Tall shrubs and trees are advancing into many tundra and wetland ecosystems but at a rate that often falls short of that predicted due to climate change. For forest, tall shrub, and tundra ecosystems in two pristine mountain ranges of Alaska, we apply a Bayesian, error-propagated calculation of expected elevational rise (climate velocity), observed rise (biotic velocity), and their difference (biotic inertia). We show a sensitive dependence of climate velocity on lapse rate and derive biotic velocity as a rigid elevational shift. Ecosystem presence identified from recent and historic orthophotos ~50 years apart was regressed on elevation. Biotic velocity was estimated as the difference between critical point elevations of recent and historic logistic fits divided by time between imagery. For both mountain ranges, the 95% highest posterior density of climate velocity enclosed the posterior distributions of all biotic velocities. In the Kenai Mountains, mean tall shrub and climate velocities were both 2.8 m y(-1). In the better sampled Chugach Mountains, mean tundra retreat was 1.2 m y(-1) and climate velocity 1.3 m y(-1). In each mountain range, the posterior mode of tall woody vegetation velocity (the complement of tundra) matched climate velocity better than either forest or tall shrub alone, suggesting competitive compensation can be important. Forest velocity was consistently low at 0.1-1.1 m y(-1), indicating treeline is advancing slowly. We hypothesize that the high biotic inertia of forest ecosystems in south-central Alaska may be due to competition with tall shrubs and/or more complex climate controls on the elevational limits of trees than tall shrubs. Among tall shrubs, those that disperse farthest had lowest inertia. Finally, the rapid upward advance of woody vegetation may be contributing to regional declines in Dall's sheep (Ovis dalli), a poorly dispersing alpine specialist herbivore with substantial biotic inertia due to dispersal reluctance.
Summary Tall‐shrub expansion into low‐statured communities, a hallmark of recent vegetative change across tundra ecosystems, involves three major genera: Alnus, Betula and Salix. Which genus expands most into tundra landscapes will determine ecosystem properties. We show that Alnus and Salix shrubs segregate thermal space (elevation × insolation) and colonize tundra landscapes differently in response to climate warming, thereby replacing different tundra types. Vegetative change estimated from repeat photography should account for hill‐slope. Methodologically, slope determines surface area estimated from orthophotos as projected pixel area times secant of pixel slope. Ecologically, the change in thermally responsive vegetative area is sensitive to terrain steepness, scaling as the cosecant of hill‐slope, so that studies should expect more shrub expansion in areas of shallow slopes than steep slopes. Repeat aerial photography in Alaska's Chugach Mountains from 1972 to 2012 orthorectified on a high‐resolution lidar digital elevation model indicated tall Salix was rare in 1972 and colonized warmer slopes by 2012. Tall Alnus colonized steeper, cooler slopes both by 2012 and by 1972. Salix and forest colonized similar thermal space. Colonization probability for both shrub genera was maximized at intermediate elevations. Alnus colonization adjacent to dwarf‐shrub tundra was twenty times as likely as Salix colonization. Salix colonization adjacent to low‐shrub/herbaceous tundra was three times as likely as Alnus colonization. Replacement of dwarf‐shrub tundra by Alnus and of low‐shrub/herbaceous communities by Salix will affect herbivores and soil properties. Good agreement between observations of plant functional type and multinomial predictions in a thermal space defined by elevation and insolation suggested that these two variables were sufficient for forecast modelling. Spatially explicit, climate‐driven generalized linear multinomial and random forest classification models in available thermal space forecast surface areas of forest, Alnus, Salix and tundra over a range of warming, modelled as upward shifted isotherms, including expected IPCC scenarios. Both modelling approaches indicated that shrubs may respond nonlinearly to warming. Synthesis. The provision of taxon‐specific coefficients for climate‐driven, spatially explicit models using high‐resolution digital elevation models is necessary for accurately forecasting vegetative change due to climate warming in montane and arctic regions.
The ability to monitor water temperature is important for assessing changes in riverine ecosystems resulting from climate warming. Direct in situ water temperature collection efforts provide point-samples but are cost-prohibitive for characterizing stream temperatures across large spatial scales, especially for small, remote streams. In contrast, satellite thermal infrared imagery may provide a spatially extensive means of monitoring riverine water temperatures, however, the accuracy of these remotely sensed temperatures for small streams is not well understood. Here, we investigated the utility of Landsat 8 thermal infrared imagery and both local and regional environmental variables to estimate subsurface temperatures in high latitude small streams (2 – 30 m wetted width), from a test watershed in southcentral Alaska. Our results suggested that Landsat-based surface temperatures were biased high, and the degree of bias varied with hydrological and meteorological factors. However, with limited in-stream validation work, results indicated it is possible to reconstruct average in situ water temperatures for small streams at regional-scales using a regression modelling framework coupled with publicly-available Landsat or air temperature information. Generalized additive models built from stream stage information from a single gage and air temperatures from a single weather station in the drainage fit to a limited set of in situ temperature recordings could estimate average stream temperatures at the watershed-level with reasonable accuracy (root mean square error = 2.4°C). Landsat information did track closely with regional air temperatures and could also be incorporated into a regression model as a substitute for air temperature to estimate in situ stream temperatures at watershed scales. Importantly, however, while average watershed-scale stream temperatures may be predictable, site-level estimates did not improve with the use of Landsat information or other local covariates, indicating that additional information may be necessary to generate accurate spatially explicit temperature predictions for small order streams.
Minimizing fishing impacts on seafloor ecosystems is a growing focus of ocean management; however, few quantitative tools exist to guide seascape-scale habitat management. To meet these needs, we developed a model to assess benthic ecosystem impacts from fishing gear contact. The habitat impacts model is cast in discrete time and can accommodate overlapping fisheries as well as incorporate gear-specific contact dynamics. We implemented the model in the North Pacific using fishing data from 2003 to 2017, estimating that habitat in 3.1% of the 1.2 million km2 study area was disturbed at the end of the simulation period. A marked decline in habitat disturbance was evident since 2010, attributable to a single regulatory gear change that lifted trawl gear components off the seafloor. Running scenarios without these gear modifications showed these policies might have contributed to a 24% reduction in habitat disturbance since their implementation. Ultimately, model outputs provide direct estimates of the spatial and temporal trends of habitat effects from fishing — a key component of regulatory policies for many of the world’s fisheries.
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