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Marcelo Somos-Valenzuela

Publications and source records attributed to Marcelo Somos-Valenzuela.

2 recordsLinked to original sources

Daily patterns of river herring (Alosa spp.) spawning migrations: Environmental drivers and variation among coastal streams in Massachusetts

The timing of life history events in many plants and animals depends on the seasonal fluctuations of specific environmental conditions. Climate change is altering environmental regimes and disrupting natural cycles and patterns across communities. Anadromous fishes that migrate between marine and freshwater habitats to spawn are particularly sensitive to shifting environmental conditions and thus are vulnerable to the effects of climate change. However, for many anadromous fish species the specific environmental mechanisms driving migration and spawning patterns are not well understood. In this study, we investigated the upstream spawning migrations of river herring Alosa spp. in 12 coastal Massachusetts streams. By analyzing long-term data sets (8–28 years) of daily fish counts, we determined the local influence of environmental factors on daily migration patterns and compared seasonal run dynamics and environmental regimes among streams. Our results suggest that water temperature was the most consistent predictor of both daily river herring presence–absence and abundance during migration. We found inconsistent effects of streamflow and lunar phase, likely due to the anthropogenic manipulation of flow and connectivity in different systems. Geographic patterns in run dynamics and thermal regimes suggest that the more northerly runs in this region are relatively vulnerable to climate change due to migration occurring later in the spring season, at warmer water temperatures that approach thermal maxima, and during a narrower temporal window compared to southern runs. The phenology of river herring and their reliance on seasonal temperature patterns indicate that populations of these species may benefit from management practices that reduce within-stream anthropogenic water temperature manipulations and maintain coolwater thermal refugia.

Massachusetts

Looking beyond wildlife: Using remote cameras to evaluate accuracy of gridded snow data

The use of remote cameras is widespread in wildlife ecology, yet few examples exist of their utility for collecting environmental data. We used a novel camera trap method to evaluate the accuracy of gridded snow data in a mountainous region of the northeastern US. We were specifically interested in assessing (1) how snow depth observations from remote cameras compare with gridded climate data, (2) the sources of error associated with the gridded data and (3) the influence of spatial sampling on bias. We compared daily observations recorded by remote cameras with Snow Data Assimilation System (SNODAS ) gridded predictions using data from three winters (2014–2016). Snow depth observations were correlated with SNODAS predictions for sites ( R 2 = 0.20) and regions ( R 2 = 0.16), yet we detected factors associated with SNODAS bias at both scales. Specifically, SNODAS underpredicted depths at high elevations, at sites with higher solar radiation, and within conifer‐dominated forest. Depths were most underpredicted at highest elevations, up to 44 and 26 cm on average at the site and region scales, respectively. Bias was greatest when predictions were lowest, occasionally predicting snow absence when depths were >100 cm at camera sites. We also detected breakdowns in accuracy when certain environmental conditions varied within the 1 km 2 SNODAS grid cells. For example, underprediction was greatest when the solar radiation values of camera stations increased relative to the mean of the SNODAS grid cells. This relationship was most prominent in mountainous regions, suggesting that factors which influence solar radiation (e.g. topographic complexity) contribute to SNODAS inaccuracy. We caution using gridded snow data for ecological studies when bias is unknown. We suggest increased sampling to adjust for errors associated with gridded data products that arise from factors, such as forest cover and topographic variability. Increasing resolution and accuracy of climate data will improve predictions of species’ responses to climate change.

New Hampshire, Vermont