Brian R. Miranda

Studying fire mitigation strategies in multi-ownership landscapes: balancing the management of fire-dependent ecosystems and fire risk

Public forests are surrounded by land over which agency managers have no control, and whose owners expect the public forest to be a “good neighbor.” Fire risk abatement on multi-owner landscapes containing flammable but fire-dependent ecosystems epitomizes the complexities of managing public lands. We report a case study that applies a landscape disturbance and succession model (LANDIS) to evaluate the relative effectiveness of four alternative fire mitigation strategies on the Chequamegon-Nicolet National Forest (Wisconsin, USA), where fire-dependent pine and oak systems overlap with a rapidly developing wildland–urban interface (WUI). We incorporated timber management of the current forest plan and fire characteristics (ignition patterns, fire sizes, and fuel-specific fire spread rates) typical for the region under current fire suppression policies, using a combination of previously published fire analyses and interactive expert opinion from the national forest. Of the fire mitigation strategies evaluated, reduction of ignitions caused by debris-burning had the strongest influence on fire risk, followed by the strategic redistribution of risky forest types away from the high ignition rates of the WUI. Other treatments (fire breaks and reducing roadside ignitions) were less effective. Escaped fires, although rare, introduced significant uncertainty in the simulations and are expected to complicate fire management planning. Simulations also show that long-term maintenance of fire-dependent communities (that is, pine and oak) representing the greatest forest fire risk requires active management. Resolving conflict between the survival of fire-dependent communities that are regionally declining and continued rural development requires strategic planning that accounts for multi-owner activities.

Spatial and temporal drivers of wildfire occurrence in the context of rural development in northern Wisconsin, USA

Most drivers underlying wildfire are dynamic, but at different spatial and temporal scales. We quantified temporal and spatial trends in wildfire patterns over two spatial extents in northern Wisconsin to identify drivers and their change through time. We used spatial point pattern analysis to quantify the spatial pattern of wildfire occurrences, and linear regression to quantify the influence of drought and temporal trends in annual number and mean size of wildfires. Analyses confirmed drought as an important driver of both occurrences and fire size. When both drought and time were incorporated in linear regression models, the number of wildfires showed a declining trend across the full study area, despite housing density increasing in magnitude and spatial extent. Fires caused by campfires and debris-burning did not show any temporal trends. Comparison of spatial models representing biophysical, anthropogenic and combined factors demonstrated human influences on wildfire occurrences, especially human activity, infrastructure and property values. We also identified a non-linear relationship between housing density and wildfire occurrence. Large wildfire occurrence was predicted by similar variables to all occurrences, except the direction of influence changed. Understanding these spatial and temporal drivers of wildfire occurrence has implications for land-use planning, wildfire suppression strategies and ecological goals.

Assessing Knowledge Ambiguity in the Creation of a Model Based on Expert Knowledge and Comparison with the Results of a Landscape Succession Model in Central Labrador

Sustainable forest management (SFM) recognizes that the spatial and temporal patterns generated at different scales by natural landscape and stand dynamics processes should serve as a guide for managing the forest within its range of natural variability (Landres et al. 1999; Gauthier et al. 2008). Landscape simulation modeling is a powerful tool that can help encompass such complexity and support SFM planning (Messier et al. 2003). Forecasting the complex behaviors of a forested landscape involving patterns and processes that interact at multiple temporal and spatial scales poses significant challenges (Gunderson and Holling 2002). Empirical evidence for the functioning of key elements, such as succession and disturbance regimes, is crucial for model parameterization (Mladenoff 2004). However, reliable empirical data about the forest vegetation dynamics that arise in response to forest management and other disturbances may be scarce, particularly in remote areas where harvesting activity has been historically limited.

Comparing fire spread algorithms using equivalence testing and neutral landscape models

We demonstrate a method to evaluate the degree to which a meta-model approximates spatial disturbance processes represented by a more detailed model across a range of landscape conditions, using neutral landscapes and equivalence testing. We illustrate this approach by comparing burn patterns produced by a relatively simple fire spread algorithm with those generated by a more detailed fire behavior model from which the simpler algorithm was derived. Equivalence testing allows objective comparisons of the output of simple and complex models, to determine if the results are significantly similar. Neutral landscape models represent a range of landscape conditions that the model may encounter, allowing evaluation of the sensitivity and behavior of the model to different landscape compositions and configurations. We first tested the model for universal applicability, then narrowed the testing to assess the practical domain of applicability. As a whole, the calibrated simple model passed the test for significant equivalence using the 25% threshold. When applied to a range of landscape conditions different from the calibration scenarios, the model failed the tests for equivalence. Although our particular model failed the tests, the neutral landscape models were helpful in determining an appropriate domain of applicability and in assessing the model sensitivity to landscape changes. Equivalence testing provides an effective method for model comparison, and coupled with neutral landscapes, our approach provides an objective way to assess the domain of applicability of a spatial model.

Variable climate response differentiates the growth of Sky Island Ponderosa Pines

Key messageThe seasonally cool and moist conditions of spring improved the growth of two co-occurring ponderosa pine species, which displayed different seasonal climatic responses and length of correlations to drought.AbstractWe examined the climatic sensitivity of two partially sympatric pine species growing at their transition zone in the Santa Catalina Mountains, AZ, USA. Pinus arizonica is found at lower elevations compared to P. ponderosa var. brachyptera. Ring widths were measured in trees at two sites and correlated with precipitation, temperature, and Palmer Drought Severity Index to assess the influence of climate on growth. The two species were analyzed within and between sites, which have similar elevation, aspect, and species composition, although soils at the two sites have different water-holding capacities. Response function analyses of P. arizonica [sampled near its upper (and wetter) elevation limit], and P. ponderosa var. brachyptera [sampled near its lower (and drier) elevation limit] indicated that annual growth correlated positively and strongly with spring precipitation at both study locations. Local site conditions had a major impact on tree growth and variability in site conditions helped resolve the differences in species’ response to climate. For example, at the less dry site, growth of the lower-elevation pine (P. arizonica) responded to early-winter precipitation, while P. ponderosa var. brachyptera did not. Also, correlation analysis indicated that P. arizonica’s growth was more sensitive to drought for longer periods than P. ponderosa var. brachyptera. Finally, partial temperature-growth correlations of P. arizonica and P. ponderosa var. brachyptera indicated growth was limited by increased growing season and winter respiration, respectively. Rising night-time temperatures during spring significantly reduced growth of P. arizonica at Mt. Lemmon. These findings demonstrate subtle yet meaningful interspecies differences in sensitivity to seasonal moisture stress and use of carbon resources.

Forecasting effects of tree species reintroduction strategies on carbon stocks in a future without historical analog

American chestnut (Castanea dentata) was once an important component forests in the central Appalachians (USA), but it was functionally extirpated nearly a century ago. Attempts are underway to reintroduce blight-resistant chestnut to its former range, but it is uncertain how current forest composition, climate, and atmospheric changes and disturbance regimes will interact to determine future forest dynamics and ecosystem services. The combination of novel environmental conditions (e.g. climate change), a reintroduced tree species and new disturbance regimes (e.g. exotic insect pests, fire suppression) have no analog in the past that can be used to parameterize phenomenological models. We therefore used a mechanistic approach within the LANDIS-II forest landscape model that relies on physiological first principles to project forest dynamics as the outcome of competition of tree cohorts for light and water as a function of temperature, precipitation, CO2 concentration, and life history traits. We conducted a factorial landscape simulation experiment to evaluate specific hypotheses about future forest dynamics in two study sites in the center of the former range of chestnut. Our results supported the hypotheses that climate change would favor chestnut because of its optimal temperature range and relative drought resistance, and that chestnut would be less competitive in the more mesic Appalachian Plateau province because competitors will be less stressed. The hypothesis that chestnut will increase carbon stocks was supported, although the increase was modest. Our results confirm that aggressive restoration is needed regardless of climate and soils, and that increased aggressiveness of chestnut restoration increased biomass accumulation. The hypothesis that chestnut restoration will increase both compositional and structural richness was not supported because chestnut displaced some species and age cohorts. Although chestnut restoration did not markedly enhance carbon stocks, our findings provide hope that this formerly important species can be successfully reintroduced and associated ecosystem services recovered.