Geology topics

Robert B. Nichols

Publications and source records attributed to Robert B. Nichols.

3 recordsLinked to original sources

USGS2017

Declining occurrence and low colonization probability in freshwater mussel assemblages: A dynamic occurrence modeling approach

Mussel monitoring data are abundant, but methods for analyzing long-term trends in these data are often uninformative or have low power to detect changes. We used a dynamic occurrence model, which accounted for imperfect species detection in surveys, to assess changes in species occurrence in a longterm data set (1986–2011) for the Tar River basin of North Carolina, USA. Occurrence of all species decreased steadily over the time period studied. Occurrence in 1986 ranged from 0.19 for Utterbackia imbecillis to 0.60 for Fusconaia masoni . Occurrence in 2010–2011 ranged from 0.10 for Lampsilis radiata to 0.40 for F. masoni . The maximum difference between occurrence in 1986 and 2011 was a decline of 0.30 for Alasmidonta undulata . Mean persistence for all species was high (0.97, 95% CI ¼ 0.95–0.99); however, mean colonization probability was very low (,0.01, 95% CI ¼ ,0.01–0.01). These results indicate that mussels persisted at sites already occupied but that they have not colonized sites where they had not occurred previously. Our findings highlight the importance of modeling approaches that incorporate imperfect detection in estimating species occurrence and revealing temporal trends to inform conservation planning.

North Carolina↗

USGS2016

Species traits and catchment-scale habitat factors influence the occurrence of freshwater mussel populations and assemblages

Conservation of freshwater unionid mussels presents unique challenges due to their distinctive life cycle, cryptic occurrence and imperilled status. Relevant ecological information is urgently needed to guide their management and conservation. We adopted a modelling approach, which is a novel application to freshwater mussels to enhance inference on rare species, by borrowing data among species in a hierarchical framework to conduct the most comprehensive occurrence analysis for freshwater mussels to date. We incorporated imperfect detection to more accurately examine effects of biotic and abiotic factors at multiple scales on the occurrence of 14 mussel species and the entire assemblage of the Tar River Basin of North Carolina, U.S.A. The single assemblage estimate of detection probability for all species was 0.42 (95% CI, 0.36–0.47) with no species- or site-specific detection effects identified. We empirically observed 15 mussel species in the basin but estimated total species richness at 21 (95% CI, 16–24) when accounting for imperfect detection. Mean occurrence probability among species ranged from 0.04 (95% CI, 0.01–0.16) for Alasmidonta undulata, an undescribed Lampsilis sp. , and Strophitus undulatus to 0.67 (95% CI, 0.42–0.86) for Elliptio icterina . Median occurrence probability among sites was <0.30 for all species with the exception of E. icterina . Site occurrence probability generally related to mussel conservation status, with reduced occurrence for endangered and threatened species. Catchment-scale abiotic variables (stream power, agricultural land use) and species traits (brood time, host specificity, tribe) influenced the occurrence of mussel assemblages more than reach- or microhabitat-scale features. Our findings reflect the complexity of mussel ecology and indicate that habitat restoration alone may not be adequate for mussel conservation. Catchment-scale management can benefit an entire assemblage, but species-specific strategies may be necessary for successful conservation. The hierarchical multispecies modelling approach revealed findings that could not be elucidated by other means, and the approach may be applied more broadly to other river basins and regions. Accurate measures of assemblage dynamics, such as occurrence and species richness, are required to create management plans for effective conservation.

Freshwater Biology↗

USGS2015

Developing a conservation strategy to maximize persistence of an endangered freshwater mussel species while considering management effectiveness and cost

We used a structured decision-making process to develop conservation strategies to increase persistence of Dwarf Wedgemussel ( Alasmidonta heterodon ) in North Carolina, USA, while accounting for uncertainty in management effectiveness and considering costs. Alternative conservation strategies were portfolios of management actions that differed by location of management actions on the landscape. Objectives of the conservation strategy were to maximize species persistence, maintain genetic diversity, maximize public support, and minimize management costs. We compared 4 conservation strategies: 1) the ‘status quo’ strategy represented current management, 2) the ‘protect the best’ strategy focused on protecting the best populations in the Tar River basin, 3) the ‘expand the distribution’ strategy focused on management of extant populations and establishment of new populations in the Neuse River basin, and 4) the ‘hybrid’ strategy combined elements of each strategy to balance conservation in the Tar and Neuse River basins. A population model informed requirements for population management, and experts projected performance of alternative strategies over a 20-y period. The optimal strategy depended on the relative value placed on competing objectives, which can vary among stakeholders. The protect the best and hybrid strategies were optimal across a wide range of relative values with 2 exceptions: 1) if minimizing management cost was of overriding concern, then status quo was optimal, or 2) if maximizing population persistence in the Neuse River basin was emphasized, then expand the distribution strategy was optimal. The optimal strategy was robust to uncertainty in management effectiveness. Overall, the structured decision process can help identify the most promising strategies for endangered species conservation that maximize conservation benefit given the constraint of limited funding.

North Carolina↗