SOW_-_CASC_Birds_and_Fire_and_LRGV_Projects_V2_1.docx

PERFORMANCE BASED STATEMENT OF WORK

Southwestern Natural Resource Management: Assessing vulnerability of vegetation and wildlife communities

1. INTRODUCTION AND BACKGROUND

A. Introduction:

The United States Geological Survey (USGS), Western Geographic Science Center (WGSC), has a requirement for work to be performed by a Data Scientist. The USGS Land Change Science project Remote Sensing for Resource Management - Southwestern Natural Resource Management is broadly concerned with issues of natural and anthropogenic disturbance impacts on in the transboundary Madrean Archipelago ecoregion, covering parts of Arizona and New Mexico in the US and Sonora and Chihuahua in Mexico, and the adjacent Northern Sierra Madre Occidental in Mexico and north to the Mogollon Plateau in Arizona. The current research objective is to 1) define ecosystem vulnerability to mega-fires and post-transformation at local and regional scales, and (2) link multi-scale bird and vegetation responses to fire regimes across the vulnerability framework to assess factors and mechanisms that drive vulnerability and resilience. This contract builds upon research conducted in FY20 to FY22 and is in support of WGSC’s FY23-24 research project funded by the USGS Southwest Climate Science Center. In addition, the Western Geographic Science Center (WGSC), has a requirement for work to be performed by a Data Scientist on the topic of spatial data modeling and analysis to support habitat conservation and landscape connectivity, in an effort to provide modeled data and guidance for USFWS and CBP on habitat connectivity issues in the Lower Rio Grande Valley.

B. Background:

1. Southwestern fire and wildlife - Wildfires have and always will be an essential part of forested systems given most species have evolved with fire for millennia. In the southwestern U.S. and northwestern Mexico, forests are vital for economic growth, recreation, clean water, and many other ecosystem services. In the last three decades these forests have been impacted by mega-fires that have fundamentally changed ecological functions and the structure and composition of communities. Therefore, it is vital to understand how both our actions and inactions contribute to the vulnerability and sustainability of forest ecosystems. To better manage our forests, the main goal of the project is to understand how recent fires have affected vegetation and animal communities by assessing mega-fires in a historical and ecological context. Doing so will require models based on remote sensing and field data at different spatial scales, including information on vegetation and wildlife. By integrating these data using spatial and temporal models our project will not only develop new knowledge but also transfer this information to resource managers in both the U.S. and Mexico, including those from First Nations by collaboratively developing sets of best management practices so that information can be applied.

2. Spatial data modeling and analysis to support landscape connectivity in the Lower Rio Grande Valley - To maintain viable populations, individual ocelots and bobcats require not only ample supplies of prey and water but also efficient energy expenditure budgets that allows predatory behavior, reproduction, and general locomotion to traverse a landscape. In the case of the Lower Rio Grande Valley (LRGV), assuring species survival requires contiguous areas of vegetation and connectivity to populations in Mexico, which is currently fragmented and impacted by border wall and other anthropogenic land uses. Similar conditions are at play for the Texas tortoise which although covering smaller home ranges (typically less than a hectare), require contiguous vegetation and food and water sources. Each species has limiting factors which impact the total energy they can expend to traverse the landscape. Tortoise energy expenditure is particularly limited by temperatures (impacting time of travel but also dependent on available shade) and local micro-topographical changes. Ocelots and bobcats share similar locomotive functions but differ in behavior, habitat, time of travel, and thermoregulation, all of which impact total energy expenditure. Ocelot is reliant on certain land cover types and is especially sensitive to vegetation density and percent canopy cover, unlike the bobcat which is a habitat generalist and is less susceptible to disturbances. These factors and therefore, energy expenditure can be multiplied by human-built obstructions such as roads, water diversion, and fences.

2. SCOPE

1. Southwestern fire and wildlife- Overall project objectives are to:

1. Define ecosystem vulnerability to mega-fires and post-fire transformation at local and regional scales. The foundation of this approach is a vulnerability framework defined by fire regime characteristics across climatic environments and associated vegetation communities. Proposed work will build upon our recent studies of human-altered fire regimes in the Madrean Sky Islands (MSI) of the U.S. and Mexico and extend our approach to a wider geographic area and range of climatic environments on the Mogollon Plateau (MP) and northern Sierra Madre Occidental (SMO).

1. Link multi-scale bird and vegetation responses to fire regimes across the vulnerability framework to assess factors and mechanisms that drive vulnerability and resilience. We will integrate remote sensing and spatial modeling with field-based data on vegetation and avian communities using data collected by collaborators in a previous study that include approximately 1700 points in Mexico and the U.S., and expand sampling to cover a broader range of environments. The framework will identify potential tipping points based on vulnerability factors and mechanisms to identify management actions.

2. Spatial data modeling and analysis to support landscape connectivity in the Lower Rio Grande Valley We are developing spatial models to estimate cumulative energy expenditure within existing corridors and habitat restoration scenarios on Lower Rio Grande National Wildlife Refuge tracts, adjacent properties, and in Tamaulipas. The models will provide managers maps and data for comparative analysis, allowing them to choose those options best suited to minimize expenditure and improve likelihood of survival and reproduction. Remote sensing and other ancillary spatial data (topography, climate, etc.) are critical for identifying and mapping important hydrologic conditions and vegetation types that support habitat and to identify connectivity and potential refugia.

3. APPLICABLE DOCUMENTS

None.

4. TECHNICAL REQUIREMENTS

Specific requirements:

A. Technical requirements: Several technical issues must be considered to successfully complete the project goals within the larger context of the USGS Land Change Science/National Land Imaging/ Southwest Climate Science Center project. Performance requires on-site or cloud-based capacity for computer-intensive data processing; assembly of spatial datasets in georeferenced formats, and their use in model construction in conjunction with available field datasets; and provision of metadata in standard formats for all data used in the project. Analysis functions designed to complete project tasks must be tested for performance and reliability; and delivery of computer code, georeferenced and tabular products must be delivered in accessible formats for use in commonly available GIS, spreadsheet, and statistical software.

B. Specific requirements: The project has specific technical and background knowledge requirements. Demonstrated experience working with large spatial data sets in combination with field data is necessary to completion of tasks. Experience in the use of generalized linear and flexible modeling techniques, multivariate statistics, as well as machine learning approaches for spatial modeling are prerequisite. Modeling experience will preferably be documented by published studies. The R Project free software environment will be used for statistical and spatial analysis; prior experience and proficiency in R programming is essential. Successful completion of the project will require a PhD with emphasis in ecology, fire ecology, and/or landscape ecology. Good communication and professional relationship skills will ensure successful participation on a research team.

C. Tasks Linking bird and vegetation communities with contemporary fire regimes Task 1. Assemble datasets for use in analyses of fire history and bird species.

A) Generate datasets that combine existing and new field data location coordinates with predictor variables derived from fire history data summarized at multiple scales.

B) Develop archive of data and code used for analysis.

Task 2. Participate as co-author on research papers for the following topics:

· Landscape change: using remote sensing indices in time series to describe ecological change.

· Climate refugia across a spectrum in vulnerability to change in climate and fire regimes (next generation of Villarreal et al. 2020).

Task 3. Develop data visualization and map products from the above research and assemble for presentation/delivery to interested stakeholders A) Organize map, tabular and graphical products for presentation B) Distribute products on agreed upon platform (i.e., report, online story map, GIS database)

Spatial data modeling and analysis to support landscape connectivity in the Lower Rio Grande Valley Task 4. Assemble information and datasets for use in modeling and support the research team with spatial modelling and statistical analyses.

A) Spatial datasets as required for team efforts, including or in addition to, disturbance history, protected area status, and existing refugia maps for the cross-border region.

B) A framework for identifying and modeling species habitat and refugia that is designed to address specific concerns of the region, including a strategy for implementation.

5. DELIVERABLES

Contractor will provide written documentation on methods that describes the process in enough detail that the work could be replicated, and with enough metadata information that would be suitable for publication in a USGS data release and/or a scientific manuscript. The contractor will provide digital maps, tables, graphs and code resulting from each of the above tasks.

6. PERFORMANCE STANDARDS

The supervisor will evaluate the quality of the work including the digital maps, tables, graphs and code resulting from each of the above tasks prior to completion and before acceptance. With map-based products, the contractor will provide some statistical estimate of thematic/model accuracy that can be used to evaluate the results.

SUPPORTING INFORMATION

1. Period of Performance: 7/1/2023-10/31/2025

2. Place of Performance: Contractor’s own facility. Contractor will not require access to USGS IT systems.

3. Format of the Deliverables are to be received in GEE scripts, methods summary document, raster image files, excel files /databases.