Questions What are the major trends in vegetation community structure and forest stand structure over a 14‐yr post‐fire period in a California closed‐cone pine forest? Which biotic and abiotic factors best explain variation in stand structure at different stages of post‐fire succession, and does the relative importance of these factors remain constant? Is there evidence of multiple successional pathways of forest stand development? Location Post‐fire Pinus muricata (bishop pine) forests at Point Reyes National Seashore, CA, USA. Methods We quantified post‐fire vegetation change from field data collected 1, 2, 6 and 14 yr following stand‐replacing wildfire that occurred in 1995. General linear models were used to assess trends in composition (plant functional groups and species diversity) and generalized linear models were used to assess trends in stand structure (post‐fire P. muricata density) and determine the relative importance of abiotic and biotic factors on stand structure in different early‐successional post‐fire years. Results Species richness and diversity peaked in the first 2 yr following fire, and then declined through year 14. Initial post‐fire P. muricata tree regeneration was high (mean 249 750 stems·ha−1 in year 1) and remained well above pre‐fire stand density levels by year 14 (mean 15 179 stems·ha−1). Post‐fire P. muricata seedling density was associated with topographic factors in years 1 and 2, negatively associated with cover of a non‐native herb in year 2, and negatively associated with cover of an early/mid‐successional shrub and positively associated with slope in years 6 and 14. Two alternative pathways of post‐fire stand development have emerged by year 14. A high‐density, closed‐canopy pathway (mean 40 875 stems·ha−1) with early intra‐specific thinning resulted on steep slopes and ridges with low shrub cover. In contrast, a low‐density, open‐canopy pathway (mean 1250 stems·ha−1) resulted on gentle slopes and where shrub cover was high. Conclusions This study provides evidence of divergent successional pathways and illustrates the importance of early‐successional species interactions and topography on longer‐term stand development trajectories in serotinous conifer forests. Early heterogeneity in vegetation establishment set the course for variability in stand structure in mid‐seral stages and may persist into later stages.

Understanding habitat preferences for endangered species is a high priority for management strategies to ensure minimum conflict between human uses and wildlife conservation. The purpose of this study was to identify oceanographic variables that predict occurrences of humpback whales Megaptera novaeangliae within the Cordell Bank and Gulf of the Farallones National Marine Sanctuaries, California, USA, to assess potential conflict with vessel traffic. We used data collected by Applied California Current Ecosystem Studies (ACCESS) conducted from 2004 to 2011. Using zero-inflated negative binomial regression, we developed predictive models and identified locations highly used by whales to characterize humpback whale habitat. We analyzed whale encounter rates at 3-km bin intervals in relation to bathymetric, surface and midwater hydrographic predictor variables and temporal variables characterizing oceanographic conditions. Our models included variables that accounted for detectability of whales. Two models were compared and contrasted: (1) a surface-only model, using only surface oceanographic variables, and (2) a surface + mid-water model, using both surface and mid-water variables. The surface + mid-water model performed significantly better than the surface-only model, which underestimated the amount of suitable whale habitat in the northern half of our study area. We compared resulting predicted habitat areas with previous and current San Francisco Bay Area shipping lane poly gonal footprints to investigate whether newly accepted changes in routes reduced areal overlap with humpback whale habitat. Although our analyses show that the area occupied by shipping traffic has decreased in areas of high predicted humpback whale habitat use, changes in vessel lane footprints do not account for several important aspects of ship-strike risk, including vessel frequency, speed, size and density patterns within the shipping lanes and variability between lanes.

Abstract. We address a poorly understood aspect of ecological niche modeling: its sensitivity to different levels of geographic uncertainty in organism occurrence data. Our primary interest was to assess how accuracy degrades under increasing uncertainty, with performance measured indirectly through model consistency. We used Monte Carlo simulations and a similarity measure to assess model sensitivity across three variables: locality accuracy, niche modeling method, and species. Randomly generated data sets with known levels of locality uncertainty were compared to an original prediction using Fuzzy Kappa. Data sets where locality uncertainty is low were expected to produce similar distribution maps to the original. In contrast, data sets where locality uncertainty is high were expected to produce less similar maps. BIOCLIM, DOMAIN, Maxent and GARP were used to predict the distributions for 1200 simulated datasets (3 species x 4 buffer sizes x 100 randomized data sets). Thus, our experimental design produced a total of 4800 similarity measures, with each of the simulated distributions compared to the prediction of the original data set and corresponding modeling method. A general linear model (GLM) analysis was performed which enables us to simultaneously measure the effect of buffer size, modeling method, and species, as well as interactions among all variables. Our results show that modeling method has the largest effect on similarity scores and uniquely accounts for 40% of the total variance in the model. The second most important factor was buffer size, but it uniquely accounts for only 3% of the variation in the model. The newer and currently more popular methods, GARP and Maxent, were shown to produce more inconsistent predictions than the earlier and simpler methods, BIOCLIM and DOMAIN. Understanding the performance of different niche modeling methods under varying levels of geographic uncertainty is an important step toward more productive applications of historical biodiversity collections.

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