Evaluating tidal marsh sustainability in the face of sea-level rise: a hybrid modeling approach applied to San Francisco Bay.

BACKGROUND: Tidal marshes will be threatened by increasing rates of sea-level rise (SLR) over the next century. Managers seek guidance on whether existing and restored marshes will be resilient under a range of potential future conditions, and on prioritizing marsh restoration and conservation activities. METHODOLOGY: Building upon established models, we developed a hybrid approach that involves a mechanistic treatment of marsh accretion dynamics and incorporates spatial variation at a scale relevant for conservation and restoration decision-making. We applied this model to San Francisco Bay, using best-available elevation data and estimates of sediment supply and organic matter accumulation developed for 15 Bay subregions. Accretion models were run over 100 years for 70 combinations of starting elevation, mineral sediment, organic matter, and SLR assumptions. Results were applied spatially to evaluate eight Bay-wide climate change scenarios. PRINCIPAL FINDINGS: Model results indicated that under a high rate of SLR (1.65 m/century), short-term restoration of diked subtidal baylands to mid marsh elevations (-0.2 m MHHW) could be achieved over the next century with sediment concentrations greater than 200 mg/L. However, suspended sediment concentrations greater than 300 mg/L would be required for 100-year mid marsh sustainability (i.e., no elevation loss). Organic matter accumulation had minimal impacts on this threshold. Bay-wide projections of marsh habitat area varied substantially, depending primarily on SLR and sediment assumptions. Across all scenarios, however, the model projected a shift in the mix of intertidal habitats, with a loss of high marsh and gains in low marsh and mudflats. CONCLUSIONS/SIGNIFICANCE: Results suggest a bleak prognosis for long-term natural tidal marsh sustainability under a high-SLR scenario. To minimize marsh loss, we recommend conserving adjacent uplands for marsh migration, redistributing dredged sediment to raise elevations, and concentrating restoration efforts in sediment-rich areas. To assist land managers, we developed a web-based decision support tool (www.prbo.org/sfbayslr).

Riparian bird response to vegetation structure: a multiscale analysis using LiDAR measurements of canopy height.

The ability to measure vegetation structure at spatial scales that are biologically meaningful for wildlife is often limited because information about the spatial scale of habitat selection is lacking and there are logistical constraints to measuring vegetation structure at ever larger spatial scales. To address this challenge, we used LiDAR-derived measurements of vegetation canopy height to quantify habitat associations of riparian birds at the Cosumnes River Preserve in central California, USA. Our objectives were (1) to evaluate the utility of LiDAR (light detection and ranging) measurements for describing habitat associations of riparian passerine birds, and (2) to capitalize on the ease with which LiDAR measurements can be summarized at multiple spatial scales to evaluate the predictive performance of vegetation measurements across spatial scales from 0.2 to 50 ha. At each location where we conducted point-count surveys of the avian community, we summarized the mean and coefficient of variation of canopy height measured at five spatial scales (0.2, 0.8, 3.1, 12.6, and 50.2 ha). For each of these spatial scales, we used stepwise model selection to identify the best logistic-regression model describing patterns of occurrence for 16 species of passerine birds that were sufficiently abundant for analysis. We then used area-under-the-curve (AUC) values to identify models that performed well (AUC > 0.75) on a temporally independent data set. Of the 16 species, 10 species had logistic-regression models with AUC values > 0.75. For six of these species, AUC values were highest for the models with vegetation measurements at the 0.2-3 ha scale. For the other four species, AUC values were highest for the model with vegetation variables measured at the 50-ha scale. These results illustrate the utility of using LiDAR-derived measurements of vegetation to understand habitat associations of riparian birds and underscore the importance of using multiscale approaches to modeling wildlife habitat use.