Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Evaluating and using existing models to map probable suitable habitat for rare plants to inform management of multiple-use public lands in the California desert

  • Gordon C. Reese ,

    Contributed equally to this work with: Gordon C. Reese, Sarah K. Carter

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Validation, Writing – original draft, Writing – review & editing

    Affiliation U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colorado, United States of America

  • Sarah K. Carter ,

    Contributed equally to this work with: Gordon C. Reese, Sarah K. Carter

    Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

    skcarter@usgs.gov

    Affiliation U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colorado, United States of America

  • Christina Lund ,

    Roles Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Writing – review & editing

    ‡ These authors also contributed equally to this work

    Affiliation California State Office, Bureau of Land Management, Sacramento, California, United States of America

  • Steven Walterscheid

    Roles Data curation, Methodology, Resources, Writing – review & editing

    ‡ These authors also contributed equally to this work

    Affiliation California State Office, Bureau of Land Management, Sacramento, California, United States of America

Evaluating and using existing models to map probable suitable habitat for rare plants to inform management of multiple-use public lands in the California desert

  • Gordon C. Reese, 
  • Sarah K. Carter, 
  • Christina Lund, 
  • Steven Walterscheid
PLOS
x

Abstract

Multiple-use public lands require balancing diverse resource uses and values across landscapes. In the California desert, there is strong interest in renewable energy development and important conservation concerns. The Bureau of Land Management recently completed a land-use plan for the area that provides protection for modeled suitable habitat for multiple rare plants. Three sets of habitat models were commissioned for plants of conservation concern as part of the planning effort. The Bureau of Land Management then needed to determine which model or combination of models to use to implement plan requirements. Our goals were to: 1) develop a process for evaluating the existing habitat models and 2) use the evaluation results to map probable and potential suitable habitat. We developed a method for evaluating the construction (input data and methods) and performance of existing models and applied it to 88 habitat models for 43 rare plant species. We also developed a process for mapping probable and potential suitable habitat based on the existing models; potential habitat maps are intended only to guide future field surveys. We were able to map probable suitable habitat for 26 of the 43 species and potential suitable habitat for 41 species. Forty percent of the project area contains probable suitable habitat for at least one species (43,338 km2), with much of that habitat (43%) occurring on lands managed by the Bureau of Land Management. Lands prioritized for renewable energy development contain 3% of the habitat modeled as suitable for at least one species. Our products can be used by agencies to review proposed projects and plan future plant surveys and by developers to target sites likely to minimize conflicts with rare plant conservation goals. Our methods can be broadly applied to understand and quantify the defensibility of models used in conservation and regulatory contexts.

Introduction

Management of multiple-use lands, which are intended to meet the needs of current and future generations, requires balancing numerous resource uses and values within and across landscapes. Many public lands in the United States accommodate multiple uses, with some of the most prominent being those managed by the Bureau of Land Management (BLM, Federal Land Policy and Management Act of 1976 [43 USC §1701]) and U.S. Forest Service (Multiple-Use Sustained-Yield Act of 1960 [16 USC §528]). The BLM manages the largest area of public lands in the United States (1,004,358 km2, [1]) for diverse resource uses and values including energy production, mineral extraction, recreation, and wildlife conservation, while also protecting land health (Fundamentals of Rangeland Health [43 CFR §4180.1]) and the quality of scientific, scenic, historical, ecological, environmental, air and atmospheric, water resource, and archeological values of the lands (Federal Land Policy and Management Act of 1976 [43 USC §1701]).

In the California desert, there are important and diverse conservation concerns involving many taxa (e.g., desert tortoise (Gopherus agassizii), Mohave ground squirrel (Spermophilus mohavensis), flat tailed-horned lizard (Phrynosoma mcallii), rare plants [24]) as well as strong interests in further developing renewable energy (solar, wind, geothermal) and recreational opportunities. The BLM manages many public lands in the California desert, and they recently completed a multiyear, multimillion dollar, land-use planning effort to identify how best to accommodate these uses and values while not unduly degrading public lands. The resulting Desert Renewable Energy Conservation Plan (DRECP) was completed in 2016 [2].

Conservation and management actions in the DRECP require numerous protections for rare plants within the project area. For example, the plan requires rare plant surveys, avoidance setbacks of 0.25 miles from species occurrences, a disturbance cap of 1% of suitable rare plant habitat across the entire plan area, and disturbance caps of 0–20% of suitable habitat within specific areas prioritized for renewable energy development [2]. Protecting habitat that models indicate is suitable, regardless of current occupation status, has rarely been implemented for plants and represents a proactive approach to conservation in areas where species are rare and many areas of the landscape have not yet been surveyed.

The surface disturbance caps in the plan were based on an initial set of habitat suitability models (we use habitat suitability models and habitat models as identical umbrella terms that include all methods for modeling habitat suitability and species distributions) commissioned in 2012 by the California Energy Commission. The original set of habitat suitability models raised numerous concerns, including the selection and accuracy of input data, scale and resolution, thresholding methods, and inclusion of heavily disturbed and developed areas [5,6]. Major tenets of the DRECP are adaptive management and the use of the best available science [2]. To this end, two additional organizations were commissioned to model the habitat suitability of rare plants. The three modeling efforts differed in input data, modeling algorithms, and decision criteria. These habitat models are hypotheses that propose the location of suitable habitat based on occurrence data and environmental variables, and they thereby require testing and validation [7].

When models and the resulting maps are used for regulatory actions, there is a clear need for approaches that are science-based, transparent, and defensible [8,9]. The BLM has committed to using the best available science to inform its management decisions [10]. Sharing data publicly promotes transparency and accountability and facilitates a shared understanding of resources and habitats between managers and stakeholders, all of which can increase stakeholder involvement in management and lead to better long-term management outcomes [11]. Transparency, defensibility, and data accessibility are particularly important in high-profile decisions that affect large land areas and many stakeholders, which is often the case with public land management decisions made by the BLM [2,12,13].

Given the availability of multiple models, the BLM needed to determine which model or combination of models should be used to implement the requirements in the land-use plan. A straightforward approach would be to use the model producing the best map, e.g., the map with modeled suitable habitat overlapping the largest number of occurrences. An evaluation of results is the most common way that models are evaluated and is ideally done with occurrences independent of those used to build the model. In addition to evaluating model results, there is also interest and value in evaluating the modeling procedure. Evaluation of results alone does not prevent the incorporation of a poorly constructed and less defensible model benefitting from unusual predictive success. Such a model may reflect known occurrence data, but the lack of a strong foundation undermines its defensibility, perhaps nowhere more than where no occurrence data exist and where distributional information is therefore greatly needed.

Numerous modeling components have been shown to affect model performance including the quantity and quality of both occurrences and environmental covariates, modeling extent and grain size (resolution), modeling algorithm, and methods and metrics used to assess performance. For example, a model might be based on species occurrence locations that inadequately cover the environmental niche or on a general suite of environmental covariates that are not all relevant to the distribution of the species; the specific covariates used to model suitable habitat are crucial to model performance [14]. Implementing techniques for selecting the best combination of covariates can minimize overfitting and improve performance [15,16]. Many studies have found that these and other steps are important to the performance of habitat suitability models, but we found few that included instructive guidelines for evaluating and comparing existing models (but see [17]). Due to data limitations, model evaluation is often based on a subset of the occurrence data used to build the model which may, largely because of autocorrelation, exaggerate model performance [18]. Independent data should therefore be used for model evaluation when available [19]. Also important, when applicable, is the threshold used to convert continuous habitat suitability results to categorical (usually binary) results [20].

Our goals were to: 1) develop a process for evaluating existing habitat suitability models including both model construction (input data and methods) and the accuracy of the results and 2) use the evaluation results to map probable suitable habitat for use in land-use planning and implementation decisions. We applied our process to a case study focused on 88 models of 43 rare plant species in the California desert for which land-use planning decisions and actions are needed to inform ongoing renewable energy development. In doing so, we developed an approach for evaluating existing habitat suitability models which is broadly applicable and can be used to understand and quantify the defensibility of other models that may be used in conservation and regulatory contexts. We hope this work can contribute to a strong foundation for future efforts focused on accommodating development actions that benefit society while also protecting habitat for rare and declining species.

Methods

Study area and species in the California desert

The project area, bounded by 32° 37.109’ and 37° 39.972’ north and 114° 7.848’ and 118° 41.485’ west, encompasses 108,126 km2 in the California desert, 44,280 km2 of which are public lands managed by the BLM [21], and 1,734 km2 of which have been prioritized for renewable energy development [22]. The area includes portions of the Mojave, Colorado/Sonoran and the Great Basin deserts and is dominated by short, isolated mountain ranges within desert plains. Within this area there is a wide elevational range from 85 m below to 2,650 m above mean sea level and a variety of landforms including mountains, plateaus, alluvial fans, playas, basins, and dunes. Given the varied topography and geology of the area, there are many different biological communities within the project area [23].

The 43 plant species we examined were of primary interest in the DRECP planning effort because they were listed under the Endangered Species Act (Endangered Species Act of 1973 as amended, 16 USC §1531–1544), considered rare or sensitive by the state or the BLM, or likely to be impacted by future development within the planning area (Table 1). We present results for Harwood’s eriastrum (Eriastrum harwoodii) here and provide complete results for all species in S1 Supporting Information.

thumbnail
Table 1. Species for which habitat suitability models were evaluated, conservation status of each species (federally endangered [FE], federally threatened [FT], state endangered [SE], and species designated as sensitive by the Bureau of Land Management [S]), number of existing habitat models, number of those models that exceeded all exclusion criteria, number of occurrences used to evaluate models and the map of probable suitable habitat, and validation metric (capture rate, i.e., the percentage of evaluation occurrences within the modeled suitable habitat).

Instances in which models exceeded the exclusion criteria and evaluation data were available, but no capture rate is provided, indicate that the suitability thresholds could not be altered to capture the desired percentage of occurrences. Species for which a disturbance cap has been identified in the Desert Renewable Energy Conservation Plan are indicated by an asterisk (*).

https://doi.org/10.1371/journal.pone.0214099.t001

Existing habitat suitability models for rare plants in the California desert

Leaders of the DRECP planning effort originally commissioned development of habitat suitability models for a suite of rare and sensitive plants to inform plan development. To address concerns with the original set of models [5,6] and broaden and update the information base used to implement plan requirements, the BLM commissioned development of additional habitat suitability models by two other contractors for rare plants occurring within the DRECP boundary.

The three sets of models included models for different species and used different inputs, algorithms, extents, resolutions, and modeling decisions [2428]. We were working with incomplete information for each contractor; in no case did we have the complete suite of information needed to reproduce the models including a comprehensive report, electronic data (occurrences and covariates), model parameters, and full model output. One contractor (working with two subcontractors) produced as many as three models for some species; when multiple models were available, we only used the model recommended for use in the DRECP planning effort. We purposefully do not refer to the individual organizations that developed specific models, as this information is not needed to understand the methods or interpret the results of our study.

Method for evaluating existing models and mapping suitable habitat

We developed a two-pronged approach for evaluating the existing habitat models that consisted of: 1) an evaluation of model construction (input data and methods), and 2) a post-hoc quantitative evaluation of model performance (Fig 1). In each prong, we established specific requirements for determining if and how model results would be used to map suitable habitat. We evaluated as acceptable those models meeting minimum criteria for occurrence input data (number, age, and spatial accuracy) and used them to map probable suitable habitat (for implementing land-use plan actions). To map potential suitable habitat (for guiding future plant surveys only), we used models not used to map the outer boundary of probable suitable habitat, as described below (Fig 1).

thumbnail
Fig 1. Process for evaluating existing habitat models and mapping probable and potential suitable habitat for rare plants in the California desert.

https://doi.org/10.1371/journal.pone.0214099.g001

Evaluating model construction (input data and methods).

We developed a list of categories and topics for evaluating model construction (input data and methods) for existing models (Table 2) by expanding and refining the work of Sofaer et al. [17] for application to our case study. We also developed specific criteria, based on the qualitative work of Sofaer et al. [17], to assign ranks of ideal, acceptable, or interpret with caution to each topic (Table 2). Importantly, our criteria had to address the specific context and purpose of this study, be practical to apply, and generate clear evaluation results based on the available information (primarily the reports and data submitted by each contractor).

thumbnail
Table 2. Categories, topics, and criteria for evaluating the model inputs and modeling methods used in existing habitat models for rare plants in the California desert.

Models were assigned qualitative rankings of interpret with caution (red), acceptable (yellow), or ideal (green) based on the criteria described below. The first three occurrence data topics (number, age, and spatial accuracy of occurrences used to develop the model) also include exclusion criteria (in bold); models exceeding all three exclusion criteria were evaluated as acceptable for use in mapping probable suitable habitat. Models failing any of these exclusion criteria were considered for use in mapping potential suitable habitat for use only in targeting future field surveys (see Methods).

https://doi.org/10.1371/journal.pone.0214099.t002

Occurrence data from 1981–2012 in the California Natural Diversity Database (CNDDB) [49] were likely used by all of the contractors, and we therefore used these data to evaluate the occurrence data topics (hereafter referred to as evaluation occurrences). Models failing to exceed exclusion criteria for the number, age, or spatial accuracy of occurrence data (see text in bold in Table 2) were not considered acceptable for mapping probable suitable habitat; they were instead considered for use in mapping potential suitable habitat for the purpose of guiding future plant surveys.

The evaluation results for each model include a brief description of the basis for each topic rank. Topics for which the contractor provided no details were assigned a rank of interpret with caution because of the lack of defensibility of that component of the model.

Evaluating the performance of the existing habitat models.

The second prong in our evaluation (see Fig 1) utilized the capture rate, i.e., the percentage of occurrences within the modeled suitable habitat, of the evaluation occurrences to evaluate the performance of model results. These evaluation occurrences were likely used by all of the contractors according to both the reports from each organization and to the lead CNDDB botanist (K. Lazar, pers. comm. May 2018). We used only those occurrence polygons with high spatial accuracy (i.e., accuracy classes 1, 2, 3 [limited to those polygons smaller than or equal in size to a circle with a radius of 150 m], and 4) [33]. We represented the location of each polygon by its internal centroid and randomly sampled the largest possible number of occurrence centroids that were separated by a minimum of 430 m from any other occurrence centroid. This last step ensured that no two evaluation points could have fallen within the same pixel during model construction for nearly all of the models (two of the 88 existing habitat models had pixel resolutions of approximately 355 m).

Mapping probable suitable habitat based on the existing models

We used only those models that met two conditions to delineate the outer boundary of probable suitable habitat. First, the model had to exceed the three exclusion criteria that we identified for the number, age, and spatial accuracy of occurrences used to develop the model (Table 2). We refer to these models as ‘acceptable’ models. Second, the model results had to capture, or be able to be modified to capture, a specified percentage of the evaluation occurrences. We reviewed the literature, explored multiple possible occurrence capture rates (70%, 80%, 90%, 100%), and worked with botany, wildlife, geographic information system (GIS), policy, and planning staff at the BLM California State Office to select 80% as the required minimum capture rate for mapping the outer boundary of probable suitable habitat for this project. Thus, models evaluated as acceptable for which the suitability threshold selected by the contractor met this capture rate, or models for which the threshold could be altered to meet this capture rate, were used to map the outer boundary of probable suitable habitat (see next section). In some cases, model thresholds were increased to reduce the capture rate to approximately 80%. One organization (Contractor B) used modeling methods that often made it difficult to closely match specific capture rates, and we therefore reduced the acceptable capture rate requirement to 78% for their models.

We used the union of models meeting both criteria, i.e., those exceeding all three exclusion criteria and capturing 80% of the evaluation occurrences, to delineate the outer boundary of probable suitable habitat for a species. If this boundary did not capture at least 90% of the evaluation occurrences, we decreased suitability thresholds of the base models until the final (union) map met the 90% capture rate requirement. The methodology of one organization (Contractor B) made it difficult to closely match the 90% capture rate and we therefore reduced the acceptable capture rate requirement to 89% when their model was included. When only one model was deemed acceptable for mapping probable suitable habitat, the threshold of that model was adjusted to capture as close to 90% of the evaluation occurrences as possible.

Within the outer boundary of probable suitable habitat, we mapped all models evaluated as acceptable, i.e., those models that exceeded the exclusion criteria regardless of whether or not they met the 80% minimum capture rate. For each pixel of probable suitable habitat, we quantified both the number of models that predicted suitable habitat and the average habitat suitability score [50].

We finalized each map by masking the Salton Sea [51]. Additionally, we calculated the area of probable suitable habitat classified as developed (i.e., ‘LF20: Developed’) by the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) existing vegetation type (EVT) dataset [52]. We considered masking developed areas in both the maps and electronic datasets; however, in a visual examination of National Agriculture Imagery Program (NAIP) imagery ([53], 60 cm resolution), we found multiple occurrence locations for multiple species that were classified as developed by LANDFIRE EVT. Thus, we chose not to mask developed areas and instead present the full model results.

In addition to the evaluation occurrences (1981–2012 CNDDB), we also computed capture rates of more recent CNDDB occurrences (2013–2018), representing an independent evaluation. We processed these recent occurrences in the same way as the 1981–2012 occurrences. While there were too few of these more recent occurrences for use in the baseline assessment for all species (see Table 1), the capture rates of these independent occurrences, when available, provide valuable additional information to map users.

Mapping potential suitable habitat for use in guiding future plant surveys

We mapped potential suitable habitat (for use in targeting future plant surveys) with models that were not used to map the outer boundary of probable suitable habitat. In general, we mapped potential suitable habitat using: 1) models that failed one or more exclusion criteria for occurrence data, and 2) models that could not be modified to meet our 80% minimum occurrence capture rate criteria. One contractor masked their model results such that their recommended map included only those watersheds with one or more known occurrences. We considered the masked results for mapping probable suitable habitat; we used the unmasked results for mapping potential suitable habitat regardless of whether the masked version was used in our map of probable suitable habitat. Thresholds for the individual models used to map potential suitable habitat were altered, when possible, to capture as close to 80% of the evaluation occurrences as possible.

Individual and multispecies products

Results were packaged in multiple ways to facilitate use by the BLM and others with an interest in resource management in the California desert. For each species, we provided: 1) an evaluation of the model construction (input data and methods), 2) a map of the existing habitat suitability models, 3) a map and performance evaluation of probable suitable habitat, and 4) a map of potential suitable habitat for guiding future plant surveys. We also developed a multi-species product representing the number of species for which each pixel was modeled as probable suitable habitat, to assist in initial screening of proposed development projects. Probable and potential suitable habitat maps are provided as: 1) hard copy maps with relevant boundaries and land-use designations (S1 Supporting Information), and 2) electronic, open-access raster datasets [50].

Results

Evaluating model construction (input data and methods) of the existing models

The three existing habitat models for Harwood’s eriastrum all exceeded the exclusion criteria for number, age, and spatial accuracy of occurrence data. Thus, we considered all for mapping probable suitable habitat (Table 3). There were topics evaluated as ‘interpret with caution’ for each model, particularly models from contractors B and C in the categories of occurrence data (contractor C), environmental covariates (both), and modeling algorithm (both). This same general pattern held true for the remaining 42 species (see S1 Supporting Information Tables B1-B43), as many of the interpret with caution rankings were related to unclear or unspecified methods that applied to every species modeled by that organization.

thumbnail
Table 3. Evaluation of model construction (input data and methods) for the three existing habitat models for Harwood’s eriastrum (Eriastrum harwoodii).

Please see Table 2 for a description of the criteria used to rate each topic as interpret with caution (red), acceptable (yellow), or ideal (green). Evidence in the organization’s submitted report, data, and metadata was used as the basis for determining topic ratings. For most occurrence data topics (indicated by an asterisk [*]), minimum ratings were based on occurrence data through 2012 currently available in the California Natural Diversity Database [49], as all contractors used CNDDB data in model development (see Methods).

https://doi.org/10.1371/journal.pone.0214099.t003

Evaluating performance of the existing models

The Harwood’s eriastrum models, as delivered by the individual organizations, captured between 66 and 81% of the 67 evaluation occurrences. The area of modeled suitable habitat also varied, from 397,802 to 3,557,133 acres (Fig 2). Two of the three models (contractors A, B) captured, or were modified to capture, 80% of the evaluation occurrences. Contractor C provided only model results above their selected suitability threshold, and thus we could not decrease the threshold to meet the 80% capture rate criteria for Harwood’s eriastrum. We therefore used the results of only two of the three models to map the outer boundary of probable suitable habitat. Two models were similarly used to map the outer boundary of probable suitable habitat for many other species (S1 Supporting Information). The number of acres of modeled suitable habitat also varied widely across contractors for some of the other rare plant species (S1 Supporting Information).

thumbnail
Fig 2. Existing habitat models for Harwood’s eriastrum developed by a) Contractor A, b) Contractor B, and c) Contractor C.

The legend includes the threshold value used by each contractor to define suitable habitat as well as the resulting number of acres of suitable habitat within the project area.

https://doi.org/10.1371/journal.pone.0214099.g002

Mapping probable suitable habitat for Harwood’s eriastrum

The (spatial) union of the two Harwood’s eriastrum models meeting the 80% desired capture rate formed the outer boundary of probable suitable habitat, encompassing 1,937,975 acres within the project area (Fig 3). The third model, which exceeded the three exclusion criteria but could not be altered to capture 80% of the evaluation occurrences, was only mapped within the already established outer boundary. As a result, 64% of the probable suitable habitat was mapped by multiple models (i.e., 2 or 3 models), and average habitat suitability scores ranged from 10 to 89 (scores were standardized from 1–100, [50]). Less than 1% (14,836 acres) of the mapped probable suitable habitat is currently classified as developed by LANDFIRE EVT. The final map of probable suitable habitat captured 96% of the evaluation occurrences (n = 67) and 100% of the 2013–2018 (i.e., independent) occurrences (n = 14).

thumbnail
Fig 3. Probable suitable habitat for Harwood’s eriastrum.

Shades of blue indicate the number of models predicting suitable habitat for the species. Results of the post-hoc performance evaluation using two sets of occurrences (evaluation occurrences from 1981–2012 and independent occurrences from 2013–2018, both from CNDDB) are shown on the map and in the legend, along with the area of probable suitable habitat within three boundaries: the project area, public lands managed by the Bureau of Land Management (BLM), and areas prioritized for development (Development Focus Areas) as identified in the Desert Renewable Energy Conservation Plan [2]. Note that some areas mapped as probable suitable habitat are currently classified as developed [52].

https://doi.org/10.1371/journal.pone.0214099.g003

Overall, we were able to map probable suitable habitat for 26 of the 43 species of interest (Table 1), with areas of probable suitable habitat ranging from 22,453 to 2,632,407 acres (S1 Supporting Information Table A1). On average, 45% of species’ probable suitable habitat occurred on public lands managed by the BLM (range 15–85%) and 2% of that habitat was within areas prioritized for renewable energy development in the DRECP (range 0–6%, S1 Supporting Information Table A1). On average, 2% of probable suitable habitat for each species was mapped by LANDFIRE EVT as developed (range 0–20%, S1 Supporting Information Table A1).

Mapping potential suitable habitat for use in guiding future plant surveys

As all three existing habitat models for Harwood’s eriastrum were used to map probable suitable habitat, the only additional areas mapped as potential suitable habitat (i.e., outside of the probable suitable habitat boundary) were: 1) the additional area mapped as suitable by Contractor C, since the threshold for their model could not be decreased to capture 80% of the evaluation points, and 2) the area mapped as suitable by Contractor B that was outside of watersheds with known occurrence locations, i.e., the unmasked version of their model (Fig 4). Within the area mapped as potential suitable habitat, 32% of the area was mapped by more than one model and the average of the standardized habitat suitability scores ranged from 35 to 89 [50].

thumbnail
Fig 4. Potential suitable habitat for Harwood’s eriastrum for guiding future plant surveys.

Shades of orange indicate the number of overlapping models predicting potential suitable habitat outside of the probable suitable habitat boundary (shown in blue). Note that some areas mapped as potential suitable habitat are currently classified as developed [52].

https://doi.org/10.1371/journal.pone.0214099.g004

Multispecies product

We identified 10,708,975 acres within the project boundary (40% of the total project area) that are mapped as probable suitable habitat for at least one rare plant species (Fig 5). Nearly 45% (4,554,576 acres) of that habitat occurs on public lands managed by the BLM, including 193,659 acres that occur within areas prioritized for renewable energy development identified in the DRECP [2].

thumbnail
Fig 5. Multispecies map of probable suitable habitat.

Shades of blue indicate the number of species for which probable suitable habitat is predicted. Note that some areas mapped as probable suitable habitat are currently classified as developed by LANDFIRE [52].

https://doi.org/10.1371/journal.pone.0214099.g005

Discussion

Effective management of multiple-use lands requires openly acknowledging tradeoffs between potentially conflicting resource uses such as development, recreation, and conservation [60]. Pressure is increasing to allow more intensive uses of public lands [61], which also provide critical habitat for sustaining many rare and declining species [62]. Renewable energy development in the California desert can move California closer to its sustainability goals for energy acquisition (California Senate Bill 350: Clean Energy and Pollution Reduction Act [2015]), but can involve tradeoffs when areas prioritized for development coincide with areas supporting rare plants and animals [63,64]. Clear, defensible, science-based information can help managers implement measures in land-use plans that help to balance such tradeoffs.

The areas prioritized for renewable energy development within the DRECP were designed to both maximize renewable energy potential and minimize environmental conflicts [65]. Our results indicate that while the vast majority of rare plant habitat occurs outside of these development focus areas, approximately 3% of the habitat suitable for Harwood’s eriastrum and 2% (range 0–6%) of the habitat suitable for other rare plant species occurs within the development focus areas. Project proponents and resource managers working in the DRECP can use our maps and other spatial data to help guide the selection of project locations to avoid or minimize loss of rare plant habitat in these areas. Project proponents are also required to conduct plant surveys during project evaluation, and results of these surveys can inform both the design of development projects and future habitat modeling and validation efforts.

The method we developed builds on existing science that highlights key topics and concerns in developing species distribution models [7,17,44,55]. We addressed common problems that can occur when contractors deliver to management agencies model outputs without the full suite of associated occurrence and covariate data [7]. A goal of the original modeling efforts was identification of both the location and degree of potential conflict between rare plant habitat and renewable energy development. Our project evaluated the construction and results (performance) of competing habitat models and provided results that can be used by land managers. Our evaluation process, particularly with respect to model construction (input data and methods), highlights numerous modeling steps that still require science-based recommendations and imposes clear criteria for use of information that may be used in a regulatory capacity. Additionally, our method is transferable and incorporates three actions to facilitate the evaluation and use of existing models for conservation and management decision-making: 1) evaluation based both on information provided by the model developer and on high-quality evaluation occurrences from a reliable source (here, one of the more than 80 natural heritage programs that NatureServe oversees), 2) close collaboration with the agency to identify who will use products and for what purposes (here, probable suitable habitat maps for informing land-use decisions and implementation of surface disturbance caps in the DRECP and potential suitable habitat maps for informing future plant surveys), and 3) provision of products in multiple formats through peer-reviewed outlets to increase transparency, defensibility, and data accessibility.

Using probable suitable habitat maps to inform development and implement the land-use plan

Our study provides clear and relevant products as well as guidelines for their interpretation and use by stakeholders with an interest in public land management in the California desert. The final maps of probable suitable habitat represent our goal of mapping the best available representation of probable suitable habitat based on the existing models and data for the species in the project boundary. Populations of these rare and sensitive plants are likely to have a better chance of persisting into the future if probable suitable habitat is protected from loss and degradation. Areas where multiple models overlap and where average habitat suitability scores are high provide additional indications that mapped areas are suitable for the species. The evaluation of model inputs and methods, which is less common than evaluations of model results [17], provides context for map use—users can have more confidence in maps for which most evaluation topics were rated as ideal or acceptable (Table 3, S1 Supporting Information Tables B1-B43). It is also important to test model results with evaluation data, ideally independent of those used to build the model (Fig 3, S1 Supporting Information); high capture rates provide users with greater confidence in map results, particularly when sample sizes are larger (e.g., ≥10 occurrence locations).

A primary envisioned use for the maps of probable suitable habitat is for agency screening of areas proposed for future renewable energy development. Our multispecies map could be used in the first of four screening steps to identify whether project areas may contain suitable habitat for rare plants. When used together, the multispecies data product and probable suitable habitat data for the individual species provide information about both how many species and which individual species may have suitable habitat at a given location. The presence of probable suitable habitat near a proposed development area is an indication that probable suitable habitat may be present on the site. The electronic data [50] are presented at a spatial resolution of 10 m pixels, which was used so that none of the contractors’ model results would be lost. Using the maps of probable suitable habitat at a resolution no smaller than 360 m (i.e., 36 pixels x 36 pixels) ensures that the maps are not used at a resolution finer than that of the coarsest input model.

A second step in the screening process is examination of the relevant probable suitable habitat species maps and recorded occurrence locations (occurrence data may be requested from CNDDB, https://www.wildlife.ca.gov/Data/CNDDB). Investigating average habitat suitability scores as well as the number of models predicting suitable habitat for a species will provide additional information, with larger scores and more models suggesting greater confidence in the suitability of a site for the species.

A third step in using our products for project screening relates to informing siting decisions (e.g., siting of a project within a Development Focus Area). Consulting information on habitat characteristics of species that may be present together with other relevant spatial data (e.g., the most recently available soils, surficial geology, land cover, and species occurrence data) will provide additional insight into the suitability of the area for the species in question. This step may provide crucial new information for areas that are currently data poor or not yet surveyed.

A final step in using our products to help inform land-use decisions, including design of the surface disturbance footprint of a proposed development, is to conduct on-the-ground plant and habitat surveys whenever our products indicate that probable suitable habitat may be present on or near the proposed disturbance area. Newly collected occurrence data from these surveys can be used to further test existing models.

Some areas of modeled probable suitable habitat might not be occupied at any given time because of fluctuations in population numbers and distribution (e.g., due to unfavorable weather conditions, competition with other native or invasive species, herbivory [66]). The distributions of rare species are particularly difficult to predict [67]; the limited knowledge of and data for most rare desert plants means that some areas predicted to be suitable may not in fact be and vice versa. For example, some locations mapped as suitable habitat may have been recently developed, or there may be small inclusions (too small to be mapped in land-cover products) of a land-cover type that is likely to support the species within larger areas of a different (less suitable) land-cover type. As we gather more plant survey data and more information about environmental factors (e.g., soils, weather and climate, physiological limits) driving the distribution of these species, we can refine habitat suitability models.

Using the maps of suitable habitat to target future plant surveys

Maps of potential suitable habitat are intended only as a tool for maximizing the benefits of on-the-ground plant surveys for collecting new data that can be used to develop and update habitat suitability models [68]. Prioritizing future survey effort in areas that are mapped as probable suitable habitat by multiple models, have relatively high habitat suitability values, and are distant from known occurrence locations may provide the greatest return on investment. Using the maps of potential suitable habitat to select future survey locations in a similar manner may further expand our information about and understanding of species’ environmental tolerances.

Improving models and data for public land management applications

Our case study application of these methods for evaluating existing models and using the results to map probable suitable habitat revealed five common areas in which available information limited our ability to confidently map probable suitable. First, for some of the species examined, existing models were based on a very small number of occurrences (<10 locations, see S1 Supporting Information). In other cases, there were no recent (>1980) occurrences in CNDDB with which to evaluate the performance of the existing model (Table 1). Targeting future survey efforts first toward these two categories of species may improve both circumstances and contribute to defensible and accurate mapping of probable suitable habitat. A second priority for additional surveys is those species for which there are fewer than 50 recent, spatially accurate occurrences, or for which the locations are highly clustered. Studies have indicated that 50 or more occurrences may be needed to stabilize performance with some modeling approaches [30], and clustered occurrence data covering a small proportion of a species’ range are less likely to represent the breadth of environmental conditions that are suitable for the species [18]. Additional considerations in prioritizing future plant surveys include the conservation status of the species at federal and state levels, the overlap of the species’ habitat with locations prioritized for energy or other types of development (e.g., recreation areas), the sensitivity of the species to projected climate change in the region, and the life history characteristics of the species.

Defensibility of model results is greatest when the organizations developing models for informing regulatory activities use methods and algorithms that are tested and well established in the scientific literature. This was not the case for two of the three sets of models examined here. Also, agencies find it difficult to use information that is only available for part of the project area. Expanding current project areas, in consultation with land managers, can accommodate future planning efforts that use slightly altered boundaries, as was the case here (J. Karuzas, pers. comm.). Delivering complete and continuous model outputs to agencies facilitates their use should different habitat suitability thresholds be relevant in the future. The multiple-use mandate of the BLM and other public land managers means that the tradeoff between using lower thresholds that identify larger areas of habitat, but may leave fewer development opportunities across the landscape, is a key consideration; such decisions may change over time and with different administrations. For example, 24 of the existing models were difficult to use for mapping probable suitable habitat because only those results above the contractor’s selected threshold were delivered to the agency.

Environmental covariates used in the models examined here have been criticized for being a ‘kitchen sink’ approach to modeling habitat without adequate consideration of spatial resolution or accuracy [5]—criticisms that are by no means unique to these models [69]. Soils data for much of the project area are spatially coarse, which is not ideal for modeling rare plant habitat, but new soil mapping efforts are underway (Jim Weigand, pers. comm.) and may improve future models. New modeling efforts may also benefit from use of the most recently available spatial data on surficial geology and seasonal temperature and precipitation, all of which were only included in some of the models examined here, despite relevancy to the distribution of many desert plants. Agencies may also want to consider using contract language that requires that new modeling efforts publish clear and comprehensive documentation and full results in a reputable, peer-reviewed outlet (e.g., scientific journal, government technical report). Finally, areas that provide suitable habitat today may no longer provide conditions that are optimal for the species in 10, 20, or 50 years. Long term habitat management and conservation efforts on public lands can benefit from considering how and over what time period climate and land-cover conditions are likely to change, revising models as needed based on more recent data, and planning for protection of habitat areas and corridors that will allow species to respond and move, over time, to new locations that may provide suitable habitat in the future.

Supporting information

S1 Supporting Information. Complete materials for all 43 rare plant species evaluated in the study.

Materials consist of a summary table, evaluations of the model construction for all species, maps of existing habitat models for all species, maps of probable suitable habitat for 26 species, and maps of potential suitable habitat for targeting future plant surveys for 41 species.

https://doi.org/10.1371/journal.pone.0214099.s001

(PDF)

Acknowledgments

We are grateful to Jeremiah Karuzas and Russell Scofield for their input and expertise in developing this project. We thank Cameron Aldridge, Catherine Jarnevich, and Todd Esque, for helpful discussions about the project. Phillip Krening and Judy Perkins provided additional botanic expertise; Judy Perkins and Cameron Barrows provided helpful reviews of an earlier draft of the manuscript. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

References

  1. 1. U.S. Department of the Interior. Public Land Statistics 2016. Denver (CO): Bureau of Land Management National Operations Center. 2017:201. Report No.: BLM/OC/ST-17/001+1165. https://www.blm.gov/sites/blm.gov/files/PublicLandStatistics2016.pdf.
  2. 2. Bureau of Land Management. Desert Renewable Energy Conservation Plan Land Use Plan Amendment to the California Desert Conservation Area Plan, Bishop Resource Management Plan and Bakersfield Resource Management Plan. 2016 [cited 2017 Oct 20]. http://www.drecp.org/finaldrecp/#lupa.
  3. 3. Kreitler J, Schloss CA, Soong O, Hannah L, Davis FW. Conservation planning for offsetting the impacts of development: a case study of biodiversity and renewable energy in the Mojave Desert. PLoS ONE. 2015;10(11):e0140226. pmid:26529595
  4. 4. Parker SS, Cohen BS, Moore J. Impact of solar and wind development on conservation values in the Mojave Desert. PLoS ONE. 2018;13(12):e0207678. https://doi.org/10.1371/journal.pone.0207678 pmid:30540781
  5. 5. Desert Renewable Energy Conservation Plan Independent Science Advisors. Recommendations of independent science advisors for the California Desert Renewable Energy Conservation Plan (DRECP). 2010 [cited 2018 Jun 29]. http://www.energy.ca.gov/2010publications/DRECP-1000-2010-008/DRECP-1000-2010-008-F.PDF.
  6. 6. DRECP Independent Science Panel. Independent science review for the California Desert Renewable Energy Conservation Plan (DRECP). 2012 [cited 2018 Jun 29]. https://www.drecp.org/documents/docs/independent_science_2012/Independent_Science_Panel_2012_Final_Report.pdf.
  7. 7. Jarnevich CS, Stohlgren TJ, Kumar S, Morisette JT, Holcombe TR. Caveats for correlative species distribution modeling. Ecological Informatics. 2015;29:6–15.
  8. 8. Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AIT, et al. Predicting species distributions for conservation decisions. Ecology Letters. 2013;16:1424–1435. pmid:24134332
  9. 9. Villero D, Pla M, Camps D, Ruiz-Olmo J, Brotons L. Integrating species distribution modelling into decision-making to inform conservation actions. Biodiversity Conservation. 2017;26:251–271.
  10. 10. Kitchell K, Cohn S, Falise R, Hadley H, Herder M, Libby K, et al. Advancing Science in the BLM: An Implementation Strategy. 2015 [cited 2018 Nov 7]. Washington (DC): Department of the Interior, Bureau of Land Management. https://www.blm.gov/sites/blm.gov/files/uploads/IB2015-040_att1.pdf.
  11. 11. Sayer J, Sunderland T, Ghazoul J, Pfund J-L, Sheil D, Meijaard E, et al. Ten principles for a landscape approach to reconciling agriculture, conservation, and other competing land uses: Proceedings of the National Academy of Sciences of the United States of America, 2013;110:8349–8356. pmid:23686581
  12. 12. Bureau of Land Management. National Petroleum Reserve-Alaska Final Integrated Activity Plan/Environmental Impact Statement. 2012 [cited 2018 Nov 7]. https://eplanning.blm.gov/epl-front-office/eplanning/planAndProjectSite.do?methodName=renderDefaultPlanOrProjectSite&projectId=67091&dctmId=0b0003e880c49eae.
  13. 13. Bureau of Land Management. Record of decision and approved resource management plan amendments for the Great Basin region, including the Greater sage-grouse sub-regions of Idaho and southwestern Montana, Nevada and Northeastern California, Oregon, Utah. 2015 [cited 2018 May 11]. https://eplanning.blm.gov/epl-front-office/projects/lup/21152/63235/68484/NVCA_Approved_RMP_Amendment.pdf.
  14. 14. Austin MP, Van Niel KP. Improving species distribution models for climate change studies: variable selection and scale. Journal of Biogeography. 2011;38:1–8.
  15. 15. Burnham K, Anderson D. Model selection and inference: a practical information-theoretic approach. New York: Springer-Verlag; 1998.
  16. 16. Engler R, Guisan A, Rechsteiner L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology. 2004;41:2263–274.
  17. 17. Sofaer HR, Jarnevich CS, Pearse IS, Lyons Smyth R, Auer S, Cook GL et al. Development and delivery of species distribution models to inform decision-making. BioScience. 19;
  18. 18. Fei S, Yu F. Quality of presence data determines species distribution model performance: a novel index to evaluate data quality. Landscape Ecology. 2016;31:31–42.
  19. 19. Acevedo P, Jimenez-Valverde A, Lobo JM, Real R. Delimiting the geographical background in species distribution modelling. Journal of Biogeography 2012;39:1383–1390.
  20. 20. Liu C, Berry PM, Dawson TP, Pearson RG. Selecting thresholds of occurrence in the prediction of species distributions. Ecography. 2005;28:385–393.
  21. 21. Bureau of Land Management. BLM California land status—surface management areas. 2018 [cited 2018 Jul 7]. https://navigator.blm.gov/data?keyword=1fca0357df7c87ae.
  22. 22. Bureau of Land Management. BLM California Development Focus Areas and Variance Process Lands (Desert Renewable Energy Conservation Plan Record of Decision). 2016 [cited 2018 Jun 29]. https://navigator.blm.gov/data?keyword=c92c420114ad8257.
  23. 23. Bureau of Land Management. Draft Desert Renewable Energy Conservation Plan (DRECP) and Environmental Impact Report/Environmental Impact Statement. 2014 [cited 2018 Nov 9]. Prepared by California Energy Commission, California Department of Fish and Wildlife, U.S. Bureau of Land Management, U.S. Fish and Wildlife Service. Report Nos.: SCH No. 2011071092, BLM/CA/PL-2014/025+1793, FWS–R8–ES–2014–N165. https://www.drecp.org/draftdrecp/.
  24. 24. BIO-WEST Inc. Rare plant modeling in California’s Mojave Desert draft report. Prepared for Christina Lund, State Botanist, Bureau of Land Management. 2014. Available from Bureau of Land Management California State Office, 2800 Cottage Way, Sacramento, CA, 95825.
  25. 25. Davis F, Soong O (University of California, Santa Barbara). Species distribution models, DRECP [Desert Renewable Energy Conservation Plan]. 2014 Oct. https://databasin.org/datasets/ by searching on each rare plant species name.
  26. 26. ECORP Consulting Inc. Rare plant modeling report, Prepared for Bureau of Land Management California State Office. 2015. Available from Bureau of Land Management California State Office, 2800 Cottage Way, Sacramento, CA, 95825.
  27. 27. Frank D, Kreitler J, Soong O, Stoms D, Dashiell S, Schloss C, Hannah L, Wilkinson W, Dingman J (University of California, Santa Barbara). Cumulative biological impacts framework for solar energy projects in the California desert. California Energy Commission; 2013 Dec. Report No.: CEC-500-2015-062. Contract No.: 500-10-021.
  28. 28. Moore KA, McIntyre PJ (University of California, Davis). Enhancing rare desert plant mapping for conservation amid renewable energy planning. California Energy Commission; 2014 Sep. Report No.: CEC-500-2016-009. Contract No.: 500-10-017.
  29. 29. Hernandez PA, Graham CH, Master LL, Albert DL. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography. 2006;29:773–785.
  30. 30. Kadmon R, Farber O, Danin A. A systematic analysis of factors affecting the performance of climatic envelope models. Ecological Applications. 2003;13:853–867.
  31. 31. Nix H. A biogeographic analysis of Australian elapid snakes. In: Longmore R, editor. Atlas of elapid snakes of Australia. Canberra: Bureau of Flora and Fauna, 1986. p. 4–15.
  32. 32. Roubicek AJ, VanDerWal J, Beaumont LJ, Pitman AJ, Wilson P, Hughes L. Does the choice of climate baseline matter in ecological niche modelling? Ecological Modelling. 2010;221:2280–2286.
  33. 33. California Natural Diversity Database. CNDDB Data Use Guidelines v4.2. Sacramento (CA): California Department of Fish and Wildlife, 2011 [cited 2018 Jun 22]. https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=27285&inline.
  34. 34. Tingley MW, Beissinger SR. Detecting range shifts from historical species occurrences: new perspectives on old data. Trends in Ecology and Evolution. 2009;24:625–633. pmid:19683829
  35. 35. Miller DAW, Nichols JD, Gude JA, Rich LN, Podruzny KM, Hines JE, et al. Determining occurrence dynamics when false positives occur: estimating the range dynamics of wolves from public survey data. PLoS ONE. 2013;8:e65808. pmid:23840372
  36. 36. Anderson RP, Gonzalez I Jr.. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecological Modelling. 2011;222:2796–2811.
  37. 37. Gu W, Swihart RK. Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biological Conservation. 2004;116:195–203.
  38. 38. Beale CM, Lennon JJ. Incorporating uncertainty in predictive species distribution modelling. Philosophical Transaction of the Royal Society B. 2012;367:247–258.
  39. 39. Gastón A, García-Viñas JI. Updating coarse-scale species distribution models using small fine-scale samples. Ecological Modelling. 2010;221:2576–2581.
  40. 40. Vaughn IP, Ormerod SJ. Improving the quality of distribution models for conservation by addressing shortcomings in the field collection of training data. Conservation Biology. 2003;17:1601–1611.
  41. 41. Li X, Si Y, Luyan J, Gong P. Dynamic response of East Asian greater white-fronted geese to changes of environment during migration: use of multi-temporal species distribution model. Ecological Modelling. 2017;360:70–79.
  42. 42. Yiwen Z, Wei LB, Yeo DCJ. Novel methods to select environmental variables in MaxEnt: a case study using invasive crayfish. Ecological Modelling. 2018;341:5–13.
  43. 43. Kramer-Schadt S, Niedballa J, Pilgrim JD, Schröder B, Lindenborn J, Reinfelder V, et al. The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions. 2013;19:1366–1379.
  44. 44. Dormann CF. Promising the future? Global change projections of species distributions. Basic and Applied Ecology. 2007;8:387–397.
  45. 45. Johnson CJ, Gillingham MP. An evaluation of mapped species distribution models used for conservation planning. Environmental Conservation. 2005;32:117–128.
  46. 46. Anderson RP, Raza A. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. Journal of Biogeography. 2010;37:1378–1393.
  47. 47. Warren DL, Seifert SN. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications. 2011;21:335–342. pmid:21563566
  48. 48. Jiménez-Valverde A, Lobo JM. Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecologica. 2007;31:361–369.
  49. 49. California Natural Diversity Database. Sacramento (CA): California Department of Fish and Wildlife, 2018 [version 2018 Jun 20]. https://www.wildlife.ca.gov/Data/CNDDB.
  50. 50. Reese GC, Carter SK. Probable and potential suitable habitat for 43 rare plant species in the California desert. In review. US Geological Survey data release. https://doi.org/xxx
  51. 51. U.S. Geological Survey. 2018. National Hydrography Dataset for the State of California. US Geological Survey. 2018 [cited 2018 Jun 29]. http://prd-tnm.s3-website-us-west-2.amazonaws.com/?prefix=StagedProducts/Hydrography/NHD/State/HighResolution/GDB/.
  52. 52. Landscape Fire and Resource Management Planning Tools Project (LANDFIRE). LANDFIRE Existing Vegetation Type (EVT) layer. U.S. Geological Survey (LANDFIRE version 1.4.0, 2014)[cited 2018 Jun 29]. http://landfire.cr.usgs.gov/viewer/.
  53. 53. National Geospatial Data Asset National Agriculture Imagery Program Imagery. California NAIP 2016 Imagery. Salt Lake City (UT): US Department of Agriculture–Farm Service Agency Aerial Photography Field Office. 2016 [cited 2018 Apr 16]. https://gis.apfo.usda.gov/arcgis/rest/services/NAIP/California_2016_60cm/ImageServer
  54. 54. The Jepsen Herbarium. Jepson eFlora: Taxon pages, vascular plants of California. 2018 [cited 2018 Jul 11]. http://ucjeps.berkeley.edu/eflora/.
  55. 55. Elith J, Burgman MA, Regan HM. Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecological Modelling. 2002;157:313–329.
  56. 56. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 2006;190:231–259.
  57. 57. Phillips SH, Dudik M. Modeling of species distribution with Maxent. Modeling of species distributions with MaxEnt: New extensions and a comprehensive evaluation. Ecography. 2008;31:161–175
  58. 58. Knick ST, Hanser SE, Preston KL. Modeling ecological minimum requirements for distribution of greater sage-grouse leks: implications for population connectivity across their western range, U.S.A., Ecology and Evolution. 2013;3:1539–1551. pmid:23789066
  59. 59. Liu C, White M, Newell G. Selecting thresholds for the prediction of species occurrence with presence‐only data. Journal of Biogeography. 2013;40:778–789.
  60. 60. Freeman OE, Duguma LA, Minang PA. Operationalizing the integrated landscape approach in practice. Ecology and Society 2015;20. http://www.ecologyandsociety.org/vol20/iss1/art24/.
  61. 61. U.S. Department of the Interior. Our priorities: American energy, climate change, jobs, regulatory reform, stewardship, and tribal nations. Washington (DC): US Department of the Interior. 2017 [cited 2017 Oct 19]. https://www.doi.gov/ourpriorities.
  62. 62. Manier DJ, Wood DJA, Bowen ZH, Donovan RM, Holloran MJ, Juliusson LM, et al. Summary of science, activities, programs, and policies that influence the rangewide conservation of Greater Sage-Grouse (Centrocercus urophasianus). Denver (CO): US Geological Survey, 2013. Report No.: 2013–1098. https://pubs.er.usgs.gov/publication/ofr20131098.
  63. 63. Lovich JE, Ennen JR. Wildlife conservation and solar energy development in the desert southwest, United States. BioScience. 2011;61:982–992.
  64. 64. Smith JA, Dwyer JF. Avian interactions with renewable energy infrastructure: An update. The Condor: Ornithological Applications 2016; 118:411–423
  65. 65. Bureau of Land Management. Desert Renewable Energy Conservation Plan Proposed Land Use Plan Amendment and Final Environmental Impact Statement. 2015 [cited 2019 Feb 14]. http://www.drecp.org/finaldrecp/
  66. 66. Alignier A, Ricci B, Biju-Duval L, Petit S. Identifying the relevant spatial and temporal scales in plant species occurrence models: the case of arable weeds in landscape mosaic of crops. Ecological complexity. 2013;15:17–25.
  67. 67. MacKenzie DI, Nichols JD, Sutton N, Kawanishi K, Bailey LL. Improving inferences in population studies of rare species that are detected imperfectly. Ecology. 2005;86:1101–1113.
  68. 68. Raxworthy CJ, Martinez-Meyer E, Horning N, Nussbaum RA, Schneider GE, Ortega-Huerta MA, et al. Predicting distributions of known and unknown reptile species in Madagascar. Nature. 2003;426:837:841. pmid:14685238
  69. 69. Fourcade Y, Besnard AG, Secondi J. Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Global Ecology and Biogeography. 2018;27:245–256.