Bruel, R. | bioRxiv Science

bioRxiv2020

Rainbow smelt population responses to species invasions and change in environmental condition

Invasive species can have major disruptions on native food webs, yet the impact of species introductions and whether they will become invasive appears to be context-dependent. Rainbow smelt and alewife co-exist as invasive species in the Laurentian Great Lakes and as native species on the Atlantic coast of North America, but in Lake Champlain rainbow smelt is the dominant native forage fish and alewife are invasive. Alewife became abundant by 2007, providing an opportunity to explore the dynamics of these two species in a system where only one is invasive. We used data from a 31-year forage fish survey to compare demographics of rainbow smelt populations in three basins of Lake Champlain with different pelagic volumes, nutrient levels, and predator abundances. Rainbow smelt catch-per-unit-effort (CPUE) remained constant in the large, deep Main Lake before and after alewife invaded, but decreased in the two smaller basins. Declines were primarily a result of increased age-0 and age-1 mortality. Predation by top piscivores, system productivity, and resource competition alone could not explain the patterns in CPUE across the basins. The mechanisms that allow alewife and rainbow smelt to co-exist could be related to system volume and oxythermal habitat availability, and may explain why the two species do not negatively affect each other in other systems. Summer hypoxia in the smaller basins could force individuals into smaller habitat volumes with higher densities of competitors and cannibalistic adult smelt. Our findings suggest that habitat size mediates the impact of invasive alewife on native rainbow smelt.

ecology↗

bioRxiv2020

Sampling requirements and approaches to detect ecosystem shifts

Environmental monitoring is a key component of understanding and managing ecosystems. Given that most monitoring efforts are still expensive and time-consuming, it is essential that monitoring programs are designed to be efficient and effective. In many situations, the expensive part of monitoring is not sample collection, but instead sample processing, which leads to only a subset of the samples being processed. For example, sediment or ice cores can be quickly obtained in the field, but they require weeks or months of processing in a laboratory setting. Standard sub-sampling approaches often involve equally-spaced sampling. We use simulations to show how many samples, and which types of sampling approaches, are the most effective in detecting ecosystem change. We test these ideas with a case study of Cladocera community assemblage reconstructed from a sediment core. We demonstrate that standard approaches to sample processing are less efficient than an iterative approach. For our case study, using an optimal sampling approach would have resulted in savings of 195 person-hours--thousands of dollars in labor costs. We also show that, compared with these standard approaches, fewer samples are typically needed to achieve high statistical power. We explain how our approach can be applied to monitoring programs that rely on video records, eDNA, remote sensing, and other common tools that allow re-sampling.

ecology↗