Restoration and conservation innovations face numerous challenges that often limit widespread adoption, including uncertainty of outcomes, risk averse or status quo biased management, and unknown trade‐offs. These barriers often result in cautious conservation that does not consider the true cost of impeding innovation, and overemphasizes the risks of unintended consequences versus the opportunities presented by proactive and innovative conservation, the intended consequences. Simulation models are powerful tools for forecasting and evaluating the potential outcomes of restoration or conservation innovations prior to on‐the‐ground deployment. These forecasts provide information about the potential trade‐offs among the risks and benefits of candidate management actions, elucidating the likelihood that an innovation will achieve its intended consequences and at what cost. They can also highlight when and where business‐as‐usual management may incur larger costs than alternative management approaches over the long‐term. Forecasts inform the decision‐making process prior to the implementation of emergent, proactive practices at broad scales, lending support for management decisions and reducing the barriers to innovation. Here we review the science, motivations, and challenges of forecasting for restoration and conservation innovations.

Despite frequently being implicated in species declines, agricultural lands may nonetheless play an important role in connecting wildlife populations by serving as movement corridors or stopover sites between areas of high-quality habitat. For many North American bird species, agricultural intensification over the past half century has substantially impacted populations, yet recent studies have noted the potential for supporting avian biodiversity on agricultural lands through the promotion of functional connectivity. To support avian conservation efforts on agricultural lands across the United States, we used publicly available data from eBird to quantify and map the effects of agriculture on habitat suitability (using random forest models) and functional connectivity (via circuit theory) for three focal species that have experienced agriculture-linked declines or range contractions in recent decades: Greater Sage-grouse (Centrocercus urophasianus), American Black Duck (Anas rubripes), and Bobolink (Dolichonyx oryzivorus). Our analysis drew on novel, remotely sensed estimates of agricultural management intensity to quantify the effects of management practices on avian habitat and movement, revealing complex, species-specific relationships between agriculture and habitat value for the three focal species. Rangelands and croplands exhibited relatively high connectivity values for Greater Sage-grouse and Bobolink, respectively, mirroring these species’ strong habitat preferences for open sagebrush and cultivated grasslands. By contrast, American Black Duck migratory connectivity was low on all agricultural cover types. Mapping our model results across each species’ geographic range in the U.S. revealed key areas for agricultural management action to preserve high-quality habitat and connectivity, and we link these spatial recommendations to government incentive programs that can be used to increase wildlife-friendly management on U.S. agricultural lands.

Background: Increased drought due to climate change will alter fire regimes in mesic forested landscapes where fuel moisture typically limits fire spread, and where fuel loads are consistently high. These landscapes are often extensively modified by human land use change and management. We forecast the influence of varying climate scenarios on potential shifts in the wildfire regime across the mesic forests of the Southern Appalachians. This area has a long history of fire exclusion, land use change, and an expanding wildland urban interface. We considered interactions among climate, vegetation, and anthropogenic influences to forecast future fire regimes and changes to the forest structure. We used climate scenarios representing divergent drought patterns (overall drought trend and interannual variability) within a process-based fire model that captures the influence of climate, fuels, and fire ignition patterns and suppression. Results: Compared to simulations using historical climate (1972-2018), future total burned area (2020-2100) increased by 42.3 % under high drought variability, 104.8 % under a substantial increase in drought severity, and 484.7 % when combined. Landscape patterns of fire exclusion and suppression drove the spatial variability of fire return intervals (FRI). Our projections indicate wide spatial variability in future fire regimes with some areas experiencing multiple fires per decade while others experience no fire. More frequent fires corresponded with increased oak prevalence and a reduction in the biomass of mesic hardwoods and maple; however, mesic hardwoods remained prevalent under all fire intervals because of their contemporary dominance. Conclusions: Our study illustrates how future drought-fire-management interactions and a history of fire exclusion could alter future fire regimes and tree species composition. We find that increasing trends in drought magnitude and variability may increase wildfire activity, particularly in areas with minimal fire suppression. In ecosystems where fuel moisture (and not load) is the standard limitation to fire spread, increased pulses of drought may provide the conditions for more fire activity, regardless of effects on fuel loading. We conclude the effects of climate and human management will determine the novel conditions for both fire regime and ecosystem structure.

Maintaining and enhancing landscape connectivity reduces biodiversity declines due to habitat fragmentation. Uncertainty remains, however, about the effectiveness of conservation for enhancing connectivity for multiple species on dynamic landscapes, especially over long time horizons. We forecasted landscape connectivity from 2020 to 2100 under four common conservation land‐acquisition strategies: acquiring the lowest cost land, acquiring land clustered around already established conservation areas, acquiring land with high geodiversity characteristics, and acquiring land opportunistically. We used graph theoretic metrics to quantify landscape connectivity across these four strategies, evaluating connectivity for four ecologically relevant species guilds that represent endpoints along a spectrum of vagility and habitat specificity: long‐ versus short‐distance dispersal ability and habitat specialists versus generalists. We applied our method to central North Carolina and incorporated landscape dynamics, including forest growth, succession, disturbance, and management. Landscape connectivity improved for specialist species under all conservation strategies employed, although increases were highly variable across strategies. For generalist species, connectivity improvements were negligible. Overall, clustering the development of new protected areas around land already designated for conservation yielded the largest improvements in connectivity; increases were several orders of magnitude beyond current landscape connectivity for long‐ and short‐distance dispersing specialist species. Conserving the lowest cost land contributed the least to connectivity. Our approach provides insight into the connectivity contributions of a suite of conservation alternatives prior to on‐the‐ground implementation and, therefore, can inform connectivity planning to maximize conservation benefit.