C. Ashton Drew

Recommendations for assessing the effectiveness of surrogate species approaches

Surrogate species approaches, including flagship, focal, keystone, indicator, and umbrella, are considered an effective means of conservation planning. For conservation biologists to apply surrogates with confidence, they must have some idea of the effectiveness of surrogates for the circumstances in which they will be applied. We reviewed tests of the effectiveness of surrogate species planning to see if research supports the development of generalized rules for (1) determining when and where surrogate species are an effective conservation tool and (2) how surrogate species should be selected such that the resulting conservation plan will effectively protect biodiversity or achieve other conservation goals. The context and methods of published studies were so diverse that we could not draw general conclusions about the spatial or temporal scales, or ecosystems or taxonomic groups for which surrogate species approaches will succeed. The science of surrogate species can progress by (1) establishing methods to compare diverse measures of effectiveness; (2) taking advantage of data-rich regions to examine the potential effectiveness of surrogate approaches; (3) incorporating spatial scale as an explanatory variable; (4) evaluating surrogate species approaches at broader temporal scales; (5) seeking patterns that will lead to hypothesis driven research; and (6) monitoring surrogate species and their target species.

Predictive Species and Habitat Modeling in Landscape Ecology

Foreword (Jianguo Liu*) Introduction (Ashton Drew*, Falk Huettmann*, Yolanda Wiersma*) Current State of Knowledge 1. Statistical, ecological and data models (Nicolette Cagle, Mike Austin) 2. The state of spatio-temporal statistical modeling in ecology (Mevin Hooten*) Integration of Ecological Theory into Modeling Practice 3. Linking ecological theory with species-habitat modeling (Alexandre Hirzel*) 4. The role of assumption in predictions of habitat availability and quality (Ed Laurent*) 5. Habitat quality and ecological theory: the importance of variation in space and time (Robert Fletcher*) 6. Data management as the scientific foundation for modeling (Falk Huettmann* and Benjamin Zuckerberg*) Simplicity, Complexity, and Uncertainty in Applied Models 7. Variation, use, and mis-use of statistical models: effects on the interpretation of research results (Yolanda Wiersma*) 8. Modeling landcover pattern and change using Random Forest (Jeffrey Evans) 9. Focused assessment of scale-dependent vegetation pattern (Todd Lookingbill) 10. Understanding year-to-year inconsistency in bird-landscape relations: the influence of life-history traits and model selection uncertainty (Sam Riffel) 11. Boreal toad (Bufo boreas boreas) population connectivity in Yellowstone National Park: quantifying matrix resistance and model uncertainty using landscape genetics (Melanie Murphy*) 12. Assessment of how fine-scale expert opinion improves large-scale regional species distribution models (Ashton Drew*) Designing Models for Increased Utility 13. Integrating and improving GAP wildlife habitat models with IFMAP, Michigans forest management decision support environment (Jay Roberts*) 14. Linking modeling to adaptive management (Tom Nudds) 15. Linking spatially explicit predictions with models in strategic conservation planning, forecasting and cumulative impact assessments (Joshua Lawler*, Falk Huettmann*, Yolanda Wiersma*) Conclusion and Outlook (Ashton Drew*, Falk Huettmann*, Yolanda Wiersma*)

Addressing the interplay of poverty and the ecology of landscapes: a Grand Challenge Topic for landscape ecologists?

We argue for the landscape ecology community to adopt the study of poverty and the ecology of landscapes as a Grand Challenge Topic. We present five areas of possible research foci that we believe that landscape ecologists can join with other social and environmental scientists to increase scientific understanding of this pressing issue: (1) scale and poverty; (2) landscape structure and human well-being; (3) social and ecological processes linked to spatial patterns in landscapes; (4) conservation and poverty, and (5) applying the landscape ecologist’s toolkit. A brief set of recommendations for landscape ecologists is also presented. These include the need to utilize broad frameworks that integrate social and ecological variables, build capacity to do this kind of work through the development of strong collaborations of researchers in developed and developing countries, create databases in international locations where extreme poverty exists, and create a new generation of researchers capable of addressing this pressing social and environmental issue.

Expert Knowledge as a Basis for Landscape Ecological Predictive Models

Defining an appropriate role for expert knowledge in science can lead to contentious debate. The professional experience of ecologists, elicited as expert judgment, plays an essential role in many aspects of landscape ecological science. Experts may be asked to judge the relevance of competing research or management questions, the quality and suitability of available data, the best balance of complexity and parsimony, and the appropriate application of model output. Even the initial decision to pursue modeling follows expert judgment regarding the cost and benefits of a model relative to data collection and the suitability of alternative modeling approaches for the specific application. Increasingly, however, professionals are asked to provide expertise to complement or even substitute for scarce data in landscape ecological models, by quantifying their personal experiences and anecdotal observations. In such cases, the professional is asked to reference their knowledge against geospatial data or landscape metrics derived from such data. We offer our chapter to raise awareness and promote discussion of this particular development within landscape ecological modeling. We draw examples from cases where expertise is provided as data in support of the predictive species-habitat models used to inform conservation planning objectives and strategies.

Experts, Expert Knowledge, and Their Roles in Landscape Ecological Applications

In an attempt to develop a forest succession model that simulates scenarios of future landscape patterns, researchers encounter many gaps in the published knowledge of forest succession trajectories. They resort to consulting local foresters and using their knowledge of forest succession to parameterize the model. In another situation, management of an elusive bird species requires estimates of the likelihood of its occurrence under specific sets of site conditions. Because the habitat characteristics of this species are not well studied or have not been published, the investigators seek the advice of specialist wildlife biologists to learn where these birds could potentially occur. In yet another case, natural resource and conservation professionals turn to expert knowledge to help them conserve or manage wildlife habitats in high-risk environments, and researchers investigate the relative merits of that expert knowledge in comparison with empirical data, as well the uncertainty and variability in expert-based predictions.

Currents, landscape structure, and recruitment success along a passive–active dispersal gradient

There exists a gradient in dispersal behavior from passive to active, which reflects organisms’ dependence upon currents vs. self-propelled movement. We asked: Do currents modify organism–landscape interactions to influence recruitment success along this dispersal gradient? Using a spatially-explicit cellular model, we simulated the recruitment success of three generalized dispersal strategies (walkers, swimmers, and drifters) through hierarchically structured benthic landscapes. We evaluated the relative recruitment success (recruited population size, overall area occupied, time to recruit) of the three dispersal strategies in similar landscapes, as well as the consequences of varying the total proportion of habitat suitable for recruitment, and the scale and pattern of habitat patchiness on recruitment success. In the presence of currents, swimmers and drifters generally recruited over larger areas and in less time than walkers. Differences among the dispersal strategies’ recruitment success were most pronounced when an intermediate number of good habitat cells (16–48% of landscape) were broadly dispersed across the landscape. Although recruitment success always increased with increasing proportion of good habitat, drifters were more sensitive, and swimmers less sensitive, to these landscape changes than walkers. We also found that organisms dispersing within currents typically responded non-linearly (logarithmically or exponentially) to increasing proportion of total good habitat, whereas walkers more often responded linearly.

King Rail (Rallus elegans) Occupancy and Abundance in Fire Managed Coastal Marshes in North Carolina and Virginia

Abstract. Curbing the declining trends of King Rails (Rallus elegans) that occupy freshwater emergent marshes requires an understanding of their ecology and response to management practices. King Rails were surveyed during the breeding season (March-June) at Back Bay and Mackay Island National Wildlife Refuges, Virginia and North Carolina, in 2009 and 2010. Twenty-two plots were surveyed in 2009 and 41 in 2010. Annual occupancy estimates were based on pooled data encompassing both refuges. In 2010, occupancy and abundance of King Rails were also estimated for each refuge and assessed with respect to fire management. Plots in 2010 were classified as recently burned (0–1 years-since-burn [YSB]) or ≥ 2 YSB. Occupancy probability was similar between 2009 (0.68 ± 0.14) and 2010 (0.62 ± 0.08). In 2010, occupancy probability was higher at Mackay Island (0.95 ± 0.06) than Back Bay (0.69 ± 0.13). Mean plot abundance (Mackay Island = 1.47 ± 0.38; Back Bay = 0.66 ± 0.22) was also higher. The probability of occupying 0–1 YSB plots was higher at both refuges (Mackay Island = 0.95 ± 0.06; Back Bay = 0.72 ± 0.20) when compared to ≥ 2 YSB plots (Mackay Island = 0.69 ± 0.13; Back Bay = 0.25 ± 0.12). Location strongly influenced occupancy of King Rails. It is plausible that the marsh composition (natural vs. created) accounted for the observed differences in occupancy. Natural marshes may provide higher quality habitat (e.g., resource availability) for King Rails than created marshes.

Expert Knowledge as a Foundation for the Management of Secretive Species and Their Habitat

In this chapter, we share lessons learned during the elicitation and application of expert knowledge in the form of a belief network model for the habitat of a waterbird, the King Rail (Rallus elegans). A belief network is a statistical framework used to graphically represent and evaluate hypothesized cause and effect relationships among variables. Our model was a pilot project to explore the value of such a model as a tool to help the US Fish and Wildlife Service (USFWS) conserve species that lack sufficient empirical data to guide management decisions. Many factors limit the availability of empirical data that can support landscape-scale conservation planning. Globally, most species simply have not yet been subject to empirical study (Wilson 2000). Even for well-studied species, data are often restricted to specific geographic extents, to particular seasons, or to specific segments of a species’ life history. The USFWS mandates that the agency’s conservation actions (1) be coordinated across regional landscapes, (2) be founded on the best available science (with testable assumptions), and (3) support adaptive management through monitoring and assessment of action outcomes. Given limits on the available data, the concept of “best available science” in the context of conservation planning generally includes a mix of empirical data and expert knowledge (Sullivan et al. 2006).

Indicator-Driven Conservation Planning Across Terrestrial, Freshwater Aquatic, and Marine Ecosystems of the South Atlantic, USA

Abstract Systematic conservation planning, a widely used approach to identify priority lands and waters, uses efficient, defensible, and transparent methods aimed at conserving biodiversity and ecological systems. Limited financial resources and competing land uses can be major impediments to conservation; therefore, participation of diverse stakeholders in the planning process is advantageous to help address broad-scale threats and challenges of the 21st century. Although a broad extent is needed to identify core areas and corridors for fish and wildlife populations, a fine-scale resolution is needed to manage for multiple, interconnected ecosystems. Here, we developed a conservation plan using a systematic approach to promote landscape-level conservation within the extent of the South Atlantic Landscape Conservation Cooperative. Our objective was to identify the highest-ranked 30% of lands and waters within the South Atlantic deemed necessary to conserve ecological and cultural integrity for the 10 prim...

Conclusion: An Attempt to Describe the State of Habitat and Species Modeling Today

We set out to deliver a book that would prompt increased attention to the ecological theory and assess the relevant assumptions that underlie predictive landscape-scale species and habitat modeling. We invited international authors who are actively engaged in advancing the discipline of predictive modeling in landscape ecology to provide chapters that would not only highlight current developments and identify outstanding gaps, but which would also reflect on how methodological choices were informed by ecological theory. In this manner, we have provided readers not with a “how-to” guide that will rapidly become outdated as methods advance, but rather insights into the thought processes, reasoning, and current debates that are common across modeling projects and methods. Such extended reflections help to show multiple viewpoints and stimulate new ideas; they rarely find space in published research manuscripts. However, we believe these will offer valuable guidance to both novice and advanced modelers seeking to discern trade-offs between alternative modeling approaches.