Coupled networks of permanent protected areas and dynamic conservation areas for biodiversity conservation under climate change

The complexity of climate change impacts on ecological processes necessitates flexible and adaptive conservation strategies that cross traditional disciplines. Current strategies involving protected areas are predominantly fixed in space, and may on their own be inadequate under climate change. Here, we propose a novel approach to climate adaptation that combines permanent protected areas with temporary conservation areas to create flexible networks. Previous work has tended to consider permanent and dynamic protection as separate actions, but their integration could draw on the strengths of both approaches to improve biodiversity conservation and help manage for ecological uncertainty in the coming decades. As there are often time lags in the establishment of new permanent protected areas, the inclusion of dynamic conservation areas within permanent networks could provide critical transient protection to mitigate land-use changes and biodiversity redistributions. This integrated approach may be particularly useful in highly human-modified and fragmented landscapes where areas of conservation value are limited and long-term place-based protection is unfeasible. To determine when such an approach may be feasible, we propose the use of a decision framework. Under certain scenarios, these coupled networks have the potential to increase spatio-temporal network connectivity and help maintain biodiversity and ecological processes under climate change. Implementing these networks would require multidisciplinary scientific evidence, new policies, creative funding solutions, and broader acceptance of a dynamic approach to biodiversity conservation

Spread of networked populations is determined by the interplay between dispersal behavior and habitat configuration

Predicting the spread of populations across fragmented habitats is vital if we are to manage their persistence in the long term. We applied network theory with a model and an experiment to show that spread rate is jointly defined by the configuration of habitat networks (i.e., the arrangement and length of connections between habitat fragments) and the movement behavior of individuals. We found that population spread rate in the model was well predicted by algebraic connectivity of the habitat network. A multigeneration experiment with the microarthropod Folsomia candida validated this model prediction. The realized habitat connectivity and spread rate were determined by the interaction between dispersal behavior and habitat configuration, such that the network configurations that facilitated the fastest spread changed depending on the shape of the species' dispersal kernel. Predicting the spread rate of populations in fragmented landscapes requires combining knowledge of species-specific dispersal kernels and the spatial configuration of habitat networks. This information can be used to design landscapes to manage the spread and persistence of species in fragmented habitats.ISSN:0027-8424ISSN:1091-649

Data from: Incorporating uncertainty into forest management planning: timber harvest, wildfire and climate change in the boreal forest

In an effort to ensure the sustainability of their forests, boreal forest managers often use forest planning models to make future projections of timber supply and other key services, such as habitat for wildlife. Projecting the fate of these services has proven to be challenging, however, as major uncertainties exist regarding the principal drivers of boreal ecosystem dynamics, including the future spatial and temporal distribution of wildfire and timber harvesting. Existing forest planning models are not well suited to dealing with this uncertainty because they produce deterministic projections based on central tendencies of these drivers. Here we present a new approach for incorporating uncertainty into forest management planning, which we demonstrate using two landscapes in the Canadian boreal forest. Our approach takes the assumptions contained within the latest forest management plans for each of these landscapes, including parameterizations of their deterministic forest planning models, and converts these assumptions into equivalent parameterizations of a stochastic, spatially-explicit state-and-transition simulation model (STSM). We then use Monte Carlo simulations with the STSM to “stress-test” the forest management plan with respect to a range of possible future uncertainties, including uncertainties in future levels and patterns of wildfire and timber harvest, along with the possible changes in wildfire that might result from future climate change. Our analysis demonstrates the importance of incorporating stochastic variability into projections of future ecosystem condition. The STSM projections that acknowledged variability in wildfire and timber harvest differed from the deterministic forest planning model projections that were based solely on mean values. Our analysis also suggests that there is an increased risk of shortfalls in timber harvest, for both boreal landscapes, associated with future projections for changes in wildfire due to climate change, and that management strategies aimed at reducing the future level of timber harvest offer an opportunity to mitigate these risks. We believe our approach provides a new risk-based framework for incorporating uncertainty into forest management, including the effects of climate change

State-and-transition simulation model files for ST-Sim software

As outlined in Appendix S1, this file contains all of the files required to run the simulations in the paper using the ST-Sim softwar