A Risk-Based Approach to Evaluating Wildlife Demographics for Management in a Changing Climate: A Case Study of the Lewis’s Woodpecker
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- Towler, E., Saab, V.A., Sojda, R.S. et al. Environmental Management (2012) 50: 1152. doi:10.1007/s00267-012-9953-z
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Given the projected threat that climate change poses to biodiversity, the need for proactive response efforts is clear. However, integrating uncertain climate change information into conservation planning is challenging, and more explicit guidance is needed. To this end, this article provides a specific example of how a risk-based approach can be used to incorporate a species’ response to climate into conservation decisions. This is shown by taking advantage of species’ response (i.e., impact) models that have been developed for a well-studied bird species of conservation concern. Specifically, we examine the current and potential impact of climate on nest survival of the Lewis’s Woodpecker (Melanerpes lewis) in two different habitats. To address climate uncertainty, climate scenarios are developed by manipulating historical weather observations to create ensembles (i.e., multiple sequences of daily weather) that reflect historical variability and potential climate change. These ensembles allow for a probabilistic evaluation of the risk posed to Lewis’s Woodpecker nest survival and are used in two demographic analyses. First, the relative value of each habitat is compared in terms of nest survival, and second, the likelihood of exceeding a critical population threshold is examined. By embedding the analyses in a risk framework, we show how management choices can be made to be commensurate with a defined level of acceptable risk. The results can be used to inform habitat prioritization and are discussed in the context of an economic framework for evaluating trade-offs between management alternatives.
KeywordsClimate change Species vulnerability Adaptation Avian conservation Forest management Risk
Natural-resource managers are becoming increasingly concerned about the projected threat that climate change poses to ecosystems. Ominous predictions for biodiversity loss (Sala and others 2000; Thomas and others 2004) have underscored the need for proactive response efforts, but addressing climate change is challenging because of incomplete knowledge of species’ responses to climate variation and uncertainties in future climate conditions. Nevertheless, natural-resource decision makers are increasingly being asked to consider climate change in their planning [United States Department of Agriculture (USDA) 2011; United States Department of Interior (USDI) 2009], and guidance for practitioners is emerging (West and others 2009). However, most recommendations to adapt conservation to climate change have come in the form of general principles and lack the specificity needed to be “actionable” in practice (Heller and Zavaleta 2009). As such, explicit examples of how to integrate climate impacts on biodiversity into conservation planning are needed.
For resource managers, vulnerability assessments have emerged as a key tool to understand how species will respond to climate change and to inform adaptation planning (Dawson and others 2011; Glick and others 2011). As defined by the Intergovernmental Panel on Climate Change (IPCC 2007), vulnerability includes three components: sensitivity, exposure, and adaptive capacity. In terms of species vulnerability, sensitivity indicates how tolerant a species is to changing conditions, whereas exposure is the degree to which a species will experience those conditions. Adaptive capacity refers to a species’ potential to decrease their exposure or sensitivity. The traditional approach to assess climate change impacts on biodiversity has been through the use of bioclimatic envelope, or species distribution, models (Guisan and Thuiller 2005). These mainly identify exposure to climate change and provide spatially explicit shifts in species or ecosystem ranges. More recently, data, modeling, and resource limitations have motivated conservation organizations and management agencies to develop relative vulnerability indices (e.g., Bagne and others 2011; see examples in Rowland and others 2011). These question-based nonspatial assessments use a systematic evaluation framework to consider exposure, sensitivity, and adaptive capacity. The approaches are complementary because both have advantages and limitations (Rowland and others 2011), but there is a pressing need to consider all three components of vulnerability, from multiple sources, in a more integrated manner (Dawson and others 2011). To this end, conceptual vulnerability frameworks have been proposed for human–environment systems (Turner and others 2003) and specifically for species (Williams and others 2008). However, despite these conceptual advances, there is still a paucity of concrete, operational examples of adaptation principles that consider climate uncertainty (Heller and Zavaleta 2009).
Given future climate uncertainty, the use of climate scenarios has emerged as a viable tool to guide conservation (Peterson and others 2003), but how to “best” incorporate climate scenarios in adaptation planning is context dependent (Dessai and others 2005). One widespread tactic has been the “top-down” approach put forth by the IPCC, where climate scenarios are derived from downscaled projections of multiple Atmosphere Ocean General Circulation Models under different emission pathways. Subsequently, these climate scenarios are applied to impact models, such as the aforementioned species distribution models. These evaluations are useful for characterizing the consensus and range of potential impacts based on the best climate science available. However, they can be difficult to use directly in adaptation due to uncertainties in climate projections and impact models (Wilby and Dessai 2010), and they often do not address all of the factors (e.g., social, economic) or spatial scales relevant to adaptation (Burton and others 2002). An alternative is to adopt a risk-based approach (e.g., Yohe and Leichenko 2010), which has been identified as the most appropriate overarching framework for adapting to climate change (Jones and Preston 2011). Jones (2001) outlines a risk-management framework for climate impact assessments; this approach also examines impacts based on climate scenarios (although not necessarily directly derived from climate models), but it focuses more on stakeholder involvement and conveys risks relevant to decision-making by organizing analyses around the likelihood of exceeding critical thresholds. Risk management offers a systematic way to weigh likelihood and consequence, but it is also flexible in its ability to incorporate a range of approaches appropriate to different adaptation contexts (Jones and Preston 2011).
In this article, we provide a specific example of using a risk-based approach to link a species’ response to climate with conservation decisions. To demonstrate the approach, we take advantage of species’ response (i.e., impact) models that have been developed for a well-studied bird species of conservation concern. Specifically, we examine the current and potential impact of climate on nest survival of the Lewis’s Woodpecker in two different habitats. For climate scenarios, we manipulate historical weather observations to create ensembles (i.e., multiple sequences of weather events) that reflect historical variability and potential climate change. These ensembles allow for a probabilistic evaluation of the risk posed to Lewis’s nest survival. To this end, two demographic analyses are conducted: (1) the relative value of each habitat is compared in terms of nest survival; and (2) the likelihood of exceeding a critical population threshold is examined for one habitat. To support habitat management, the analyses are embedded in a risk framework where a level of acceptable risk is defined, and the climate scenarios are used to identify the climate shift that corresponds to that level. The results can be used to inform habitat prioritization and are discussed in the context of an economic framework for evaluating trade-offs between management alternatives.
Case Study Overview
In this article, the Lewis’s Woodpecker (Melanerpes lewis), a well-studied bird species of conservation concern, is used to demonstrate how to link a species’ response to climate with conservation decisions. This section provides a brief background on Lewis’s Woodpecker followed by a description of previously published species’ response (i.e., impact) models that are used for the evaluation. Finally, we examine future climate projections for the case study area.
Lewis’s Woodpecker (M. lewis) populations are potentially decreasing at both regional and local scales (Tobalske 1997). Consequently, the species has been designated as a species of conservation concern by several state and federal agencies (Neel 1999; Ritter 2000; United States Fish and Wildlife Service 2008; USDA 2009). This woodpecker is found in open woodlands throughout the western United States, and the most productive nesting habitats are burned pine forests (Bock 1970; Raphael and White 1984; Tobalske 1997; Linder and Anderson 1998; Saab and others 2011) as well as riparian woodlands composed of cottonwoods (Populus spp.) (Saab and Vierling 2001) and aspens (Populus tremuloides) (Newlon and Saab 2011).
Species’ Response Models
Characteristics of the field-monitoring studies and best-fit nest survival models
Newlon and Saab (2011)
Saab et al. (2011)
Aspen riparian woodlands
Burned conifer forests
Weather station (COOPID)b
Craters of the Moon, ID (102260)
Idaho City, ID (104442)
Best-fit logit model:
Intercept Cfc (Cl)d
3.4 (0.68, 6.2)
8.8 (6.9, 10.7)
TMX Cfc (Cl)d
0.19 (0.07, 0.31)
−0.038 (−0.060, −0.016)
PCP Cfc (Cl)d
−0.21 (−0.31, −0.11)
NID Cfc (Cl)d
−0.18 (−0.25, −0.11)
PFP Cfc (Cl)d
−0.80 (−1.2, −0.41)
Climate Change Projections
The preceding response models show that Lewis’s Woodpeckers are sensitive to climate conditions, in particular, temperature and precipitation. To assess how future climate conditions might impact this species, we examined climate change projections to 2050 for the case study area. This information serves to inform our climate scenario development (see “Climate Scenarios”).
Both case study sites are located in Idaho (Fig. 1), which is part of the Pacific Northwest region (PNW). PNW projections have been extensively analyzed (Mote and Salathé 2010), but given the localized nature of this assessment, it was desirable to also focus on the study area using a new high-resolution, dynamically downscaled data set. We analyzed 36 km-resolution simulations from the National Center for Atmospheric Research (NCAR) Nested Regional Climate Model (NRCM; Holland and others 2010) embedded in the global Community Climate System Model (Collins and others 2006) for the A2 greenhouse gas (GHG) emission pathway. We compared daily NRCM simulations of current and future climate for southern Idaho (dashed box in Fig. 1) for the nesting period. Two time slices were examined: 2020–2030 to estimate 2025 and 2045–2055 to estimate 2050. The NRCM provides changes in maximum daily temperatures, which are required by the response models, and show an average daily TMX increase of 3.0 °C by 2050, and 1.0 °C by 2025 (figures not shown). These results are consistent with PNW multimodel results, which report average summer seasonal increases of 2.7 °C by the 2040s, ranging from 1 to 4.5 °C [estimated from Fig. 9 in Mote and Salathé (2010) for the A1B GHG emission pathway; we note that the A1B and A2 GHG emission pathways are nearly identical up to 2050].
Probabilistic Risk Analysis
For the risk analysis, first we develop climate scenarios that provide a probabilistic estimate of the impact response. Second, we describe two demographic habitat assessments that use the climate scenarios in conjunction with the species’ response models.
Climate scenarios were developed by manipulating historical weather observations to create multiple “new” sequences of daily weather (i.e., ensembles) during the nesting season. To provide a robust characterization of climate variability as a baseline, we first create a climate scenario that reflects historical or “natural” variability. To this end, we obtained weather observations of daily TMX and PCP data from 1959 to 2009 from both weather stations (Table 1) from the National Climatic Data Center (available at: http://lwf.ncdc.noaa.gov/oa/climate/stationlocator.html). Specifically, data beginning on May 29 (NID = 7) through the N = 51 day nesting period (Newlon and Saab 2011) was used. We used these historical data to simulate the ensembles for the natural variability climate scenario and, subsequently, to derive the climate change scenarios.
To generate the natural variability scenario, we adopt a simple daily disaggregation technique based on resampling historical proportional vectors (Nowak and others 2010). The stochastic disaggregation approach has been well tested for streamflow (Nowak and others 2010; Towler 2010), and the step-by-step approach is detailed in Online Reference 1. In short, the natural variability climate scenario is comprised of 250 ensembles, where each ensemble is comprised of PCP and TMX for the 51 day nesting season. The approach faithfully preserved the historical distributional statistics for PCP and TMX at the seasonal and daily timescales; sample validation results are shown in Online Reference 2.
Next, climate change scenarios are developed. We derive two independent scenario sets from the natural variability scenario that were relevant to each of the forthcoming assessments: the delta TMX and the above-PCP scenarios.
Delta TMX Scenarios
Although modeled precipitation response remains uncertain, extremes are likely to increase (see “Climate Change Projections”). Through 2050, natural variability is likely to play a dominant role, especially at smaller spatial and temporal scales (Hawkins and Sutton 2009). Given these findings, natural variability and extreme values are the most important considerations for PCP.
Demographic Assessment by Habitat
Comparison of Alternatives
The delta TMX scenarios were used to compare the current and potential impact of climate on nest survival of the Lewis’s Woodpecker in two different habitats. In other words: Is aspen or burned pine habitat more valuable in terms of nest survival? For decision making, this information is useful for prioritization of habitat conservation and restoration. For simplicity, we consider only the survival of Lewis’s Woodpecker, as measured by OSR, in valuing these habitats.
The OSR distributions of each habitat are compared using a nonparametric significance test. The more the distributions overlap, the more similarly the habitats are valued. Conversely, as the OSR distributions diverge, one habitat gains import over the other [assuming constant habitat quality (see discussion in “Strengths and Limitations”)]. The underlying concept of this approach is the basis for many well-known parametric significance tests (e.g., Student t test), but the nonparametric approach is appealing for its lack of assumptions, intuitive nature, and ability to be paired with stochastic simulations. The approach does require identifying a subjective confidence level; here we examine three cases: 50 % (case A), 75 % (case B), and 95 % (case C). Case A is the tipping point, where at least 50 % of one distribution (i.e., the median) is above the other. Case B, a moderate decision threshold, is achieved when at least 75 % of the distribution is offset (i.e., the 75th percentile of one is above the 25th percentile of the other), and so on for the more stringent case C. Although subjective, the confidence level can be chosen by a decision maker to be commensurate with the risk she or he is willing to take. Alternatively, the overlap fraction can be read directly and used to set decision weights. For example, case B could justify allocating 75 % (25 %) of available resources to projects that protect/improve the better (worse) habitat.
Threshold Exceedance Likelihood
The above-PCP scenarios were used to evaluate the likelihood of exceeding a critical threshold, which can serve as relevant organizing points for quantifying risk (Jones 2001). Here, we focus on the early-burn pine and examine the likelihood that the habitat will be a population sink for the Lewis’s Woodpecker, that is, that the habitat will not be adding an increasing number of recruits to the overall population. Calculated from Saab and Vierling (2001), early-burn pine is a sink if the OSR value is <0.49. From the OSR distribution, we can directly obtain the percent of data below the sink threshold, thus resulting in a measure of the associated risk. Unfortunately, this type of threshold information was not available for the late-burn pine or aspen habitats for comparison.
In this case, whether or not the early-burn pine habitat is a population sink is primarily sensitive to extreme PCP events; thus, the above-PCP scenarios are appropriate. Other demographic processes (e.g., predation) that potentially affect source–sink relationships were not considered here.
Daily TMX increase that would achieve each risk-based confidence level (i.e., case) for overall survival rate
Delta TMX (C)
Aspen versus pine (early-burn)
Aspen versus pine (late-burn)
Case A (50th and 50th tie)
Case B (25th and 75th tie)
Case C (5th and 95th tie)
Given that the purpose of this study was to integrate climate impacts on biodiversity into decision-making, the next step is to explore how the results from the demographic analyses can be incorporated into conservation planning. We begin by considering the results listed in Table 2 in light of the climate change projections examined in “Climate Change Projections”. First, we consider a comparison of aspens with early-burn pine (i.e., the first 4 years after a burn). Although increasing temperatures are associated with increased nest survival in aspen habitat, if we use the NRCM 2050 projection of 3 °C as a guide, then early-burn pine is likely to remain more valuable in terms of nest survival than aspen habitat because aspen does not satisfy case A until 4 °C (Table 2). One caveat is that Lewis’s Woodpecker nesting in early-burn pines is vulnerable to high (extreme) precipitation events, thus the habitat’s value is decreased under wetter precipitation outlooks. For example, if one accepts a population sink risk ≤10 %, then the early-burn pine remains the top priority habitat unless the above-PCP forecast is >60 %. Barring that scenario, it seems that the early-burn habitat’s dominance will be robust to temperature increases projected up to 2050. Subsequently, the evaluation between aspens and late-burn pine (i.e., 5 to 12 years after a fire) comes into play. Under natural variability, late-burn pine holds a slight advantage, although its lead is tenuous using the NRCM 2025 projection of 1 °C as a guide (Table 2). Aspen achieves the moderate and stringent cases with 4 °C and 5 °C, respectively, which is approximately the upper range of the regional multimodel projections. Although other factors must be considered, our results provide managers with quantitative information that could be integrated into an adaptive-management design and can help managers place their objectives, actions, and evaluations into a long-term perspective.
Decision matrix between two choices: (1) allow clear-cutting in burned pine and use the funds to restore aspen or (2) do not allow clear-cutting
Favors choice (1)
Favors choice (2)
the value of W is small (large);
net revenues from logging, L−R, are large (small); and/or
the survival rate advantage of burned pine over aspens is small (large).
the value of W is large;
net losses, L−R, are small (i.e., less negative); and/or
the survival rate advantage of aspens compared with burned pine is large (i.e., right-hand side is more negative).
Note that the decision matrix includes a criterion to calculate a difference between OSR values. Because of the stochastic approach taken here, whether that difference is positive or negative can be determined by its achievement of the aforementioned confidence levels (i.e., cases A, B, and C).
We present this example to illustrate how climate information can be applied to wildlife demographic data and used to inform management decisions. An actual land-management application likely would be more complex than the example outlined here. In the real world, input from stakeholders would be critical to define the relevant set of options and, most likely, more than two alternatives must be considered. We also acknowledge that the type of environmental valuation alluded to here, i.e., attaching a dollar value to the survival of Lewis’s woodpeckers in a particular region, is both technically difficult and perhaps philosophically objectionable to some. However, land-management alternatives inherently involve trade-offs across competing objectives. Careful attempts to make trade-offs explicit enhance transparency and force a more frank discussion of how to consider trade-offs under different contexts.
Strengths and Limitations
Although it is not meant to be a comprehensive comparison between all Lewis’s Woodpecker habitats, our framework provides an important step toward bridging the gap between climate impacts and management actions. However, because the analysis aims to inform decision making, the results must be reviewed with awareness of the associated limitations. Here, we discuss how this study handles two key concepts that have impeded previous adaptation planning efforts: (1) uncertainties in future climate conditions and (2) how species will respond to climate variation.
To address uncertainties in future climate conditions, we used climate scenarios. First, emphasis was placed on characterizing natural variability, which was subsequently used to develop the climate change scenarios. The climate change scenarios were developed to be relevant to the demographic assessments and informed by climate model projections. Nevertheless, using climate scenarios to identify the climate shifts that are relevant to risk-based decision making (i.e., Table 2) insured that we were not beholden to any one climate model; thus, the analysis would continue to be relevant with updated projections. As shown in this article, managers can examine how the identified climate scenarios compare with projections from high-resolution downscaling efforts and coarser multimodel consensus and ranges (also see Mote and others 2011 for discussion of selecting and combining climate model projections).
To understand how Lewis’s Woodpecker responds to climate variation, we used previously developed nest survival models that included weather predictors. We recognize that adequate response data are often lacking to explore these types of quantitative relationships; nevertheless, when they do exist, we encourage their use. If they do not exist, but climate is suspected to be an important factor, then monitoring projects may be needed to develop relevant data sets. When this is not possible, managers may need to rely more on complementary relative vulnerability approaches (e.g., Bagne and others 2011; also see Rowland and others 2011).
We also note that in terms of the impact (i.e., nest survival) models, two types of uncertainty, parameter and structural, were not explicitly considered. Characterizing parameter uncertainty would be relatively straightforward through techniques such as Monte Carlo simulation. For example, the parameter distribution could be constructed using the upper and lower confidence limits included in Table 1. The structural (or model) uncertainty of the impact models is a more difficult issue. We only examine one “best-fit” model for each habitat, but it is possible that other predictors might be relevant under conditions not observed during the field campaign. Furthermore, the impact models only address one component of population dynamics: the survival rate. These results could be embedded in a more comprehensive population dynamics model.
Related to these points, we reiterate that the impact models used do not explicitly consider potential nonstationarities. For example, these results assumed that habitat quality would remain constant over time. However, projections by Rehfeldt and others (2009) suggest that the area occupied by aspen could decrease rapidly during the course of the 21st century. Attempts to model this would add additional complexity, but promising approaches include landscape-based models (e.g., Turner and others 2008) and statistically derived methods, such as hierarchical Bayesian modeling (Gelman 2004).
This study provides a concrete example of how to use a risk-based approach to inform species and habitat management. Furthermore, this is the only study to date that explores the impact of climate change on the Lewis’s Woodpecker. Consistent with the individual species–based approach that typically guides conservation efforts (Glick and others 2011), this framework was showed for the Lewis’s Woodpecker. However, the approach is general and could be readily extended to other species where adequate response variable data exist. Of course, conservation efforts should draw from multiple sources and approaches (Dawson and others 2011), and we offer this as a contribution toward that goal.
This research was supported by the Postdocs Applying Climate Expertise fellowship program funded by the NOAA Climate Program Office and the U.S. Geological Survey and administered by the University Corporation for Atmospheric Research Visiting Scientist Programs. The authors acknowledge the United States Forest Service Rocky Mountain Research Station and the National Center for Atmospheric Research (NCAR). NCAR is sponsored by the National Science Foundation. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the United States government.
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