The 2019/2020 summer of Antarctic heatwaves.

This summer, a heatwave across Antarctica saw temperatures soar above average. Temperatures above zero are especially significant because they accelerate ice melt. Casey Station had its highest temperature ever, reaching a maximum of 9.2°C and minimum of 2.5°C. The highest temperature in Antarctica was 20.75°C on 9 February. Here we discuss the biological implications of such extreme events.

Moving beyond presence and absence when examining changes in species distributions.

Species distributions are often simplified to binary representations of the ranges where they are present and absent. It is then common to look for changes in these ranges as indicators of the effects of climate change, the expansion or control of invasive species or the impact of human land-use changes. We argue that there are inherent problems with this approach, and more emphasis should be placed on species relative abundance rather than just presence. The sampling effort required to be confident of absence is often impractical to achieve, and estimates of species range changes based on survey data are therefore inherently sensitive to sampling intensity. Species niches estimated using presence-absence or presence-only models are broader than those for abundance and may exaggerate the viability of small marginal sink populations. We demonstrate that it is possible to transform models of predicted probability of presence to expected abundance if the sampling intensity is known. Using case studies of Antarctic mosses and temperate rain forest trees, we demonstrate additional insights into biotic change that can be gained using this method. While species becoming locally extinct or colonising new areas are extreme and obviously important impacts of global environmental change, changes in abundance could still signal important changes in biological systems and be an early warning indicator of larger future changes.

Semi-Automated Analysis of Digital Photographs for Monitoring East Antarctic Vegetation.

Climate change is affecting Antarctica and minimally destructive long-term monitoring of its unique ecosystems is vital to detect biodiversity trends, and to understand how change is affecting these communities. The use of automated or semi-automated methods is especially valuable in harsh polar environments, as access is limited and conditions extreme. We assessed moss health and cover at six time points between 2003 and 2014 at two East Antarctic sites. Semi-automatic object-based image analysis (OBIA) was used to classify digital photographs using a set of rules based on digital red, green, blue (RGB) and hue-saturation-intensity (HSI) value thresholds, assigning vegetation to categories of healthy, stressed or moribund moss and lichens. Comparison with traditional visual estimates showed that estimates of percent cover using semi-automated OBIA classification fell within the range of variation determined by visual methods. Overall moss health, as assessed using the mean percentages of healthy, stressed and moribund mosses within quadrats, changed over the 11 years at both sites. A marked increase in stress and decline in health was observed across both sites in 2008, followed by recovery to baseline levels of health by 2014 at one site, but with significantly more stressed or moribund moss remaining within the two communities at the other site. Our results confirm that vegetation cover can be reliably estimated using semi-automated OBIA, providing similar accuracy to visual estimation by experts. The resulting vegetation cover estimates provide a sensitive measure to assess change in vegetation health over time and have informed a conceptual framework for the changing condition of Antarctic mosses. In demonstrating that this method can be used to monitor ground cover vegetation at small scales, we suggest it may also be suitable for other extreme environments where repeat monitoring via images is required.