A Comparison of Five Statistical Methods for Predicting Stream Temperature Across Stream Networks

The health of freshwater aquatic systems, particularly stream networks, is mainly influenced by water temperature, which controls biological processes and influences species distributions and aquatic biodiversity. Thermal regimes of rivers are likely to change in the future, due to climate change and other anthropogenic impacts, and our ability to predict stream temperatures will be critical in understanding distribution shifts of aquatic biota. Spatial statistical network models take into account spatial relationships but have drawbacks, including high computation times and data pre-processing requirements. Machine learning techniques and generalized additive models (GAM) are promising alternatives to the SSN model. Two machine learning methods, gradient boosting machines (GBM) and Random Forests (RF), are computationally efficient and can automatically model complex data structures. However, a study comparing the predictive accuracy among a variety of widely-used statistical modeling techniques has not yet been conducted. My objectives for this study were to 1) compare the accuracy among linear models (LM), SSN, GAM, RF, and GBM in predicting stream temperature over two stream networks and 2) provide guidelines in choosing a prediction method for practitioners and ecologists. Stream temperature prediction accuracies were compared with the test-set root mean square error (RMSE) for all methods. For the actual data, SSN had the highest predictive accuracy overall, which was followed closely by GBM and GAM. LM had the poorest performance overall. This study shows that although SSN appears to be the most accurate method for stream temperature prediction, machine learning methods and GAM may be suitable alternatives

Grass-Shrub Associations over a Precipitation Gradient and Their Implications for Restoration in the Great Basin, USA

As environmental stress increases positive (facilitative) plant interactions often predominate. Plant-plant associations (or lack thereof) can indicate whether certain plant species favor particular types of microsites (e.g., shrub canopies or plant-free interspaces) and can provide valuable insights into whether “nurse plants” will contribute to seeding or planting success during ecological restoration. It can be difficult, however, to anticipate how relationships between nurse plants and plants used for restoration may change over large-ranging, regional stress gradients. We investigated associations between the shrub, Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis), and three common native grasses (Poa secunda, Elymus elymoides, and Pseudoroegneria spicata), representing short-, medium-, and deep-rooted growth forms, respectively, across an annual rainfall gradient (220–350 mm) in the Great Basin, USA. We hypothesized that positive shrub-grass relationships would become more frequent at lower rainfall levels, as indicated by greater cover of grasses in shrub canopies than vegetation-free interspaces. We sampled aerial cover, density, height, basal width, grazing status, and reproductive status of perennial grasses in canopies and interspaces of 25–33 sagebrush individuals at 32 sites along a rainfall gradient. We found that aerial cover of the shallow rooted grass, P. secunda, was higher in sagebrush canopy than interspace microsites at lower levels of rainfall. Cover and density of the medium-rooted grass, E. elymoides were higher in sagebrush canopies than interspaces at all but the highest rainfall levels. Neither annual rainfall nor sagebrush canopy microsite significantly affected P. spicata cover. E. elymoides and P. spicata plants were taller, narrower, and less likely to be grazed in shrub canopy microsites than interspaces. Our results suggest that exploring sagebrush canopy microsites for restoration of native perennial grasses might improve plant establishment, growth, or survival (or some combination thereof), particularly in drier areas. We suggest that land managers consider the nurse plant approach as a way to increase perennial grass abundance in the Great Basin. Controlled experimentation will provide further insights into the life stage-specific effectiveness and practicality of a nurse plant approach for ecological restoration in this region

Grass-Shrub Spatial Associations Over Precipitation and Grazing Gradients in the Great Basin, USA

Plant spatial patterns have been studied to gain insight into plant interactions such as competition and facilitation (positive plant interactions). The stress gradient hypothesis predicts that as environmental stress increases facilitation dominates, while competition dominates in less stressful conditions. Beneficial plants (nurses) can create favorable abiotic conditions for subanopy plants. Additionally, palatable herbaceous species growing under nurse shrub canopies benefit from physical protection. I investigated spatial associations between Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis) and three native grasses (Poa secunda, Elymus elymoides, and Pseudoroegneria spicata) across a rainfall gradient in the Great Basin, USA. I also explored the effect of grazing on grass-shrub spatial associations. I hypothesized that positive shrub-grass spatial associations would become more frequent at lower rainfall levels; I further hypothesized that 1) at intermediate levels of stress, positive grass-shrub spatial associations would dominate and 2) at extreme levels of stress, positive grass-shrub spatial associations and interactions would no longer dominate. At high moisture stress, the addition of grazing stress may limit the nurse’s ability to provide to benefits to subcanopy plants. Cover of P. secunda was greater in shrub canopy microsites than interspaces at low to moderate levels of rainfall. Cover and density of E. elymoides were greater in sagebrush canopies over most rainfall levels. Elymus elymoides and P. spicata were taller and narrower in basal width and less likely to be grazed in canopy versus interspace microsites. I next investigated the effects of grazing intensity over a rainfall gradient and found a significant interaction of rainfall and microsite on P. secunda cover. Poa secunda formed positive interactions with A. tridentata at lower rainfall levels, regardless of grazing intensity. Its cover was significantly greater in interspaces at high rainfall compared to low rainfall sites. Elymus elymoides density was greater in canopy vs. interspace microsites, regardless of rainfall level or grazing intensity. Plant spatial associations can indicate which nurse microsites are favorable to plant growth and may improve seeding or planting success during ecological restoration. My results suggest that exploiting sagebrush canopy microsites for restoration of native perennial grasses would improve plant establishment, growth or survival particularly in drier areas

Site means and predicted regression lines (with 95% confidence bands) for <i>P</i>. <i>spicata</i> for a) density (number of plants / m²), b) height (cm), and c) basal width (cm) across rainfall and shrub microsites.

<p>All regression lines were not significantly different from 0. Sites are distributed across a rainfall gradient, and at each site cover was assessed for three microsites (canopy, edge, and interspace).</p

Type 3 fixed effects estimates for <i>P</i>. <i>secunda</i>, <i>E</i>. <i>elymoides</i>, and <i>P</i>. <i>spicata</i> analyses.

<p>F statistics (F), degrees of freedom (df), and p values (p) are given for each of the 3 main fixed effects: rainfall, sagebrush microsite (canopy, edge, interspace), and microsite*rainfall interaction.</p

Least square means for <i>E</i>. <i>elymoides</i> basal width (cm) at 25^th, 50^th, 75^th, and 90^th percentile of rainfall.

<p>Error bars represent standard error of the mean; different letters denote means that differ significantly from one another within each percentile of rainfall.</p

Least square means for <i>E</i>. <i>elymoides</i> density (plants/m²) at 25^th, 50^th, 75^th, and 90^th percentile values of rainfall.

<p>Error bars represent standard error of the mean; different letters denote means that differ significantly from one another within each percentile of rainfall.</p

Hypotheses.

<p>At low rainfall levels, absolute cover and density of perennial grasses is greater in sub-canopy (solid line) than interspace (dashed line) microsites (i.e., positive grass-shrub relationship). As rainfall increases, the relationship reverses; at greater rainfall levels, absolute cover and density of perennial grasses are greater in interspace than sub-canopy microsites (i.e., negative grass-shrub relationship).</p

Sampling sites in UT, ID, and NV.

<p>Sites were located in five major land resource areas (geographically associated land units) across the Great Basin, USA (inset). Maps were created in ArcMap v10.1. Major land resource area spatial data was obtained from the Natural Resources Conservation Service’s Geospatial Data Gateway (<a href="https://gdg.sc.egov.usda.gov/" target="_blank">https://gdg.sc.egov.usda.gov/</a>).</p