Diurnal temperature range as a key predictor of plants' elevation ranges globally.

A prominent hypothesis in ecology is that larger species ranges are found in more variable climates because species develop broader environmental tolerances, predicting a positive range size-temperature variability relationship. However, this overlooks the extreme temperatures that variable climates impose on species, with upper or lower thermal limits more likely to be exceeded. Accordingly, we propose the 'temperature range squeeze' hypothesis, predicting a negative range size-temperature variability relationship. We test these contrasting predictions by relating 88,000 elevation range sizes of vascular plants in 44 mountains to short- and long-term temperature variation. Consistent with our hypothesis, we find that species' range size is negatively correlated with diurnal temperature range. Accurate predictions of short-term temperature variation will become increasingly important for extinction risk assessment in the future.

Tree and shrub expansion at treeline drive contrasting responses in a subarctic passerine community.

Inferences about the mechanisms of distributional change are often made from simple assessments of variation in the geographical positions of populations. However, direct assessments of species' responses to local habitat change may be necessary for proper understanding of the drivers of distributional dynamics. Amplified climate warming is inducing cascading impacts in boreal-tundra regions including the expansion of conifers and deciduous shrubs (shrubs). In Denali National Park (Denali), Alaska, passerine birds are exhibiting rapid upslope shifts in distribution but the relative roles of conifer and shrub (woody vegetation) expansion in driving these shifts are unknown. Without directly assessing passerine-vegetation dynamics, the assumption has been that the observed upslope shifts are indicative of shrub-adapted passerines tracking the upslope expansion of shrubs. Here, we jointly investigate the processes of conifer and shrub expansion and their relationship to changes in passerine abundance in Denali. We used a remotely sensed vegetation cover timeseries (1985-2020) to assess the topographic and edaphic correlates of conifer and shrub expansion. We then assessed the impacts of changes in shrub and conifer cover on the relative abundance of 12 passerine species (1995-2020). Shrub and conifer colonization rates were highest at intermediate elevations near treeline. However, forest- and shrub-adapted passerines differed in terms of the location in which their response was concentrated relative to treeline. The population growth rates of forest-adapted passerines exhibited stronger effects of woody vegetation expansion at sites that were initially above treeline (IAT). In contrast, the population growth rates of shrub-adapted passerines exhibited the negative effects of conifer expansion together with the positive effects of shrub expansion at initially below treeline sites. However, they showed a weak response to woody vegetation expansion at sites that were IAT. Below treeline conifer infilling appears to be pushing the elevational distributions of shrub-adapted passerines upslope rather than these species following the pull of modest shrub expansion above treeline, as previously assumed. Overall, our findings illustrate the need for explicitly accommodating heterogeneity in habitat change at small spatial scales to properly view the distributional response, particularly when habitat change is concentrated at ecotones.

Latent trajectory models for spatio-temporal dynamics in Alaskan ecosystems.

The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a Bayesian hierarchical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sensed imagery. We used latent trajectory processes to model dynamic state probabilities that evolve annually, from which we derived transition probabilities between ecotypes. Our latent trajectory model accommodates temporal irregularity in survey intervals and uses spatio-temporally heterogeneous climate drivers to infer rates of land cover transitions. We characterized multi-scale spatial correlation induced by plot and subplot arrangements in our study system. We also developed a Pólya-Gamma sampling strategy to improve computation. Our model facilitates inference on the response of ecosystems to shifts in the climate and can be used to predict future land cover transitions under various climate scenarios.

Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity.

Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation responses in sensitive assemblages but weak responses in robust assemblages. But, patterns and mechanisms of sensitivity and robustness are not yet well understood, largely due to a lack of long-term measurements of climate and vegetation. Fortunately, observations are sometimes available across a broad spatial extent. We develop a novel statistical model for a multivariate response based on unknown cluster-specific effects and covariances, where cluster labels correspond to sensitivity and robustness. Our approach utilizes a prototype model for cluster membership that offers flexibility while enforcing smoothness in cluster probabilities across sites with similar characteristics. We demonstrate our approach with an application to vegetation abundance in Alaska, USA, in which we leverage the broad spatial extent of the study area as a proxy for unrecorded historical observations. In the context of the application, our approach yields interpretable site-level cluster labels associated with assemblage-level sensitivity and robustness without requiring strong a priori assumptions about the drivers of climate sensitivity.

Bridging implementation gaps to connect large ecological datasets and complex models.

Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when attempting to incorporate ecological field data with mechanistic models directly using sophisticated statistical methods. To illustrate these complexities and pragmatic paths forward, we present an analysis using tree-ring basal area reconstructions in Denali National Park (DNPP) to constrain successional trajectories of two spruce species (Picea mariana and Picea glauca) simulated by a forest gap model, University of Virginia Forest Model Enhanced-UVAFME. Through this process, we provide preliminary ecological inference about the long-term competitive dynamics between slow-growing P. mariana and relatively faster-growing P. glauca. Incorporating tree-ring data into UVAFME allowed us to estimate a bias correction for stand age with improved parameter estimates. We found that higher parameter values for P. mariana minimum growth under stress and P. glauca maximum growth rate were key to improving simulations of coexistence, agreeing with recent research that faster-growing P. glauca may outcompete P. mariana under climate change scenarios. The implementation challenges we highlight are a crucial part of the conversation for how to bring models together with data to improve ecological inference and forecasting.

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.

Variability in the expansion of trees and shrubs in boreal Alaska.

The expansion of shrubs and trees across high-latitude ecosystems is one of the most dramatic ecological manifestations of climate change. Most of the work quantifying these changes has been done in small areas and over relatively recent time scales. These land-cover transitions are highly spatially variable, and we have limited understanding of the factors underlying this variation. We use repeat photography to generate a data set of land-cover changes in Denali National Park and Preserve, Alaska, stretching back a century and spanning a range of edaphic, topographic, and climatic conditions. Most land-cover classes were quite stable, with low probabilities of transitioning to other land-cover types. The advance of woody vegetation into low-stature tundra, and the spread of conifer trees into shrub-dominated areas, were both more likely at low elevations and in areas without permafrost. Permafrost also reduced the likelihood of herbaceous vegetation transitioning to woody cover. Exceptions to the general trend of relative stability included nearly all (96%) of the broadleaf forest-dominated areas being invaded by conifers, an expected successional trajectory, and many open gravel river bars (17.8%) transitioning to thick shrubs. These floodplain areas were distinctly not at equilibrium, as only 0.1% of shrub-dominated areas converted to gravel. Warming temperatures in coming decades and concomitant declines in the extent of permafrost are predicted to enhance the spread of woody vegetation in Denali further, but only by ~3%. Land-cover transitions, notably the rapid advance of trees and shrubs observed in other studies, could be less likely and more spatially heterogeneous here than in other high-latitude systems.

Stand basal area and solar radiation amplify white spruce climate sensitivity in interior Alaska: Evidence from carbon isotopes and tree rings.

The negative growth response of North American boreal forest trees to warm summers is well documented and the constraint of competition on tree growth widely reported, but the potential interaction between climate and competition in the boreal forest is not well studied. Because competition may amplify or mute tree climate-growth responses, understanding the role current forest structure plays in tree growth responses to climate is critical in assessing and managing future forest productivity in a warming climate. Using white spruce tree ring and carbon isotope data from a long-term vegetation monitoring program in Denali National Park and Preserve, we investigated the hypotheses that (a) competition and site moisture characteristics mediate white spruce radial growth response to climate and (b) moisture limitation is the mechanism for reduced growth. We further examined the impact of large reproductive events (mast years) on white spruce radial growth and stomatal regulation. We found that competition and site moisture characteristics mediated white spruce climate-growth response. The negative radial growth response to warm and dry early- to mid-summer and dry late summer conditions intensified in high competition stands and in areas receiving high potential solar radiation. Discrimination against 13 C was reduced in warm, dry summers and further diminished on south-facing hillslopes and in high competition stands, but was unaffected by climate in open floodplain stands, supporting the hypothesis that competition for moisture limits growth. Finally, during mast years, we found a shift in current year's carbon resources from radial growth to reproduction, reduced 13 C discrimination, and increased intrinsic water-use efficiency. Our findings highlight the importance of temporally variable and confounded factors, such as forest structure and climate, on the observed climate-growth response of white spruce. Thus, white spruce growth trends and productivity in a warming climate will likely depend on landscape position and current forest structure.

Bottom-up processes drive reproductive success in an apex predator.

One of the central goals of the field of population ecology is to identify the drivers of population dynamics, particularly in the context of predator-prey relationships. Understanding the relative role of top-down versus bottom-up drivers is of particular interest in understanding ecosystem dynamics. Our goal was to explore predator-prey relationships in a boreal ecosystem in interior Alaska through the use of multispecies long-term monitoring data. We used 29 years of field data and a dynamic multistate site occupancy modeling approach to explore the trophic relationships between an apex predator, the golden eagle, and cyclic populations of the two primary prey species available to eagles early in the breeding season, snowshoe hare and willow ptarmigan. We found that golden eagle reproductive success was reliant on prey numbers, but also responded prior to changes in the phase of the snowshoe hare population cycle and failed to respond to variation in hare cycle amplitude. There was no lagged response to ptarmigan populations, and ptarmigan populations recovered quickly from the low phase. Together, these results suggested that eagle reproduction is largely driven by bottom-up processes, with little evidence of top-down control of either ptarmigan or hare populations. Although the relationship between golden eagle reproductive success and prey abundance had been previously established, here we established prey populations are likely driving eagle dynamics through bottom-up processes. The key to this insight was our focus on golden eagle reproductive parameters rather than overall abundance. Although our inference is limited to the golden eagle-hare-ptarmigan relationships we studied, our results suggest caution in interpreting predator-prey abundance patterns among other species as strong evidence for top-down control.

Weather-driven change in primary productivity explains variation in the amplitude of two herbivore population cycles in a boreal system.

Vertebrate populations throughout the circumpolar north often exhibit cyclic dynamics, and predation is generally considered to be a primary driver of these cycles in a variety of herbivore species. However, weather and climate play a role in entraining cycles over broad landscapes and may alter cyclic dynamics, although the mechanism by which these processes operate is uncertain. Experimental and observational work has suggested that weather influences primary productivity over multi-year time periods, suggesting a pathway through which weather and climate may influence cyclic herbivore dynamics. Using long-term monitoring data, we investigated the relationships among multi-year weather conditions, measures of primary productivity, and the abundance of two cyclic herbivore species: snowshoe hare and northern red-backed vole. We found that precipitation (rain and snow) and growing season temperatures were strongly associated with variation in primary productivity over multi-year time horizons. In turn, fourfold variation in the amplitude of both the hare and vole cycles observed in our study area corresponded to long-term changes in primary productivity. The congruence of our results for these two species suggests a general mechanism by which weather and climate might influence cyclic herbivore population dynamics. Our findings also suggested that the association between climate warming and the disappearance of cycles might be initiated by changes in primary productivity. This work provides an explanation for observed influences of weather and climate on primary productivity and population cycles and will help our collective understanding of how future climate warming may influence these ecological phenomena in the future.

Climate sensitivity of reproduction in a mast-seeding boreal conifer across its distributional range from lowland to treeline forests.

Mast-seeding conifers such as Picea glauca exhibit synchronous production of large seed crops over wide areas, suggesting climate factors as possible triggers for episodic high seed production. Rapidly changing climatic conditions may thus alter the tempo and spatial pattern of masting of dominant species with potentially far-reaching ecological consequences. Understanding the future reproductive dynamics of ecosystems including boreal forests, which may be dominated by mast-seeding species, requires identifying the specific cues that drive variation in reproductive output across landscape gradients and among years. Here we used annual data collected at three sites spanning an elevation gradient in interior Alaska, USA between 1986 and 2011 to produce the first quantitative models for climate controls over both seedfall and seed viability in P. glauca, a dominant boreal conifer. We identified positive associations between seedfall and increased summer precipitation and decreased summer warmth in all years except for the year prior to seedfall. Seed viability showed a contrasting response, with positive correlations to summer warmth in all years analyzed except for one, and an especially positive response to warm and wet conditions in the seedfall year. Finally, we found substantial reductions in reproductive potential of P. glauca at high elevation due to significantly reduced seed viability there. Our results indicate that major variation in the reproductive potential of this species may occur in different landscape positions in response to warming, with decreasing reproductive success in areas prone to drought stress contrasted with increasing success in higher elevation areas currently limited by cool summer temperatures.