Sensitivity of global terrestrial ecosystems to climate variability.

The identification of properties that contribute to the persistence and resilience of ecosystems despite climate change constitutes a research priority of global relevance. Here we present a novel, empirical approach to assess the relative sensitivity of ecosystems to climate variability, one property of resilience that builds on theoretical modelling work recognizing that systems closer to critical thresholds respond more sensitively to external perturbations. We develop a new metric, the vegetation sensitivity index, that identifies areas sensitive to climate variability over the past 14 years. The metric uses time series data derived from the moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index, and three climatic variables that drive vegetation productivity (air temperature, water availability and cloud cover). Underlying the analysis is an autoregressive modelling approach used to identify climate drivers of vegetation productivity on monthly timescales, in addition to regions with memory effects and reduced response rates to external forcing. We find ecologically sensitive regions with amplified responses to climate variability in the Arctic tundra, parts of the boreal forest belt, the tropical rainforest, alpine regions worldwide, steppe and prairie regions of central Asia and North and South America, the Caatinga deciduous forest in eastern South America, and eastern areas of Australia. Our study provides a quantitative methodology for assessing the relative response rate of ecosystems--be they natural or with a strong anthropogenic signature--to environmental variability, which is the first step towards addressing why some regions appear to be more sensitive than others, and what impact this has on the resilience of ecosystem service provision and human well-being.

Remote assessment of locally important ecological features across landscapes: how representative of reality?

The local ecological footprinting tool (LEFT) uses globally available databases, modeling, and algorithms to, remotely assess locally important ecological features across landscapes based on five criteria: biodiversity (beta-diversity), vulnerability (threatened species), fragmentation, connectivity, and resilience. This approach can be applied to terrestrial landscapes at a 300-m resolution within a given target area. Input is minimal (latitude and longitude) and output is a computer-generated report and series of maps that both individually and synthetically depict the relative value of each ecological criteria. A key question for any such tool, however, is how representative is the remotely obtained output compared to what is on the ground. Here, we present the results from comparing remotely- vs. field-generated outputs from the LEFT tool on two distinct study areas for beta-diversity and distribution of threatened species (vulnerability), the two fields computed by LEFT for which such an approach is feasible. The comparison method consists of a multivariate measure of similarity between two fields based on discrete wavelet transforms, and reveals consistent agreement across a wide range of spatial scales. These results suggest that remote assessment tools such as LEFT hold great potential for determining key ecological features across landscapes and for being utilized in preplanning biodiversity assessment tools.

A comparison of the attractiveness of flowering plant blossoms versus attractive targeted sugar baits (ATSBs) in western Kenya.

Attractive Targeted Sugar Baits (ATSB) have been demonstrated to result in significant reductions in malaria vector numbers in areas of scarce vegetation cover such as in Mali and Israel, but it is not clear whether such an effect can be replicated in environments where mosquitoes have a wide range of options for sugar resources. The current study evaluated the attractiveness of the predominant flowering plants of Asembo Siaya County, western Kenya in comparison to an ATSB developed by Westham Co. Sixteen of the most common flowering plants in the study area were selected and evaluated for relative attractiveness to malaria vectors in semi-field structures. Six of the most attractive flowers were compared to determine the most attractive to local Anopheles mosquitoes. The most attractive plant was then compared to different versions of ATSB. In total, 56,600 Anopheles mosquitoes were released in the semi-field structures. From these, 5150 mosquitoes (2621 males and 2529 females) of An. arabiensis, An. funestus and An. gambiae were recaptured on the attractancy traps. Mangifera indica was the most attractive sugar source for all three species while Hyptis suaveolens and Tephrosia vogelii were the least attractive plants to the mosquitoes. Overall, ATSB version 1.2 was significantly more attractive compared to both ATSB version 1.1 and Mangifera indica. Mosquitoes were differentially attracted to various natural plants in western Kenya and ATSB. The observation that ATSB v1.2 was more attractive to local Anopheles mosquitoes than the most attractive natural sugar source indicates that this product may be able to compete with natural sugar sources in western Kenya and suggests this product may have the potential to impact mosquito populations in the field.

Can Regenerative Agriculture increase national soil carbon stocks? Simulated country-scale adoption of reduced tillage, cover cropping, and ley-arable integration using RothC.

Adopting Regenerative Agriculture (RA) practices on temperate arable land can increase soil organic carbon (SOC) concentration without reducing crop yields. RA is therefore receiving much attention as a climate change mitigation strategy. However, estimating the potential change in national soil carbon stocks following adoption of RA practices is required to determine its suitability for this. Here, we use a well-validated model of soil carbon turnover (RothC) to simulate adoption of three regenerative practices (cover cropping, reduced tillage intensity and incorporation of a grass-based ley phase into arable rotations) across arable land in Great Britain (GB). We develop a modelling framework which calibrates RothC using studies of these measures from a recent systematic review, estimating the proportional increase in carbon inputs to the soil compared to conventional practice, before simulating adoption across GB. We find that cover cropping would on average increase SOC stocks by 10 t·ha-1 within 30 years of adoption across GB, potentially sequestering 6.5 megatonnes of carbon dioxide per year (MtCO2·y-1). Ley-arable systems could increase SOC stocks by 3 or 16 t·ha-1, potentially providing 2.2 or 10.6 MtCO2·y-1 of sequestration over 30 years, depending on the length of the ley-phase (one and four years, respectively, in these scenarios). In contrast, our modelling approach finds little change in soil carbon stocks when practising reduced tillage intensity. Our results indicate that adopting RA practices could make a meaningful contribution to GB agriculture reaching net zero greenhouse gas emissions despite practical constraints to their uptake.

Predicting bird phenology from space: satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment.

Population-level studies of how tit species (Parus spp.) track the changing phenology of their caterpillar food source have provided a model system allowing inference into how populations can adjust to changing climates, but are often limited because they implicitly assume all individuals experience similar environments. Ecologists are increasingly using satellite-derived data to quantify aspects of animals' environments, but so far studies examining phenology have generally done so at large spatial scales. Considering the scale at which individuals experience their environment is likely to be key if we are to understand the ecological and evolutionary processes acting on reproductive phenology within populations. Here, we use time series of satellite images, with a resolution of 240 m, to quantify spatial variation in vegetation green-up for a 385-ha mixed-deciduous woodland. Using data spanning 13 years, we demonstrate that annual population-level measures of the timing of peak abundance of winter moth larvae (Operophtera brumata) and the timing of egg laying in great tits (Parus major) and blue tits (Cyanistes caeruleus) is related to satellite-derived spring vegetation phenology. We go on to show that timing of local vegetation green-up significantly explained individual differences in tit reproductive phenology within the population, and that the degree of synchrony between bird and vegetation phenology showed marked spatial variation across the woodland. Areas of high oak tree (Quercus robur) and hazel (Corylus avellana) density showed the strongest match between remote-sensed vegetation phenology and reproductive phenology in both species. Marked within-population variation in the extent to which phenology of different trophic levels match suggests that more attention should be given to small-scale processes when exploring the causes and consequences of phenological matching. We discuss how use of remotely sensed data to study within-population variation could broaden the scale and scope of studies exploring phenological synchrony between organisms and their environment.