Neglected issues in using weather and climate information in ecology and biogeography

Is New Always Better? Frontiers in Global Climate Datasets for Modeling Treeline Species in the Himalayas

Comparing and evaluating global climate datasets and their effect on model performance in regions with limited data availability has received little attention in ecological modeling studies so far. In this study, we aim at comparing the interpolated climate dataset Worldclim 1.4, which is the most widely used in ecological modeling studies, and the quasi-mechanistical downscaled climate dataset Chelsa, as well as their latest versions Worldclim 2.1 and Chelsa 1.2, with regard to their suitability for modeling studies. To evaluate the effect of these global climate datasets at the meso-scale, the ecological niche of Betula utilis in Nepal is modeled under current and future climate conditions. We underline differences regarding methodology and bias correction between Chelsa and Worldclim versions and highlight potential drawbacks for ecological models in remote high mountain regions. Regarding model performance and prediction plausibility under current climatic conditions, Chelsa-based models significantly outperformed Worldclim-based models, however, the latest version of Chelsa contains partially inherent distorted precipitation amounts. This study emphasizes that unmindful usage of climate data may have severe consequences for modeling treeline species in high-altitude regions as well as for future projections, if based on flawed current model predictions. The results illustrate the inevitable need for interdisciplinary investigations and collaboration between climate scientists and ecologists to enhance climate-based ecological model quality at meso- to local-scales by accounting for local-scale physical features at high temporal and spatial resolution.

Evaluating multiple historical climate products in ecological models under current and projected temperatures

Gridded historical climate products (GHCPs) are employed with increasing frequency when modeling ecological phenomena across large scales and predicting ecological responses to projected climate changes. Concurrently, there is an increasing acknowledgement of the need to account for uncertainty when employing climate projections from ensembles of global circulation models (GCMs) and emissions scenarios. Despite the growing usage and documented differences among GHCPs, uncertainty characterization has primarily focused on GCM and emissions scenario choice, while the consequences of using a single GHCP to make predictions over space and time have received less attention. Here we employ average July temperature data from observations and seven GHCPs to model plant canopy cover and tree basal area across central Alaska, USA. We first compare the fit of, and support for, models employing observed temperatures, GHCP temperatures, and GHCP temperatures with an elevation adjustment, finding (1) greater support for, and better fit using, elevation-adjusted vs. raw temperature models and (2) overall similar fits of elevation-adjusted models employing temperatures from observations or GHCPs. Focusing on basal area, we next compare predictions generated by elevation-adjusted models employing GHCP data under current conditions and a warming scenario of current temperatures plus 2°C, finding good agreement among GHCPs though with between-GHCP differences and variation primarily at middle elevations (~1,000 m). These differences were amplified under the warming scenario. Finally, using pooled indices of prediction variation and difference across GHCP models, we identify characteristics of areas most likely to exhibit prediction uncertainty under current and warming conditions. Despite (1) overall good performance of GHCP data relative to observations in models and (2) positive correlation among model predictions, variation in predictions across models, particularly in mid-elevation areas where the position of treeline may be changing, suggests researchers should exercise caution if selecting a single GHCP for use in models. We recommend the use of multiple GHCPs to provide additional uncertainty information beyond standard estimated prediction intervals, particularly when model predictions are employed in conservation planning.

Climate change and climate change velocity analysis across Germany

Although there are great concerns to what extent current and future climate change impacts biodiversity across different spatial and temporal scales, we still lack a clear information on different climate change metrics across fine spatial scales. Here we present an analysis of climate change and climate change velocity at a local scale (1 × 1 km) across Germany. We focus on seasonal climate variability and velocity and investigate changes in three time periods (1901–2015, 1901–1950 and 1951–2015) using a novel statistical approach. Our results on climate variability showed the highest trends for the 1951–2015 time period. The strongest (positive/negative) and spatially the most dispersed trends were found for Summer maximum temperature and Summer minimum temperatures. For precipitation the strongest positive trends were most pronounced in the summer (1951–2015) and winter (1901–2015). Results for climate change velocity showed that almost 90% of temperature velocities were in the range of 0.5 to 3 km/year, whereas all climate velocities for precipitation were within the range of −3.5 to 4.5 km/year. The key results amplify the need for more local and regional scale studies to better understand species individualistic responses to recent climate change and allow for more accurate future projections and conservation strategies.

High resolution temperature data for ecological research and management on the Southern Ocean Islands

Southern Ocean Islands are globally significant conservation areas. Predicting how their terrestrial ecosystems will respond to current and forecast climate change is essential for their management and requires high-quality temperature data at fine spatial resolutions. Existing datasets are inadequate for this purpose. Remote-sensed land surface temperature (LST) observations, such as those collected by satellite-mounted spectroradiometers, can provide high-resolution, spatially-continuous data for isolated locations. These methods require a clear sightline to measure surface conditions, however, which can leave large data-gaps in temperature time series. Using a spatio-temporal gap-filling method applied to high-resolution (~1 km) LST observations for 20 Southern Ocean Islands, we compiled a complete monthly temperature dataset for a 15-year period (2001-2015). We validated results using in situ measurements of microclimate temperature. Gap-filled temperature observations described the thermal heterogeneity of the region better than existing climatology datasets, particularly for islands with steep elevational gradients and strong prevailing winds. This dataset will be especially useful for terrestrial ecologists, conservation biologists, and for developing island-specific management and mitigation strategies for environmental change.

Identifying climate risk perceptions, information needs, and barriers to information exchange among public land managers

Meeting ecosystem management challenges posed by climate change requires building effective communication channels among researchers, planners and practitioners to focus research on management issues requiring new knowledge. We surveyed resource managers within two regions of the western United States regions to better understand perceived risks and vulnerabilities associated with climate change and barriers to obtaining and using relevant climate science information in making ecosystem management decisions. We sought to understand what types of climate science information resource managers find most valuable, and the formats in which they prefer to receive climate science information. We found broad concern among natural resource managers in federal agencies that climate change will make it more difficult for them to achieve their management goals. Primary barriers to incorporating climate science into planning are distributed among challenges identifying, receiving, and interpreting appropriate science and a lack of direction provided by agency leadership needed to meaningfully use this emerging science in resource planning.

Changes in the geographical distribution of plant species and climatic variables on the West Cornwall peninsula (South West UK)

Recent climate change has had a major impact on biodiversity and has altered the geographical distribution of vascular plant species. This trend is visible globally; however, more local and regional scale research is needed to improve understanding of the patterns of change and to develop appropriate conservation strategies that can minimise cultural, health, and economic losses at finer scales. Here we describe a method to manually geo-reference botanical records from a historical herbarium to track changes in the geographical distributions of plant species in West Cornwall (South West England) using both historical (pre-1900) and contemporary (post-1900) distribution records. We also assess the use of Ellenberg and climate indicator values as markers of responses to climate and environmental change. Using these techniques we detect a loss in 19 plant species, with 6 species losing more than 50% of their previous range. Statistical analysis showed that Ellenberg (light, moisture, nitrogen) and climate indicator values (mean January temperature, mean July temperature and mean precipitation) could be used as environmental change indicators. Significantly higher percentages of area lost were detected in species with lower January temperatures, July temperatures, light, and nitrogen values, as well as higher annual precipitation and moisture values. This study highlights the importance of historical records in examining the changes in plant species’ geographical distributions. We present a method for manual geo-referencing of such records, and demonstrate how using Ellenberg and climate indicator values as environmental and climate change indicators can contribute towards directing appropriate conservation strategies.