DRAFT March 2012 APPENDIX C. Species Habitat Model Results

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1 APPENDIX C Species Habitat Model Results

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3 This appendix describes the species habitat modeling methods for the Desert Renewable Energy Conservation Plan (DRECP) and presents the results of the modeling efforts. The species habitat modeling method described below was used to develop the habitat models for each of the proposed Covered Species. C1.0 BACKGROUND Species modeling is a necessary component of the planning process for DRECP because of the following factors: Need for extrapolating habitat distribution across areas lacking adequate data due to lack of comprehensive survey results across the Plan Area; Need to obtain information that will supplement existing surveys as part of the planning process; Need to transcend the limitations of the snapshot in time effect that survey data represents when existing field data are available; Need for synthesis and analysis of multiple data sources across the entire Plan Area; Need to identify and rank biological values between areas; and Need to establish baseline conditions to compare alternate conservation strategies. Given these factors, the DRECP Independent Science Advisors (ISA) recommend careful use of habitat suitability models or species distribution models (DRECP ISA 2010). Species modeling can provide an objective, transparent, and repeatable means of assessing species habitat distribution where the species distribution or distribution of suitable habitat for a species is not well known. For these reasons, species habitat modeling results provide additional biological information to be used in the following components of the DRECP: gap analysis, effects analysis, conservation strategy, monitoring, and adaptive management. The approaches to assess the potential effects of climate change on species habitat and distribution for the DRECP are being developed and are not addressed in this document. Additionally, the approaches to address reference states for the purposes of monitoring and adaptive management for the DRECP are being developed and are not addressed in this document. Two types of models are typically used in conservation planning applications: expert-based models and statistically based models. Expert-based models (see Section C3.0) identify species-specific habitat distribution based on scientific literature and expert opinion related to the physical and biological habitat parameters associated with species C-1

4 occurrence. As the ISA stated, expert-based models are appropriate where species occurrence data are not sufficient to conduct more rigorous modeling, where species occurrence data are strongly biased spatially across a plan area, or during the initial, exploratory analyses of environmental factors associated with species occurrence. Statistically based models (see Section C4.0) specify suitable habitat and may even predict the likelihood of species occurrence based on correlations between presence/absence data and physical and biological habitat parameters. The ISA indicated that empirical, statistically based models are preferred over expert-based models (such models better control for subjective or biased input), but acknowledged that statistically based models would require sufficient occurrence data across the Plan Area. Expert-based models and statistically based models were developed for proposed Covered Species for the DRECP. The use of models in the DRECP conservation planning process is to identify areas of higher probability for species presence or, in other words, high probability of suitable conditions for a species (i.e., species habitat) within the Plan Area. The statistically based (i.e., Maxent) species distribution models will be used in conjunction with the expert-based models to assist in the identification of potential high-priority conservation areas for the DRECP conservation strategy. The models will also be used as one measure of quantification of expected conservation and covered project impacts for evaluation of conservation strategy alternatives. C2.0 SPECIES AND HABITAT MODEL SELECTION Each of the 77 proposed Covered Species was evaluated to determine whether a habitat model is needed or appropriate for determining the species habitat distribution in the Plan Area. A DRECP-specific habitat model was not considered necessary for a particular species if: A habitat model for the species currently exists that adequately displays the distribution of the species or suitable habitat for the species in the Plan Area (e.g., the statistically based species distribution model for desert tortoise [Nussear et al. 2009] as refined by the U.S. Fish and Wildlife Service). In these cases, the existing model is cited and used to display the habitat distribution for that species in the Plan Area. The species geographic range and distribution of occurrences are well established and recognized through existing field survey data such that modeling would not contribute additional information for the species in the Plan Area (e.g., a restricted range plant species such as Lane Mountain milk-vetch for which comprehensive C-2

5 rangewide surveys have been conducted). In these cases, the existing occurrence data were cited and used to map and characterize the distribution for that species in the Plan Area. C3.0 EXPERT-BASED MODELS The following section describes the methods used to develop the expert-based species models. It presents the physical and ecological factors data layers used, and describes the process used for modeling and subsequent model review and refinement. C3.1 GIS Data Layers for Species Model Development The following list of data layers includes the physical and ecological factors that were available for use to create the species habitat models (note that not all data layers were needed or used in every model). Land Cover (including vegetation type) o Natural community, NVCS macrogroup, and NVCS group vegetation types based on the DRECP land cover map Soil Parent Material and Soil Texture o Soil parent material is derived from statewide surficial geology data from the California Department of Conservation. Soil texture comes from the USDA National Resources Conservation Service (NRCS) Soil Survey Geographic Database (SSURGO). Landform o Landform is derived from the Land Facet tool using digital elevation model (DEM) data. This data layer classifies areas as ridgelines, plains, valleys, or slopes. Elevation Range, Percent Slope, and Aspect o Elevation range, percent slope, and aspect are derived from the DEM data. Hydrology, Water bodies, and Wetlands o Hydrology and water bodies are from the USGS National Hydrography Dataset (NHD). Wetlands are from the USFWS National Wetland Inventory (NWI). These datasets include seeps and springs. Watershed C-3

6 o Watersheds are from the California Department of Water Resources (DWR). Ecoregion Subsection o Ecoregion subsections are from the USDA. This layer is useful to distinguish limits to the range of Covered Species. C3.2 Modeling Process Development of the species habitat models was a multi-step process involving a collaboration of biologists and GIS analysts. To facilitate this collaboration and to document the process, a DRECP species modeling Microsoft SharePoint site was created. The SharePoint site allowed biologists to select and parameterize the data layers considered important for the habitat model of each species. The selection and parameterization of the model for each species was based on the best available scientific information related to the species natural history and habitat associations, which is summarized in each species profile in Appendix B. Biologists reviewed and considered this expert-based information in developing the models. Biologists also indicated the map algebra to specify how the parameterized layers were to be combined. For example, a species that is known to occur in specific vegetation communities with certain soils over a range of elevations would be modeled using the following map algebra: suitable habitat = vegetation type A and vegetation type B restricted by soil type 1 at elevations of 3,000 to 4,000 feet. The resulting map would show the modeled suitable habitat for the species in the Plan Area. The selected data layers, parameters, and map algebra were then reviewed internally before being approved for the initial GIS model run. The SharePoint site allowed the team to track model development status, document internal review, and report species-by-species model input data and model results. Habitat model results were displayed as a species-specific map of suitable habitat overlaid by species occurrence points from the DRECP species occurrence database. Following each model run for each species, the model testing, refinement, and review process was conducted. C3.3 Model Review and Refinement Each habitat model run for each species underwent model review and refinement. Model review involved assessments of how well the DRECP modeled suitable habitat captured the expected results based on expert knowledge, as well as the known species distribution (based on descriptions of the species distribution, range maps, and species occurrence data). Model review acknowledged limitations in the data used to characterize habitat variables (e.g., resolution of the elevation data or the accuracy of the land cover mapping), limitations in the current knowledge of species habitat affiliations, and the survey C-4

7 effort/quality of the occurrence data used to review the model against. Model refinements were made if the model results did not agree with expert knowledge for a species. This was an iterative process of model runs, model reviews, and model refinements until a model was considered reasonably accurate and complete and ready for outside review. Field validation of the habitat models through additional surveys was not a part of the testing and refinement of the models. Species model output went through two levels of outside review: Renewable Energy Action Team (REAT) agencies review and Science Expert review. Input from the REAT agencies and outside scientific experts were solicited on species modeling approaches, modeling parameters, and preliminary model results. Additional model runs were conducted, as necessary, based on comments from reviewers. C4.0 STATISTICALLY BASED MODELS The following section describes the methods used to develop the statistically based species models using Maxent. It presents the environmental variables and species occurrence data used, and describes the process used to create the statistical distribution modeling, including the Maxent program, statistical evaluation of the Maxent models, and methods for predicting habitat. It also includes next steps and the limitations of Maxent. C4.1 Data Used in Statistically Based Models The following describes the environmental variables and species occurrence data used for the statistically based species models. C4.1.1 Environmental Variables Environmental variables used to model species distribution with Maxent (see description in Section C4.2) included climate data and terrestrial data (e.g., vegetation, slope) at a 100- meter grid cell size, across the Plan Area. Terrestrial data (Table C-1) were converted from a vector format to a grid of 100-meter cell size. Climate data (Table C-1) were downloaded from WorldClim via and converted from 1-kilometer to 100-meter (1 hectare) grid cell size. Climate variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. As shown in Table C-1, the bioclimatic variables represent annual averages (e.g., mean annual temperature, annual precipitation), seasonality (e.g., annual range in temperature and precipitation), and limiting C-5

8 environmental variables (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). Table C-1 Environmental Variables Used in Maxent Environmental Variable Aspect Bulk Density Geology Depth to Bedrock Elevation Landform Land Cover Slope Soil Texture Annual Mean Temperature (BI01) Mean Diurnal Range (BI02) Isothermality (BIO3) Temperature Seasonality (BIO4) Max Temperature of Warmest Month (BIO5) Min Temperature of Coldest Month (BIO6) Temperature Annual Range (BIO7) Mean Temperature of Wettest Quarter (BIO8) Mean Temperature of Driest Quarter (BIO9) Mean Temperature of Warmest Quarter (BIO10) Description/Source Terrestrial Variables Divided into 4 categories, derived from 30-meter digital elevation model. Bulk density of vegetation cover, derived from State Soil Geographic (STATSGO) dataset. Surficial geology data from the California Department of Conservation. Soil depth to bedrock, derived from STATSGO dataset. Elevation derived from 30 meter grid cell size digital elevation model. Landform is derived from the Land Facet tool using digital elevation model (DEM) data. This data layer classifies areas as ridgelines, plains, valleys, or slopes. Generalized vegetation classification based on Sawyer, Keeler-Wolf and Evens (2009). Generated from 30-meter grid cell size digital elevation model. Soil texture comes from the USDA National Resources Conservation Service (NRCS) Soil Survey Geographic Database (SSURGO). Climate Variables The mean of all the monthly mean temperatures. Each monthly mean temperature is the mean of that month s maximum and minimum temperature. The mean of all the monthly diurnal temperature ranges. Each monthly diurnal range is the difference between that month s maximum and minimum temperature. The mean diurnal range (parameter 2) divided by the Annual Temperature Range (parameter 7) x 100. Coefficient of Variation as the standard deviation of the monthly mean temperatures expressed as a percentage of the mean of those temperatures (i.e., the annual mean). The highest temperature of any monthly maximum temperature. The lowest temperature of any monthly minimum temperature. The difference between the Max Temperature of Warmest Period and the Min Temperature of Coldest Period. The wettest quarter of the year is determined (3-month period), and the mean temperature of this period is calculated. The driest quarter of the year is determined (3-month period), and the mean temperature of this period is calculated. The warmest quarter of the year is determined (3-month period), and the mean temperature of this period is calculated. C-6

9 Environmental Variable Mean Temperature of Coldest Quarter (BIO11) Annual Precipitation (BIO12) Precipitation of Wettest Month (BIO13) Precipitation of Driest Month (BIO14) Precipitation Seasonality (BIO15) Precipitation of Wettest Quarter (BIO16) Precipitation of Driest Quarter (BIO17) Precipitation of Warmest Quarter (BIO18) Precipitation of Coldest Quarter (BIO19) Table C-1 Environmental Variables Used in Maxent Description/Source The coldest quarter of the year is determined (to the nearest week), and the mean temperature of this period is calculated The mean of the sum of all the monthly precipitation estimates over the averaging period. The precipitation of the wettest month. The precipitation of the driest month. The Coefficient of Variation is the standard deviation of the monthly precipitation estimates expressed as a percentage of the mean of those estimates (i.e., the annual mean). The wettest quarter of the year is determined (3-month period), and the total precipitation over this period is calculated. The driest quarter of the year is determined (3-month period), and the total precipitation over this period is calculated. The warmest quarter of the year is determined (3-month period), and the total precipitation over this period is calculated. The coldest quarter of the year is determined (3-month period), and the total precipitation over this period is calculated. Sources: WorldClim website ( Dudek GIS data files. C4.1.2 Species Occurrence Data Occurrence data were obtained from the California Natural Diversity Database (CNDDB), Bureau of Land Management, California Department of Fish and Game, and California Avian Data Center. Maxent species distribution models were only developed for DRECP Covered Species with greater than 30 occurrence points within the Plan Area as a conservative cutoff for model development (inaccurate model results are more likely with fewer than 25 occurrence points [Hernandez et al. 2006]). Maxent is only able to use one occurrence point per 1-hectare grid cell, so the number of occurrence points used by the model for each species may be fewer than the total number of occurrence points in the DRECP database. The list of DRECP Covered Species with 30 or more occurrence points (as determined by Maxent) is included in Table C-2. C-7

10 Table C-2 DRECP Covered Species with More than 30 Occurrence Points Usable by Maxent Species Number of Occurrence Points Usable by Maxent Peirson s milk-vetch 8,840 desert tortoise* 1,286 flat-tailed horned lizard 1,010 burrowing owl 897 Mohave ground squirrel 511 golden eagle 502 alkali mariposa lily 259 desert cymopterus 205 Lane Mountain milk-vetch 202 Barstow woolly sunflower 156 Mojave monkeyflower 113 Tracy s eriastrum 101 Mojave fringe-toed lizard 92 Charlotte s phacelia 89 coast horned lizard 85 Red Rock tarplant 79 Red Rock poppy 76 Yuma clapper rail 64 pallid bat 60 Gila woodpecker 52 Townsend s big-eared bat 51 California black rail 47 California leaf-nosed bat 44 western yellow-billed cuckoo 36 Parish s daisy 34 Nelson s bighorn sheep 32 peninsular bighorn sheep 32 Swainson s hawk 32 Owens Valley checkerbloom 30 arroyo toad 30 Note: *Already has a Maxent model in use by the DRECP. The occurrence data used to model species distribution was derived from several different sources. The use of data from multiple sources collected at different times, spatial scales, and for different purposes can result in an unsystematic and spatially biased occurrence data set. Sampling effort is, as expected, far greater in the western Mojave Desert and the C-8

11 Imperial Valley where there is a greater human presence and more road access that enables more intensive sampling. Maxent allows for the use of a bias file to effectively weight the value of occurrence data by sampling intensity. Sampling effort varies by taxonomic group and taxa observed by similar methods will share similar bias (e.g., the sampling effort for plants is generally independent of the sampling effort for birds because botanists are not recording bird observances). Therefore, a proxy for sampling effort for each taxonomic group was developed by creating a bias layer based on occurrence data pooled by taxonomic group, similar to methods employed by Phillips et al. (2009). While the inclusion of a bias layer for each taxonomic group may improve the Maxent results for many species, there are limitations on how much it is possible to correct for sampling bias in presence-only models. C4.2 Modeling Process/Program Statistical species distribution modeling was performed with Maxent (Phillips et al. 2006). Maxent is a predictive modeling program which uses species occurrences and environmental variables to predict a species distribution across a landscape. The model predicts the relative suitability of an area, or landscape unit, for a species (i.e., habitat) based upon species occurrence data and GIS data layers representing spatial variation of a number of environmental variables. It is relatively assumption free, in that the GIS data layers can consist of any measurable, geographically explicit variable for which data are available (e.g., land cover, climate data, topography, soil type). Conceptually, Maxent works on the premise that the distribution of a given species is determined by a limited range of environmental conditions that are related to the species ecology. It uses this limited range of conditions to apply a relative probability of occurrence or, in other words, a measure of suitability to each landscape unit (grid cell) across the area of analysis. For a more detailed explanation of Maxent from a statistical perspective, see Phillips et al. (2006) and Elith et al. (2011). Maxent performs well when compared to other modeling methods using presence-only data (Elith et al. 2006, Phillips et al. 2006) with limited sample sizes (Hernandez et al. 2006). It can also accommodate correlated variables more easily than other habitat modeling methods (Elith et al. 2011), eliminating the need to preselect environmental variables and allowing Maxent to determine which variables best fit the occurrence data. C4.3 Statistical Evaluation of the Maxent Models Maxent generates statistics that aid in determining the overall predictive strength of the model. To optimize the model s predictive ability, Maxent randomly selects 10,000 C-9

12 background points from the Plan Area to compare with the environmental conditions at occurrence points for each species (Phillips and Dudik 2008). Maxent also selects a random subset of 25% of the occurrence points to test model performance. Maxent s statistical outputs, using the test data, allow for greater interpretation of the strength of the model s predictive ability. Phillips (2010) provides a detailed explanation of the key statistical indicators used in interpreting the results that are produced by the Maxent software. The three important statistical indicators that accompany the models produced by Maxent are listed below and briefly explained. Training Gain: Training gain is a measure of goodness of fit of the model to the occurrence data. It starts at zero and increases during the Maxent run as the model is trained. At the end of the run, the gain indicates how closely the model is concentrated around the occurrence data, or how precisely it predicts suitable conditions for the species. Generally, more narrowly distributed species are likely to have a higher training gain as the model will more strongly predict the probability of species presence in narrow ranges with more limited environmental conditions, than for more wide-ranging species with a broader range of environmental conditions, less divergent than the random background sample. Area Under the Curve (AUC): To quantify the predictive power of the model, Maxent generates Receiver Operator Characteristic (ROC) curves, which measure the ability of the model to correctly decipher between random background points and actual occurrence points. The predictive power of the model is indicated both by the AUC for the test data (as the AUC approaches 1, the model s ability to decipher between random background points and actual occurrence points increases); and by the consistency between the ROCs for the test and training data. It is important to note that AUC values, like the training gain, tend to be higher for species with narrow ranges. In general, AUC scores between 0.7 and 0.8 are considered fair to good. AUC scores above 0.9 are considered excellent (Swets 1988). Binomial Test of Omission: Maxent uses test data, that is, species occurrence points not used to construct the model, to assess the model s predictive power. The Binomial Test of Omission is used to determine whether environmental conditions at test data localities are predicted by the model better than random. See Phillips (2010) for further explanation. 4.4 Predicted Habitat Maxent was used to produce a probability of species occurrence for each 1-hectare grid cell in the Plan Area. To allow direct comparison with expert models and provide suitable input to Marxan, the reserve design modeling tool, it is necessary to develop a binary habitat model, that is, habitat versus non-habitat. The threshold for each binary habitat model was C-10

13 determined using the Jenks Natural Breaks (Jenks 1967) method to classify the probabilistic output of Maxent into two classes. The Jenks method maximizes between class variability and minimizes within class variability to find the strongest natural breakpoint in the histogram of cell probability values. This approach provided a systematic and objective approach for identifying a threshold that separates areas of higher probability of occurrence (habitat) from areas of lower probability of occurrence (non-habitat) for each species. C4.5 Limitations of Maxent Maxent has excellent predictive performance for presence-only modeling (i.e., models that use only occurrence data, not absence data as well) (Elith et al. 2006), but it is not immune to the limitations of using presence-only data, the most pervasive of which is sampling bias. That is, Maxent is limited to finding the optimum probability distribution according to existing occurrence data, so it may exclude environmental conditions if occurrence records do not exist in those areas, regardless of the area s suitability. Certain measures can be taken to correct for degrees of sampling bias, but environmental conditions completely lacking occurrence data may be excluded from the Maxent distribution. Presence-only data also lacks any indication of sampling effort, so it is important to realize that the probability of suitable habitat conditions is a relative probability calculation for each species (because sampling effort may vary by species). Maxent estimates the probability that a species occurs in a certain area, given the environment and with certain assumptions about prevalence and sampling effort. Without extensive information assessing the sampling effort and bias inherent in occurrence data, it is impossible to calculate species prevalence and calibrate the likelihood of a species occurring at a given location and, by extension, to calibrate between species the probability of suitable conditions across the Plan Area. However, a relative probability distribution is still valuable in assessing the suitability of a landscape unit for a species to prioritize the area during reserve design. C5.0 REFERENCES CITED Elith, J., C. H. Graham, R. P. Anderson, M. Dudík, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J. McC. M. Overton, A. T. Peterson, S. J. Phillips, K. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberón, S. Williams, M. S. Wisz, and N. E. Zimmermann Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: C-11

14 Elith, J., S.J. Phillips, T. Hastie, M. Dudik, Y.E. Chee, and C.J. Yates A statistical explanation of MaxEnt for ecologists. Diversity and Distribution 17: Hernandez, P.A., C.H. Graham, L.L. Master and D.L. Albert The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: ) Jenks, George F The Data Model Concept in Statistical Mapping. International Yearbook of Cartography 7: Nussear, K., T. Esque, R. Inman, L. Gass, K. Thomas, C. Wallace, J. Blainey, D. Miller, and R. Webb, R Modeling habitat of the desert tortoise (Gopherus agassizii) in the Mojave and parts of the Sonoran Deserts of California, Nevada, Utah, and Arizona: U.S. Geological Survey Open-File Report Phillips, S A Brief Tutorial on Maxent in Species Distribution Modeling for Educators and Practitioners. Lessons in Conservation 3: Phillips, S.J. and M. Dudik Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: Phillips, S.J., M. Dudik, J. Elith, C.H. Graham, A. Lehmann, J. Leathwich, and S. Ferrier Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19(1): mple%20selection%20bias.pdf Phillips, S.J., R.P. Anderson and R.E. Schapire Maximum entropy modeling of species geographic distribution. Ecological Modeling 190: C-12

15 Sawyer, J.O. and T. Keeler-Wolf, and J. Evens A Manual of California Vegetation, Second Edition. Swets, J.A Measuring the accuracy of diagnostic systems. Science 240: Wisz, M.S., R.J. Hijmans, J. Li, A.T. Peterson, C.H. Graham, A. Guisan, and NCEAS Predicting Species Distributions Working Group. C-13

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