The use of singlebeam echo‐sounder depth data to produce demersal fish distribution models that are comparable to models produced using multibeam echo‐sounder depth

Seafloor characteristics can help in the prediction of fish distribution, which is required for fisheries and conservation management. Despite this, only 5%–10% of the world''s seafloor has been mapped at high resolution, as it is a time‐consuming and expensive process. Multibeam echo‐sounders (MBES) can produce high‐resolution bathymetry and a broad swath coverage of the seafloor, but require greater financial and technical resources for operation and data analysis than singlebeam echo‐sounders (SBES). In contrast, SBES provide comparatively limited spatial coverage, as only a single measurement is made from directly under the vessel. Thus, producing a continuous map requires interpolation to fill gaps between transects. This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated SBES data with full‐coverage MBES distribution models. A Random Forest classifier was used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Loxodon macrorhinus, Pristipomoides multidens, and Pristipomoides typus, with depth and depth derivatives (slope, aspect, standard deviation of depth, terrain ruggedness index, mean curvature, and topographic position index) as explanatory variables. The results indicated that distribution models for A. stellatus, G. grandoculis, L. sceleratus, and L. macrorhinus performed poorly for MBES and SBES data with area under the receiver operator curves (AUC) below 0.7. Consequently, the distribution of these species could not be predicted by seafloor characteristics produced from either echo‐sounder type. Distribution models for P. multidens and P. typus performed well for MBES and the SBES data with an AUC above 0.8. Depth was the most important variable explaining the distribution of P. multidens and P. typus in both MBES and SBES models. While further research is needed, this study shows that in resource‐limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation.     

An at‐home laboratory in plant biology designed to engage students in the process of science

The COVID‐19 pandemic prompted a transition to remote delivery of courses that lack immersive hands‐on research experiences for undergraduate science students, resulting in a scientific research skills gap. In this report, we present an option for an inclusive and authentic, hands‐on research experience that all students can perform off‐campus. Biology students in a semester‐long (13 weeks) sophomore plant physiology course participated in an at‐home laboratory designed to study the impacts of nitrogen addition on growth rates and root nodulation by wild nitrogen‐fixing Rhizobia in Pisum sativum (Pea) plants. This undergraduate research experience, piloted in the fall semester of 2020 in a class with 90 students, was created to help participants learn and practice scientific research skills during the COVID‐19 pandemic. Specifically, the learning outcomes associated with this at‐home research experience were: (1) generate a testable hypothesis, (2) design an experiment to test the hypothesis, (3) explain the importance of biological replication, (4) perform meaningful statistical analyses using R, and (5) compose a research paper to effectively communicate findings to a general biology audience. Students were provided with an at‐home laboratory kit containing the required materials and reagents, which were chosen to be accessible and affordable in case students were unable to access our laboratory kit. Students were guided through all aspects of research, including hypothesis generation, data collection, and data analysis, with video tutorials and live virtual sessions. This at‐home laboratory provided students an opportunity to practice hands‐on research with the flexibility to collect and analyze their own data in a remote setting during the COVID‐19 pandemic. This, or similar laboratories, could also be used as part of distance learning biology courses.     

Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference

1. The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities.
2. Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species‐specific estimates by leveraging detection information among species. Here, we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability.
3. We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N‐mixture models. Our approach substantially increases precision in estimates of abundance compared with models that do not share detection information among species. We then expand this model and apply it to repeated detection/non‐detection data collected on six species of tits (Paridae) breeding at 119 1 km² sampling sites across a P. montanus hybrid zone in northern Switzerland (2004–2020). We find strong impacts of forest cover and elevation on population persistence and colonization in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonization probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies.
4. While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.

     

A comparison in species distribution model performance of succulents using key species and subsets of environmental predictors

Identifying the environmental drivers of the global distribution of succulent plants using the Crassulacean acid metabolism pathway of photosynthesis has previously been investigated through ensemble‐modeling of species delimiting the realized niche of the natural succulent biome. An alternative approach, which may provide further insight into the fundamental niche of succulent plants in the absence of dispersal limitation, is to model the distribution of selected species that are globally widespread and have become naturalized far beyond their native habitats. This could be of interest, for example, in defining areas that may be suitable for cultivation of alternative crops resilient to future climate change. We therefore explored the performance of climate‐only species distribution models (SDMs) in predicting the drivers and distribution of two widespread CAM plants, Opuntia ficus‐indica and Euphorbia tirucalli. Using two different algorithms and five predictor sets, we created distribution models for these exemplar species and produced an updated map of global inter‐annual rainfall predictability. No single predictor set produced markedly more accurate models, with the basic bioclim‐only predictor set marginally out‐performing combinations with additional predictors. Minimum temperature of the coldest month was the single most important variable in determining spatial distribution, but additional predictors such as precipitation and inter‐annual precipitation variability were also important in explaining the differences in spatial predictions between SDMs. When compared against previous projections, an a posteriori approach correctly does not predict distributions in areas of ecophysiological tolerance yet known absence (e.g., due to biotic competition). An updated map of inter‐annual rainfall predictability has successfully identified regions known to be depauperate in succulent plants. High model performance metrics suggest that the majority of potentially suitable regions for these species are predicted by these models with a limited number of climate predictors, and there is no benefit in expanding model complexity and increasing the potential for overfitting.