A Practical Look at the Variable Area Transect

The variable area transect (VAT) is a plotless density estimator that has received little attention in the ecological literature despite having potentially robust estimation properties. VAT allows for density estimations without the lengthy search times associated with other plotless density estimators. In spite of this, little has been written about the effect of varying transect widths on its density estimation properties or on the practical implementation of the VAT in field settings. An artificial population sampler was used to examine the effect of transect width on density estimates obtained using the VAT. Three transect widths were chosen corresponding to the mean object size, the largest object size, and twice the size of the largest object. Transect width had a marked effect on the quality of the density estimation, with the largest transect width resulting in significant negative biases in estimation. For the narrowest width, most estimates were within 10% of the true value for a nonrandomly distributed poulation. The practical considerations of choosing a VAT transect width are enumerated

Should we use climate analogs to predict climate impacts? A contemporary validation.

There is a need for location-specific, fine-grain, climate impact information to inform regional and local-level climate adaptation. However, such climate information is hard to obtain from the existing models either due to the lack of sufficient data or too coarse of a model output to be useful. Analog impact models (AIMs), provide an alternative approach. AIMs rely on climate analogs (locations in space and time that have similar climate) to forecast future climate impacts at a spatial grain as fine as a four kilometer pixel. Analog impact models assume that locations with equivalent climate (climate analogs) also share other important characteristics, such as vegetation type, primary productivity, disturbance regimes, etc. AIMs provide spatially resolved, fine grain predictions of climate impacts, which have the potential to inform location-specific climate adaptation. The use of AIMs is growing, yet there is a lack of information on the quality of AIM predictions. Validating AIMs is a challenge, as actual climate impacts can not be observed in the present and compared to AIM predictions. We evaluate AIMs by testing their performance on climate analogs in space in the reference climate period. We identify spatial climate analogs in the western US for the 1961-1990 period using 30 year normals of four climate variables (mean maximum temperature of the warmest month, minimum temperature of the coldest month, actual evapotranspiration, and climatic water deficit). We evaluate AIM performance by comparing remotely sensed Landsat tree-canopy data at each pixel of interest (i.e. the observed value) to the tree cover at its candidate analog pixels (i.e. the predicted value) at increasing climatic dissimilarity levels. We find that the AIM predicts tree cover well: the slope of the linear fit of predicted vs actual cover is 0.78 (R2 = 0.78) for climatically closest analogs. Model bias increases and precision decreases with increasing climate dissimilarity between the focal and the analog pixels. Tree cover is often overpredicted for pixels with low tree cover, suggesting that recent disturbance may drive the error at the low cover end. Our study provides support for the utility of climate analogs as a climate impact assessment tool and provides details on the effects of climatic dissimilarity, the number of climate analogs considered, and spatial distribution of spatial analogs on the quality of prediction

Fire Activity and Severity in the Western US Vary along Proxy Gradients Representing Fuel Amount and Fuel Moisture

Numerous theoretical and empirical studies have shown that wildfire activity (e.g., area burned) at regional to global scales may be limited at the extremes of environmental gradients such as productivity or moisture. Fire activity, however, represents only one component of the fire regime, and no studies to date have characterized fire severity along such gradients. Given the importance of fire severity in dictating ecological response to fire, this is a considerable knowledge gap. For the western US, we quantify relationships between climate and the fire regime by empirically describing both fire activity and severity along two climatic water balance gradients, actual evapotranspiration (AET) and water deficit (WD), that can be considered proxies for fuel amount and fuel moisture, respectively. We also concurrently summarize fire activity and severity among ecoregions, providing an empirically based description of the geographic distribution of fire regimes. Our results show that fire activity in the western US increases with fuel amount (represented by AET) but has a unimodal (i.e., humped) relationship with fuel moisture (represented by WD); fire severity increases with fuel amount and fuel moisture. The explicit links between fire regime components and physical environmental gradients suggest that multivariable statistical models can be generated to produce an empirically based fire regime map for the western US. Such models will potentially enable researchers to anticipate climate-mediated changes in fire recurrence and its impacts based on gridded spatial data representing future climate scenarios

Can fire atlas data improve species distribution model projections?

Correlative species distribution models (SDMs) are widely used in studies of climate change impacts, yet are often criticized for failing to incorporate disturbance processes that can influence species distributions. Here we use two temporally independent data sets of vascular plant distributions, climate data, and fire atlas data to examine the influence of disturbance history on SDM projection accuracy through time in the mountain ranges of California, USA. We used hierarchical partitioning to examine the influence of fire occurrence on the distribution of 144 vascular plant species and built a suite of SDMs to examine how the inclusion of fire-related predictors (fire occurrence and departure from historical fire return intervals) affects SDM projection accuracy. Fire occurrence provided the least explanatory power among predictor variables for predicting species’ distributions, but provided improved explanatory power for species whose regeneration is tied closely to fire. A measure of the departure from historic fire return interval had greater explanatory power for calibrating modern SDMs than fire occurrence. This variable did not improve internal model accuracy for most species, although it did provide marginal improvement to models for species adapted to high-frequency fire regimes. Fire occurrence and fire return interval departure were strongly related to the climatic covariates used in SDM development, suggesting that improvements in model accuracy may not be expected due to limited additional explanatory power. Our results suggest that the inclusion of coarse-scale measures of disturbance in SDMs may not be necessary to predict species distributions under climate change, particularly for disturbance processes that are largely mediated by climate

Changing forest structure across the landscape of the Sierra Nevada, CA, USA, since the 1930s

Understanding the dynamics of forest structure aids inference regarding future forests and their distributions around the world. Over the last few decades, several papers have addressed changing forest structure in the Sierra Nevada, CA, USA, but these studies were limited in scope. We carried out a broad comparison of forest density and composition in the 1930s versus the 2000s for the west slope of the central and northern Sierra Nevada, using the two most extensive data sets available. Forests in this region have endured a long, complex history of human disturbance, and are now experiencing climatic shifts. We subdivided the landscape into elevation and latitude zones and compared historical and modern tree densities within each zone. We compared densities in historical plots to burned and unburned modern plots, as well as densities of individual tree species in historical vs. modern plots for their entire elevational distribution. Density of small trees (10.2-30.4 cm dbh) was significantly higher in the modern data set for all elevations and all latitudes, ranging from 20 to 148% higher. However, density of large trees (61.0 cm) was lower in the modern data set for most elevations and latitudes, ranging from 41% to 60% lower in most zones. Density difference of mid-sized trees (30.5-60.9 cm) was mixed, but was generally higher in modern plots. The pattern of more small trees but fewer large trees held for most individual species as well, but with notable exceptions. Our comparison of burned and unburned plots strongly implicates fire suppression as a driver of increased density of small trees in low- to mid-elevation forests. However, modern high-elevation (.2500 m) forests, where fire suppression impacts should be minimal, were also significantly denser than historical plots. Changing climatic conditions may be driving increased densities of small trees in high elevations, as well as decreased densities of large trees across the region

Understanding Relationships Among Abundance, Extirpation, and Climate at Ecoregional Scales

Recent research on mountain-dwelling species has illustrated changes in species\u27 distributional patterns in response to climate change. Abundance of a species will likely provide an earlier warning indicator of change than will occupancy, yet relationships between abundance and climatic factors have received less attention. We tested whether predictors of counts of American pikas (Ochotona princeps) during surveys from the Great Basin region in 1994-1999 and 2003-2008 differed between the two periods. Additionally, we tested whether various modeled aspects of ecohydrology better predicted relative density than did average annual precipitation, and whether risk of site-wide extirpation predicted subsequent population counts of pikas. We observed several patterns of change in pika abundance at range edges that likely constitute early warnings of distributional shifts. Predictors of pika abundance differed strongly between the survey periods, as did pika extirpation patterns previously reported from this region. Additionally, maximum snowpack and growing-season precipitation resulted in better-supported models than those using average annual precipitation, and constituted two of the top three predictors of pika density in the 2000s surveys (affecting pikas perhaps via vegetation). Unexpectedly, we found that extirpation risk positively predicted subsequent population size. Our results emphasize the need to clarify mechanisms underlying biotic responses to recent climate change at organism-relevant scales, to inform management and conservation strategies for species of concern

Artificial amplification of warming trends across the mountains of the western United States

Observations from the main mountain climate station network in the western United States (U.S.) suggest that higher elevations are warming faster than lower elevations. This has led to the assumption that elevation-dependent warming is prevalent throughout the region with impacts to water resources and ecosystem services. Here we critically evaluate this network\u27s temperature observations and show that extreme warming observed at higher elevations is the result of systematic artifacts and not climatic conditions. With artifacts removed, the network\u27s 1991–2012 minimum temperature trend decreases from +1.16°C decade−1 to +0.106°C decade−1 and is statistically indistinguishable from lower elevation trends. Moreover, longer-term widely used gridded climate products propagate the spurious temperature trend, thereby amplifying 1981–2012 western U.S. elevation-dependent warming by +217 to +562%. In the context of a warming climate, this artificial amplification of mountain climate trends has likely compromised our ability to accurately attribute climate change impacts across the mountainous western U.S