Shading and Watering as a Tool to Mitigate the Impacts of Climate Change in Sea Turtle Nests

Increasing sand temperatures resulting from climate change may negatively impact sea turtle nests by altering sex ratios and decreasing reproductive output. We analyzed the effect of nest shading and watering on sand temperatures as climate mitigation strategies in a beach hatchery at Playa Grande, Costa Rica. We set up plots and placed thermocouples at depths of 45cm and 75cm. Half of the plots were shaded and half were exposed to the sun. Within these exposure treatments, we applied three watering treatments over one month, replicating local climatic conditions experienced in this area. We also examined gravimetric water content of sand by collecting sand samples the day before watering began, the day after watering was complete, and one month after completion. Shading had the largest impact on sand temperature, followed by watering and depth. All watering treatments lowered sand temperature, but the effect varied with depth. Temperatures in plots that received water returned to control levels within 10 days after watering stopped. Water content increased at both depths in the two highest water treatments, and 30 days after the end of water application remained higher than plots with low water. While the impacts of watering on sand temperature dissipate rapidly after the end of application, the impacts on water content are much more lasting. Although less effective at lowering sand temperatures than shading, watering may benefit sea turtle clutches by offsetting negative impacts of low levels of rain in particularly dry areas. Prior to implementing such strategies, the natural conditions at the location of interest (e.g. clutch depth, environmental conditions, and beach characteristics) and natural hatchling sex ratios should be taken into consideration. These results provide insight into the effectiveness of nest shading and watering as climate mitigation techniques and illustrate important points of consideration in the crafting of such strategies.     

Occupancy Modeling for Improved Accuracy and Understanding of Pathogen Prevalence and Dynamics

Most pathogen detection tests are imperfect, with a sensitivity < 100%, thereby resulting in the potential for a false negative, where a pathogen is present but not detected. False negatives in a sample inflate the number of non-detections, negatively biasing estimates of pathogen prevalence. Histological examination of tissues as a diagnostic test can be advantageous as multiple pathogens can be examined and providing important information on associated pathological changes to the host. However, it is usually less sensitive than molecular or microbiological tests for specific pathogens. Our study objectives were to 1) develop a hierarchical occupancy model to examine pathogen prevalence in spring Chinook salmon Oncorhynchus tshawytscha and their distribution among host tissues 2) use the model to estimate pathogen-specific test sensitivities and infection rates, and 3) illustrate the effect of using replicate within host sampling on sample sizes required to detect a pathogen. We examined histological sections of replicate tissue samples from spring Chinook salmon O. tshawytscha collected after spawning for common pathogens seen in this population: Apophallus/echinostome metacercariae, Parvicapsula minibicornis, Nanophyetus salmincola/ metacercariae, and Renibacterium salmoninarum. A hierarchical occupancy model was developed to estimate pathogen and tissue-specific test sensitivities and unbiased estimation of host- and organ-level infection rates. Model estimated sensitivities and host- and organ-level infections rates varied among pathogens and model estimated infection rate was higher than prevalence unadjusted for test sensitivity, confirming that prevalence unadjusted for test sensitivity was negatively biased. The modeling approach provided an analytical approach for using hierarchically structured pathogen detection data from lower sensitivity diagnostic tests, such as histology, to obtain unbiased pathogen prevalence estimates with associated uncertainties. Accounting for test sensitivity using within host replicate samples also required fewer individual fish to be sampled. This approach is useful for evaluating pathogen or microbe community dynamics when test sensitivity is <100%.     

How do different aspects of biodiversity change through time? A case study on an Australian bird community

The study of ecological communities through time can reveal fundamental ecological processes and is key to understanding how natural and human pressures will affect biodiversity. Most studies of ecological communities through time consider only one or a few summary measures (e.g. species richness, total abundance), which might neglect important aspects of community structure or function. We studied temporal variation in several measures of species diversity, size diversity, and species composition in an intensively sampled bird community to determine whether different biodiversity measures change synchronously. We used a novel function regression model, which supports the study of diversity measures that are distributions (e.g. species abundance distributions) alongside measures that are scalar values (e.g. species richness). Most diversity measures changed predictably within years, but inter‐annual changes in size diversity and species composition were not reflected in species diversity. Within and among years, there was considerable variation in distributional measures that was not captured in scalar measures. Predictable variation within years probably was related to seasonal variation in weather patterns or food availability, but variation in size diversity among years probably resulted from stochastic changes in species composition. These results suggest that species and size diversity may be decoupled, and that inferences on scalar diversity measures might not reflect fundamental changes to community structure or function. Our method supports the inclusion of size‐based measures and distributional measures in ecological analyses, and broader uptake of our approach is likely to provide new insight into the processes structuring ecological communities, and inform the links between structure and function in ecological communities.