A comparison of population viability measures

The viability of populations can be quantified with several measures, such as the probability of extinction, the mean time to extinction, or the population size. While conservation management decisions can be based on these measures, it has not yet been explored systematically if different viability measures rank species and scenarios similarly and if one viability measure can be converted into another to compare studies. To address this challenge, we conducted a quantitative comparison of eight viability measures based on the simulated population dynamics of more than 4500 virtual species. We compared (a) the ranking of scenarios based on different viability measures, (b) assessed direct correlations between the measures, and (c) explored if parameters in the simulation models can alter the relationship between pairs of viability measures. We found that viability measures ranked species similarly. Despite this, direct correlations between the different measures were often weak and could not be generalized. This can be explained by the loss of information due to the aggregation of raw data into a single number, the effect of model parameters on the relationship between viability measures, and because distributions, such as the probability of extinction over time, cannot be ranked objectively. Similar scenario rankings by different viability measures show that the choice of the viability metric does in many cases not alter which population is regarded more viable or which management option is the best. However, the more two scenarios or populations differ, the more likely it becomes that different measures produce different rankings. We thus recommend that PVA studies publish raw simulation data, which not only describes all risks and opportunities to the reader but also facilitates meta-analyses of PVA studies.

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Population structure and the influence of microenvironment and genetic similarity on individual growth at Alaskan white spruce treelines

Knowledge on the adaptation of trees to rapid environmental changes is essential to preserve forests and their ecosystem services under climate change. Treeline populations are particularly suitable for studying adaptation processes in trees, as environmental stress together with reduced gene flow can enhance local adaptation. We investigated white spruce (Picea glauca) populations in Alaska on one moisture-limited and two cold-limited treeline sites with a paired plot design of one forest and one treeline population each, resulting in six plots. Additionally, one forest plot in the middle of the distribution range complements the study design. We combined spatial, climatic and dendrochronological data with neutral genetic marker of 2203 trees to investigate population genetic structure and drivers of tree growth. We used several individual-based approaches including random slope mixed-effects models to test the influence of genetic similarity and microenvironment on growth performance. A high degree of genetic diversity was found within each of the seven plots associated with high rates of gene flow. We discovered a low genetic differentiation between the three sites which was better explained by geographic distances than by environmental differences, indicating genetic drift as the main driver of population differentiation. Our findings indicated that microenvironmental features had an overall larger influence on growth performances than genetic similarity among individuals. The effects of climate on growth differed between sites but were smaller than the effect of tree size. Overall, our results suggest that the high genetic diversity of white spruce may result in a wider range of phenotypes which enhances the efficiency of selection when the species is facing rapid climatic changes. In addition, the large intra-individual variability in growth responses may indicate the high phenotypic plasticity of white spruce which can buffer short-term environmental changes and, thus, allow enduring the present changing climate conditions.

Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy

The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood anatomical (QWA) analyses, where the main challenges reside in the detection of a high number of cells, in the intrinsic variability of wood anatomical features, and in the sample quality. To properly classify and interpret features within the images, DCNNs need to undergo a training stage. We performed the training with images from transversal wood anatomical sections, together with manually created optimal outputs of the target cell areas. The target species included an example for the most common wood anatomical structures: four conifer species; a diffuse-porous species, black alder (Alnus glutinosa L.); a diffuse to semi-diffuse-porous species, European beech (Fagus sylvatica L.); and a ring-porous species, sessile oak (Quercus petraea Liebl.). The DCNN was created in Python with Pytorch, and relies on a Mask-RCNN architecture. The developed algorithm detects and segments cells, and provides information on the measurement accuracy. To evaluate the performance of this tool we compared our Mask-RCNN outputs with U-Net, a model architecture employed in a similar study, and with ROXAS, a program based on traditional image analysis techniques. First, we evaluated how many target cells were correctly recognized. Next, we assessed the cell measurement accuracy by evaluating the number of pixels that were correctly assigned to each target cell. Overall, the "learning process" defining artificial intelligence plays a key role in overcoming the issues that are usually manually solved in QWA analyses. Mask-RCNN is the model that better detects which are the features characterizing a target cell when these issues occur. In general, U-Net did not attain the other algorithms' performance, while ROXAS performed best for conifers, and Mask-RCNN showed the highest accuracy in detecting target cells and segmenting lumen areas of angiosperms. Our research demonstrates that future software tools for QWA analyses would greatly benefit from using DCNNs, saving time during the analysis phase, and providing a flexible approach that allows model retraining.

Direct and Indirect Effects of Environmental Limitations on White Spruce Xylem Anatomy at Treeline

Treeline ecosystems are of great scientific interest to study the effects of limiting environmental conditions on tree growth. However, tree growth is multidimensional, with complex interactions between height and radial growth. In this study, we aimed to disentangle effects of height and climate on xylem anatomy of white spruce [Picea glauca (Moench) Voss] at three treeline sites in Alaska; i.e., one warm and drought-limited, and two cold, temperature-limited. To analyze general growth differences between trees from different sites, we used data on annual ring width, diameter at breast height (DBH), and tree height. A representative subset of the samples was used to investigate xylem anatomical traits. We then used linear mixed-effects models to estimate the effects of height and climatic variables on our study traits. Our study showed that xylem anatomical traits in white spruce can be directly and indirectly controlled by environmental conditions: hydraulic-related traits seem to be mainly influenced by tree height, especially in the earlywood. Thus, they are indirectly driven by environmental conditions, through the environment's effects on tree height. Traits related to mechanical support show a direct response to environmental conditions, mainly temperature, especially in the latewood. These results highlight the importance of assessing tree growth in a multidimensional way by considering both direct and indirect effects of environmental forcing to better understand the complexity of tree growth responses to the environment.

SoilTemp: A global database of near-surface temperature

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.

Global assessment of relationships between climate and tree growth

Tree-ring records provide global high-resolution information on tree-species responses to global change, forest carbon and water dynamics, and past climate variability and extremes. The underlying assumption is a stationary (time-stable), quasi-linear relationship between tree growth and environment, which however conflicts with basic ecological and evolutionary theory. Indeed, our global assessment of the relevant tree-ring literature demonstrates non-stationarity in the majority of tested cases, not limited to specific proxies, environmental parameters, regions or species. Non-stationarity likely represents the general nature of the relationship between tree-growth proxies and environment. Studies assuming stationarity however score two times more citations influencing other fields of science and the science-policy interface. To reconcile ecological reality with the application of tree-ring proxies for climate or environmental estimates, we provide a clarification of the stationarity concept, propose a simple confidence framework for the re-evaluation of existing studies and recommend the use of a new statistical tool to detect non-stationarity in tree-ring proxies. Our contribution is meant to stimulate and facilitate discussion in light of our results to help increase confidence in tree-ring-based climate and environmental estimates for science, the public and policymakers.

Moisture-driven shift in the climate sensitivity of white spruce xylem anatomical traits is coupled to large-scale oscillation patterns across northern treeline in northwest North America

Tree growth at northern treelines is generally temperature-limited due to cold and short growing seasons. However, temperature-induced drought stress was repeatedly reported for certain regions of the boreal forest in northwestern North America, provoked by a significant increase in temperature and possibly reinforced by a regime shift of the pacific decadal oscillation (PDO). The aim of this study is to better understand physiological growth reactions of white spruce, a dominant species of the North American boreal forest, to PDO regime shifts using quantitative wood anatomy and traditional tree-ring width (TRW) analysis. We investigated white spruce growth at latitudinal treeline across a >1,000 km gradient in northwestern North America. Functionally important xylem anatomical traits (lumen area, cell-wall thickness, cell number) and TRW were correlated with the drought-sensitive standardized precipitation-evapotranspiration index of the growing season. Correlations were computed separately for complete phases of the PDO in the 20th century, representing alternating warm/dry (1925-1946), cool/wet (1947-1976) and again warm/dry (1977-1998) climate regimes. Xylem anatomical traits revealed water-limiting conditions in both warm/dry PDO regimes, while no or spatially contrasting associations were found for the cool/wet regime, indicating a moisture-driven shift in growth-limiting factors between PDO periods. TRW reflected only the last shift of 1976/1977, suggesting different climate thresholds and a higher sensitivity to moisture availability of xylem anatomical traits compared to TRW. This high sensitivity of xylem anatomical traits permits to identify first signs of moisture-driven growth in treeline white spruce at an early stage, suggesting quantitative wood anatomy being a powerful tool to study climate change effects in the northwestern North American treeline ecotone. Projected temperature increase might challenge growth performance of white spruce as a key component of the North American boreal forest biome in the future, when drier conditions are likely to occur with higher frequency and intensity.