Geology ReportsSearch

Geology topics

S. M. Collins

Publications and source records attributed to S. M. Collins.

2 recordsLinked to original sources

Factors affecting nest success of colonial nesting waterbirds in southwest Louisiana

Subsidence and accelerated sea level rise impact nesting area availability and flood probabilities of breeding islands for colonial nesting waterbirds. In 2017 and 2018, we monitored 855 nests of four species of colonial nesting waterbirds on Rabbit Island, LA, to determine factors affecting nest and chick success. Based on logistic exposure models of nests, tricolored herons had the greatest likelihood of survival to hatch (mean (95% confidence interval)) (77% (65.9–83.1%)), followed by brown pelicans (70% (59.9–98.5%)), roseate spoonbills (70% (38.9–83.8%)), and Forster’s terns (12% (10.7–12.2%)). Likelihood of survival to fledge was highest for tricolored herons (32% (12.8–40.7%)), followed by brown pelicans (28% (19.5–28.6%)), roseate spoonbills (47% (43.7–53.3%)), and Forster’s terns (0% (0.005–0.01%)). Nesting strategy and nest timing impacted survival rate; however, the effect depended on timing of inundation events as the timing of inundation events varied across years. Flooding was the primary cause of nest failure for most species. In 2003–2012, rapid expansion in brown pelican colony numbers and significant chick production occurred at Rabbit Island, but hydrologic records indicate no island inundation occurred during the breeding season from the beginning of the hydrologic record (2006) through 2011. Thus, our results contrast with those of previous studies conducted under different hydrologic conditions and demonstrate the challenges of short-term studies informing coastal restoration in a system that is influenced by multi-year to multi-decadal climatic cycles.

Louisiana

Winter precipitation and summer temperature predict lake water quality at macroscales

Climate change can have strong effects on aquatic ecosystems, including disrupting nutrient cycling and mediating processes that affect primary production. Past studies have been conducted mostly on individual or small groups of ecosystems, making it challenging to predict how future climate change will affect water quality at broad scales. We used a subcontinental-scale database to address three objectives: (1) identify which climate metrics best predict lake water quality, (2) examine whether climate influences different nutrient and productivity measures similarly, and (3) quantify the potential effects of a changing climate on lakes. We used climate data to predict lake water quality in ~11,000 north temperate lakes across 17 U.S. states. We developed a novel machine learning method that jointly models different measures of water quality using 48 climate metrics and accounts for properties inherent in macroscale data (e.g. , spatial autocorrelation). Our results suggest that climate metrics related to winter precipitation and summer temperature were strong predictors of lake nutrients and productivity. However, we found variation in the magnitude and direction of the relationship between climate and water quality. We predict that a likely future climate change scenario of warmer summer temperatures will lead to increased nutrient concentrations and algal biomass across lakes (median ~3%–9% increase), whereas increased winter precipitation will have highly variable effects. Our results emphasize the importance of heterogeneity in the response of individual ecosystems to climate and are a caution to extrapolating relationships across space.

Water Resources Research