extreme rain-on-snow events that alter snowpack conditions and can encapsulate tundra vegetation in basal ice ( icing) for several months. However, tundra climate change studies have mainly focused on summer warming. Here, we investigate icing effects on vascular plant phenology, productivity, and reproduction in a pioneer field experiment in high Arctic Svalbard, simulating rain-on-snow and resultant icing in five consecutive winters, assessing vascular plant responses throughout each subsequent growing season. We also tested whether icing responses were modified by experimentally increased summer temperatures. Icing alone delayed early phenology of the dominant shrub, Salix polaris, but with evidence for a catch-up (through shortened developmental phases and increased community-level primary production) later in the growing season. This compensatory response occurred at the expense of delayed seed maturation and reduced community-level inflorescence production. Both the phenological delay and allocation trade-offs were associated with icing-induced lags in spring thawing and warming of the soil, crucial to regulating plant nutrient availability and acquisition. Experimental summer warming modified icing effects by advancing and accelerating plant phenology (leaf and seed development), thus increasing primary productivity already early in the growing season, and partially offsetting negative icing effects on reproduction. Thus, winter and summer warming must be considered simultaneously to predict tundra plant climate change responses. Our findings demonstrate that winter warm spells can shape high Arctic plant communities to a similar level as summer warming. However, the absence of accumulated effects over the years reveals an overall resistant community which contrasts with earlier studies documenting major die-off. As rain-on-snow events will be rule rather than exception in most Arctic regions, we call for similar experiments in coordinated circumpolar monitoring programmes across tundra plant communities.

plant biology

Solving the Sample Size Problem for Resource Selection Analysis

O_LISample size sufficiency is a critical consideration for conducting Resource-Selection Analyses (RSAs) from GPS-based animal telemetry. Cited thresholds for sufficiency include a number of captured animals M [≥] 30 and as many relocations per animal N as possible. These thresholds render many RSA-based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. C_LIO_LIWe provide the first comprehensive solution for RSA sample size by deriving closed-form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 2 biologically meaningful quantities: habitat selection strength and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores, and herbivores living in boreal, temperate, and tropical forests, montane woodlands, swamps, and arctic tundra). C_LIO_LIOur analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results contradict conventional wisdom by demonstrating that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with far fewer data than is commonly assumed. C_LIO_LIWe identify several critical steps in implementing these equations, including (i) a priori selection of expected model coefficients, and (ii) sampling intensity for background (absence/pseudo-absence) data within a given definition of availability. We show that random sampling of background data violates the underlying mathematics of RSA, leading to incorrect values for necessary M and N and potentially incorrect RSA model outputs. We argue that these equations should be a mandatory component for all future RSA studies. C_LI

ecology