PIs in University of California, Davis active in the last 90 days
Allocations with low numbers of SUs (10,000 or less) are usually those used as educational allocations, or are given as startup allocations, or extensions. Allocations with less than 10 SUs are usually used for storage purposes.

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Name Project Title Teragrid Resource Discipline Board Type Base Allocation
Jawdat Al-Bassam Structural mechanisms of molecular assemblies regulating the polymerization and biogenesis of microtubues PSC Regular Memory (Bridges) Cell Biology Startup 55,064
" " TACC Dell PowerEdge C8220 Cluster with Intel Xeon Phi coprocessors (Stampede) " " 50,000
" " SDSC Dell Cluster with Intel Haswell Processors (Comet) " " 20,000
" " SDSC Medium-term disk storage (Data Oasis) " " 2,000
" " PSC Storage (Bridges Pylon) " " 500
" " TACC Long-term tape Archival Storage (Ranch) " " 500
" " IU/TACC Jetstream " " 0
Varaprasad Bandaru Developing Spatially Explicit Regional Modeling Framework for Studying Impacts of Poplar based Bioenergy Systems PSC Regular Memory (Bridges) Ecological Studies Startup 50,000
" " SDSC Dell Cluster with Intel Haswell Processors (Comet) " " 50,000
" " SDSC Medium-term disk storage (Data Oasis) " " 500
" " PSC Storage (Bridges Pylon) " " 500
C. Titus Brown Compute Infrastructure to Support the Data Intensive Biology Summer Institute for Sequence Analysis at UC Davis IU/TACC Jetstream Biological Sciences Educational 432,000
Richard Grosberg The evolution and implications of life history in marine invertebrates: a genomic and transcriptomic analysis PSC Regular Memory (Bridges) Systematic and Population Biology Research 1,619,929
" " PSC Storage (Bridges Pylon) " " 2,048
" " PSC Large Memory Nodes (Bridges Large) " " 352
Melissa Kardish The role of microbiota in mediating local adaptation and plant influence on ecosystem function in a marine foundation species, Zostera marina SDSC Dell Cluster with Intel Haswell Processors (Comet) Ecological Studies Startup 50,000
" " SDSC Medium-term disk storage (Data Oasis) " " 500
Louise Kellogg CIG Science Gateway and Community Codes for the Geodynamics Community TACC Dell PowerEdge C8220 Cluster with Intel Xeon Phi coprocessors (Stampede) Geophysics Research 859,072
" " HP/NVIDIA Interactive Visualization and Data Analytics System (Maverick) " " 15,000
" " TACC Long-term tape Archival Storage (Ranch) " " 10,000
Moria Robinson From soils to webs: effects of environmental heterogeneity on specialization of plant-herbivore-parasitoid ecological networks TACC Dell PowerEdge C8220 Cluster with Intel Xeon Phi coprocessors (Stampede) Ecological Studies Startup 50,000
" " TACC Long-term tape Archival Storage (Ranch) " " 500
Daniel Standage Training in cyberinfrastructure for data intensive biology IU/TACC Jetstream Biological Sciences Startup 50,000
" " IU/TACC Storage (Jetstream Storage) " " 2,400
Michelle Stitzer Simulating Transposable Elements in Plant Genomes TACC Dell PowerEdge C8220 Cluster with Intel Xeon Phi coprocessors (Stampede) Biological Sciences Startup 50,000
" " SDSC Dell Cluster with Intel Haswell Processors (Comet) " " 20,000
" " TACC Long-term tape Archival Storage (Ranch) " " 500
" " SDSC Medium-term disk storage (Data Oasis) " " 500
Dean Tantillo MECHANISMS OF BIOORGANIC AND ORGANOMETALLIC CYCLIZATION REACTIONS PSC Regular Memory (Bridges) Organic and Macromolecular Chemistry Research 1,565,000
" " SDSC Dell Cluster with Intel Haswell Processors (Comet) " " 1,079,000
" " PSC Storage (Bridges Pylon) " " 500
" " SDSC Medium-term disk storage (Data Oasis) " " 500
Andrew Wetzel Simulating the Local Group TACC Dell PowerEdge C8220 Cluster with Intel Xeon Phi coprocessors (Stampede) Extragalactic Astronomy and Cosmology Research 3,649,351
" " TACC Long-term tape Archival Storage (Ranch) " " 50,000
Matthew Williamson Spatially explicit estimates of the likelihood of conservation action Open Science Grid (OSG) Ecological Studies Startup 100,000
" " PSC Regular Memory (Bridges) " " 10,000
" " PSC Large Memory Nodes (Bridges Large) " " 1,000
" " PSC Storage (Bridges Pylon) " " 500
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Project Abstract

Structural mechanisms of molecular assemblies regulating the polymerization and biogenesis of microtubues

PI: Jawdat Al-Bassam



Microtubules are intracellular dynamic protein tubular protein polymers that regulate the cell shape, form bipolar mitotic spindles and regulate migration of eukaryotic cells. Microtubules polymerize and depolymerize from alpha-beta tubulin heterodimer proteins maintained in the cytoplasms of eukaryotic cells. A wide variety of intracellular molecular-machines are conserved across eukaryotes that accelerate all aspects of microtubule polymerization, depolymerization and assembly of alpha-beta tubulin dimers. Microtubule based motor assemblies mediate the motility of mThese molecular assemblies mediate these activities by binding individual tubulin dimers and interacting with microtubule polymers. Our research group is focused on the physical mechanisms of these regulatory complexes. We use x-ray crystallography and cryo-electron microscopy approaches to study structures of these molecular assemblies in complex with microtubules or tubulin dimers. The process of structure determination utilizes extensive computational resources for structural determination using image analysis program such as RELION or SPARX that statistical approaches and maximum likelihood approaches to determine high resolution structures of macromolecules. Our goal in this startup allocation is to utilize these programs to determine structures of macromolecular assemblies in different states to determine their physical organization and conformational changes during activity cycles.
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Project Abstract

Developing Spatially Explicit Regional Modeling Framework for Studying Impacts of Poplar based Bioenergy Systems

PI: Varaprasad Bandaru



As a renewable energy source, biofuels are expected to play an important role in sustainably meeting U.S long term energy goals. As part of a larger regional research and development initiative focused on researching sustainable ways of producing biofuel from poplar production in the U.S Pacific Northwest region (for details, visit http://hardwoodbiofuels.org), we are interested in modeling hybrid poplar to understand different aspects at the regional level including 1) identification of potential locations for growing poplar plantation; 2) assessing inherent biomass potential on suitable locations; 3) evaluating environmental and economic impacts with adoption of hybrid poplar in the place of current croplands and conserved grasslands. For this assessment, we are planning to implement the Environmental Policy Integrated Climate Model (EPIC) at high spatial resolution. The EPIC is an integrated biophysical and biogeochemical simulation model that can be used to assess available feedstock for bioenergy, water and soil quality, greenhouse gas emissions, and nutrient loss under various climate and management conditions. Since the EPIC model is a point scale model, when applied at the spatial scale, each pixel is considered as one simulation point. Using earlier allocations, we were able to build a framework to run the EPIC model using parallel computing and made simulations for small regions in Pacific Northwest region but requires implementing over all croplands and grasslands in PNW region. As such, we need to have access to GORDON and we would like to request renewal of our project to another one year.
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Project Abstract

Developing Spatially Explicit Regional Modeling Framework for Studying Impacts of Poplar based Bioenergy Systems

PI: Varaprasad Bandaru



As a renewable energy source, biofuels are expected to play an important role in sustainably meeting U.S long term energy goals. As part of a larger regional research and development initiative focused on researching sustainable ways of producing biofuel from poplar production in the U.S Pacific Northwest region (for details, visit http://hardwoodbiofuels.org), we are interested in modeling hybrid poplar to understand different aspects at the regional level including 1) identification of potential locations for growing poplar plantation; 2) assessing inherent biomass potential on suitable locations; 3) evaluating environmental and economic impacts with adoption of hybrid poplar in the place of current croplands and conserved grasslands. For this assessment, we are planning to implement the Environmental Policy Integrated Climate Model (EPIC) at high spatial resolution. The EPIC is an integrated biophysical and biogeochemical simulation model that can be used to assess available feedstock for bioenergy, water and soil quality, greenhouse gas emissions, and nutrient loss under various climate and management conditions. Since the EPIC model is a point scale model, when applied at the spatial scale, each pixel is considered as one simulation point. Using earlier allocations, we were able to build a framework to run the EPIC model using parallel computing and made simulations for small regions in Pacific Northwest region but requires implementing over all croplands and grasslands in PNW region. As such, we need to have access to GORDON and we would like to request renewal of our project to another one year.
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Project Abstract

Developing Spatially Explicit Regional Modeling Framework for Studying Impacts of Poplar based Bioenergy Systems

PI: Varaprasad Bandaru



As a renewable energy source, biofuels are expected to play an important role in sustainably meeting U.S long term energy goals. As part of a larger regional research and development initiative focused on researching sustainable ways of producing biofuel from poplar production in the U.S Pacific Northwest region (for details, visit http://hardwoodbiofuels.org), we are interested in modeling hybrid poplar to understand different aspects at the regional level including 1) identification of potential locations for growing poplar plantation; 2) assessing inherent biomass potential on suitable locations; 3) evaluating environmental and economic impacts with adoption of hybrid poplar in the place of current croplands and conserved grasslands. For this assessment, we are planning to implement the Environmental Policy Integrated Climate Model (EPIC) at high spatial resolution. The EPIC is an integrated biophysical and biogeochemical simulation model that can be used to assess available feedstock for bioenergy, water and soil quality, greenhouse gas emissions, and nutrient loss under various climate and management conditions. Since the EPIC model is a point scale model, when applied at the spatial scale, each pixel is considered as one simulation point. Using earlier allocations, we were able to build a framework to run the EPIC model using parallel computing and made simulations for small regions in Pacific Northwest region but requires implementing over all croplands and grasslands in PNW region. As such, we need to have access to GORDON and we would like to request renewal of our project to another one year.
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Project Abstract

Compute Infrastructure to Support the Data Intensive Biology Summer Institute for Sequence Analysis at UC Davis

PI: C. Titus Brown



Large datasets have become routine in biology. However, performing a computational analysis of a large dataset can be overwhelming, especially for novices. From June 18 to July 21, 2017 (30 days), the Lab for Data Intensive Biology will be running several different computational training events at the University of California, Davis for 100 people and 25 instructors. In addition, there will be a week-long instructor training in how to reuse our materials, and focused workshops, such as: GWAS for veterinary animals, shotgun environmental -omics, binder, non-model RNAseq, introduction to Python, and lesson development for undergraduates. The materials for the workshop were previously developed and tested by approximately 200 students on Amazon Web Services cloud compute services at Michigan State University’s Kellogg Biological Station from 2010 and 2016, with support from the USDA and NIH. Materials are and will continue to be CC-BY, with scripts and associated code under BSD; the material will be adapted for Jetstream cloud usage and made available for future use.
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Project Abstract

The evolution and implications of life history in marine invertebrates: a genomic and transcriptomic analysis

PI: Richard Grosberg



Our lab aims to develop genetic resources for non-model organisms in order to better understand the evolutionary causes and consequences of diversification of life histories. We have six ongoing projects—each taking a different approach to determine the evolutionary and ecological effects of life history on varying organisms. Our projects integrate field work, bench work, and computational work. All of our projects require vast amounts of data from next generation sequencing to be completed. We are requesting computational time primarily to assemble transcriptomes and genomes for several species as well as for analyzing large RADseq datasets. Given the size of these data, we require access to computational resources beyond what we have in our laboratory.
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Project Abstract

The role of microbiota in mediating local adaptation and plant influence on ecosystem function in a marine foundation species, Zostera marina

PI: Melissa Kardish



Increasing research suggests that microbiota interact with plants and animals to alter host fitness and disease resistance. Furthermore, microbiome composition can vary among host genotypes and environments, and may contribute to observed variation in host phenotype. Individual variation in phenotype within key species, such as foundation plant species or keystone consumers, affects the structure and functioning of entire ecosystems, providing a potentially important mechanism by which microbiomes contribute to the functioning of macroscopic ecosystems. However, few experiments test causal links between host phenotype and microbiome composition, and, outside of a few model systems, virtually no studies examine the cascading effects of variation in a host’s microbiome on communities or ecosystems. I conducted a series of reciprocal transplants of the marine angiosperm, Zostera marina, and have sequenced the V4-V5 region of the 16 S gene of bacteria associated with leaves, roots, and adjacent sediment. This will allow me to examine the sources of natural variation in the microbiome of the marine angiosperm Zostera marina (eelgrass), and the potential consequences of microbiome composition for host fitness, host local adaptation, and the effect of eelgrass on ecosystem structure and functioning. To accomplish this analysis, I would like to use Qiime the Gordon Computing Cluster to assist in the processing of 16S data from these transplants as well as from temporal data.
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Project Abstract

CIG Science Gateway and Community Codes for the Geodynamics Community

PI: Louise Kellogg



The Computational Infrastructure for Geodynamics (CIG), an NSF cyberinfrastructure facility, aims to enhance the capabilities of the geodynamics community through developing software that can be used to address a range of challenging problems in geophysics. CIG supports code development and benchmarking, user training, and new users by providing small allocations of computation time along with user support for CIG codes. CIG supports the aforementioned efforts in the following areas of activity: mantle dynamics, seismic wave propagation, geodynamo, and crustal and lithospheric dynamics on both million-year and earthquake time-scales. These efforts have resulted in successful allocation requests by our community and involvement of international researchers in benchmarking the next generation of geodynamo codes all of which were enabled by our community allocation.
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Project Abstract

From soils to webs: effects of environmental heterogeneity on specialization of plant-herbivore-parasitoid ecological networks

PI: Moria Robinson



Variation in soil resources is a ubiquitous form of environmental heterogeneity, with strong direct and indirect effects on organismal traits and species interactions. Multiple theories address the relationship between resources and trophic interactions: the Resource Availability Hypothesis (RAH) suggests that resource availability shapes plant traits and, consequently, their quality to herbivores; in turn, the Slow Growth, High Mortality (SGHM) and High Performance, High Mortality Hypotheses (HPHM) relate plant quality to interactions between herbivores and natural enemies. The objective of this work is to unify these theories by exploring how soil resource variability drives trophic interactions, and extend predictions to emergent properties of ecological networks. To address these questions we sampled the diverse, dominant, and heretofore understudied assemblage of larval Lepidoptera and parasitoid enemies associated with California chaparral, across a natural soil fertility mosaic of serpentine (low resource) and non-serpentine (higher resource) soils. We built interaction networks between plants, herbivores, and parasitoids and are now ready to explore structural changes across soil context. We will do this by using the topological network metric modularity, which describes the degree to which networks are divided into small subunits of dense interactions. The algorithm used to compute modularity is computationally expensive and require extensive iteration; for this reason, XSEDE would enable us to thoroughly describe the likelihood landscape of network modularity across soil types, and iterate these calculations across multiple webs. Specifically, we will be calculating modularity for empirical webs 1000x to find the maximally likely modularity estimate. Because the likelihood landscape for such topological metrics is complex, running the algorithm many times is the best way to find the most likely modularity score. Finally, these empirical scores are compared to a null expectation built from random permutations of the original matrices. To do so, we will create 500 null webs from each original network, and again calculate modularity iteratively (1000x) per null. A pilot study indicates that each run takes ~ 10min (~200h per empirical network; 80,000h per null distribution). Because we have built networks at multiple spatial scales and are asking comparative questions, we are requesting computational power (300,000 SUs) to complete all analyses in full.
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Project Abstract

Training in cyberinfrastructure for data intensive biology

PI: Daniel Standage



This proposal requests computing resources on the IU/TACC Jetstream platform in support of training and outreach efforts in the Lab for Data Intensive Biology at UC Davis. Through our lab's research collaborations, workshops, rotation student mentoring, and informal weekly "meet and analyze data" sessions, we frequently encounter scientists faced with bioinformatics computing problems beyond what their training has prepared them for. Accordingly, along with our research software tools we have published several protocols for analysis of genome-scale data (http://khmer-protocols.readthedocs.org/). These protocols are now one of our go-to resources for on-boarding new students, collaborators, and colleagues. However, one lingering obstacle in these training efforts is that the laptop and desktop computers most scientists have access to are insufficient even for these basic introductory protocols, and much more so for their ongoing computing needs. The immediate goal of this allocation is to address the need for compute resources in these training and outreach activities. As a secondary result we hope and expect that experience with these resources will help our colleagues become more independent, empowered to write their own research allocation proposals and utilize the diversity of cyberinfrastructure resources at their disposal.
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Project Abstract

Simulating Transposable Elements in Plant Genomes

PI: Michelle Stitzer



Unlike most genes which are static in their positions on chromosomes, transposable elements (TEs) are pieces of DNA that can move from one position to another in the genome. Discovered in maize where they make up 85% of the genome, TEs are major components of most eukaryotic genomes. When TEs move to new positions in the genome, they generate mutations where they insert. These new mutations have been implicated in a multitude of plant phenotypes and responses to the environment, but genome-wide identification of novel and non-reference TEs has been difficult in plants, in part due to their recent and widespread amplification through plant genomes. I will use XSEDE resources to simulate TE movement in the maize and Arabidopsis genomes, and benchmark recently developed methods that identify new TE insertions by mapping paired-end Illumina reads from resequenced individuals to a reference genome. I have simulated genomes with TEs in new positions, and with simulated Illumina reads from these genomes. I will apply different TE detection methods (including splitreader, pecnv, reannotaTE, and others) to better understand the sensitivity and specificity of these methods. This benchmarking will facilitate my interpretation of results obtained from species-wide resequencing data.
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Project Abstract

MECHANISMS OF BIOORGANIC AND ORGANOMETALLIC CYCLIZATION REACTIONS

PI: Dean Tantillo



The focus of the research proposed herein, a renewal of CHE030089N, is to apply modern quantum chemical methods to the elucidation of molecular mechanisms of organic chemical reactions that are used in the synthesis and biosynthesis of polycyclic organic molecules. During this award period, we will focus on cation-promoted polycyclization reactions involved in biosyntheses of terpene natural products (expanded from the previous grant period), and will broaden our efforts to include Rh-promoted cyclization reactions. During this grant period we will focus our efforts on direct/ab initio molecular dynamics calculations and extensive conformational searches, which are the most time-consuming calculations we carry out.
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Project Abstract

Simulating the Local Group

PI: Andrew Wetzel



We request a renewal allocation to continue our ultra-high-resolution simulations of galaxy evolution, star formation, and stellar feedback, with which we will study the physics of the interstellar medium (ISM), the formation of stars, stellar feedback, galaxy formation, and the cosmological distribution of dark matter, with new physics and game-changing resolution. This renewal will allow us to build on the significant numerical and physical advances that our previous XSEDE research allocations have enabled, in order to run a targeted suite of simulations, using dark matter + gas + stars together with state-of-the-art treatments of stellar physics, to understand the Local Group, comprising the Milky Way (MW), Andromeda (M31), the Large Magellanic Cloud (LMC), and numerous satellite dwarf galaxies. A wealth of exciting ongoing/upcoming observational projects are targeted to near-field cosmology and galactic archaeology in the Local Group, by measuring stellar populations and phase-space distribution of stars in/around the Milky Way, Andromeda, and its satellite dwarf galaxies at un- precedented levels, including the Hubble Space Telecope, SDSS-APOGEE survey, the Dark Energy Survey (DES), the Gaia mission (surveying 1 billion stars), and LSST. These observational campaigns are revolutionizing our understanding of galaxy formation, from massive galaxies like the Milky Way to the faintest known dwarf galaxies, as well as the nature of dark matter on the smallest cosmological scales. However, interpreting and understanding these results, including making predictions for upcoming observations, requires ultra-high-resolution cosmological simulations, which can resolve structure on 1 pc scales, and which include the necessary physics of hydrodynamics, star formation, and feedback, all carefully targeted to the environment of the Local Group. Thus, we propose a suite of cosmological zoom-in simulations targeted to the Milky Way, Andromeda, and the LMC, all at unprecedentedly high resolution, to provide much-needed theoretical insight, guidance, and mock datasets for these observations. Each of our simulated systems will be resolved with > 200 million particles and followed self-consistently over their entire history to the present day in live cosmological settings carefully matched to the Local Group environment, using our state-of-the-art Feedback In Realistic Environments (FIRE) physics model. These simulations not only will represent a significant numerical improvement beyond previous work in terms of resolution, but also they will enable us to model the physics of the Local Group with unprecedented realism, as our simulations uniquely incorporate all of the important stellar feedback mechanisms: radiation pressure in the ultraviolet, optical, and infrared; stellar winds; supernova explosions of Types I & II; and photoionization heating in HII regions, in a self-consistent manner. Because of their unprecedented resolution, physical realism, and careful matching to the Local Group environment, our proposed simulations will address a wide array of timely scientific questions. For the galaxies like the Milky Way/Andromeda, we will study in detail (1) gas accretion, angular momentum transport, and its role in disk formation, including the impact of close pairs of galaxies like the Milky Way and Andromeda, (2) turbulent cascades in the ISM induced by cosmic accretion and stellar feedback, (3) stellar migration and chemical mixing within the disk, (4) the impact of massive satellites/subhalos on kinematic heating of the disk, and (5) how 6-D measurement of stellar orbits in the halo can be used to reconstruct the underlying mass/potential of the Milky Way. Moreover, our simulations are the first that span the dynamic range needed to model self-consistently the satellite dwarf galaxies that are observed around the Milky Way and Andromeda, while including the relevant baryonic physics to predict the properties of their observed stars: from massive satellites like the LMC with M∗ = 2 × 109 M⊙ to faint dwarf galaxies with M∗ ∼ 105 M⊙. Because they are so faint and dark-matter dominated, such “dwarf” galaxies represent a key frontier field for testing (1) the Cold Dark Matter (CDM) paradigm of cosmology, (2) the epoch of re-ionization, and (3) the most extreme regimes of galaxy evolution. Our initial results from our previous XSEDE allocation have demonstrated that our physically motivated stellar feedback models can resolve several outstanding mysteries regarding the properties of these faint satellite galaxies. Our proposed simulations thus represent the culmination of several years of work supported by XSEDE, involving code development and optimization customized for the physical problems of galaxy formation. Our simulations also will be critical to enable the NSF-funded work of several graduate students and postdocs. In this renewal, we request 15 million SUs to run a suite of targeted simulations. Specifically, we will run the following simulations, carefully designed for targeted questions regarding the environment of the Local Group: 3 realizations of Local Group-like pairs of Milky Way and Andromeda-like galaxies (10.5 million SUs), and 3 realizations of Milky-Way-like galaxies with LMC-like satellites (3 million SUs). In each case, we will run 3 realization, which represents a minimum reasonable number for statistically significant results and to survey scatter in galaxy formation history. To compare with Local Group observations, we must run each simulation across its entire formation history to the present day (z = 0).
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Project Abstract

Spatially explicit estimates of the likelihood of conservation action

PI: Matthew Williamson



Conservation is an inherently human endeavor - initiated, designed, and deployed by humans to alter future behavior or undo previous impacts to affect positive changes in biodiversity. Predicting where conservation will occur in the future, however, remains a challenge. We propose a conceptual model where conservation action is determined by gradients of ecological value, individual willingness, and institutional ability. Conservation action may occur at any location along this 3-dimensional continuum, but becomes increasingly likely as ecological values, individual willingness, and institutional ability simultaneously approach their maxima. We demonstrate this approach using available high-resolution, spatially explicit data on demographics, economic drivers, institutional characteristics, and environmental conditions to evaluate the degree to which the spatial coincidence of these factors affects the likelihood of conservation. We use Bayesian hierarchical models that treat past conservation action as probabilistic outcomes of the interaction of ecological, institutional, and social covariates to identify key explanatory variables influencing the likelihood of conservation action using multi-model inference and hierarchical variance partitioning to evaluate the relative importance of each factor in explaining past conservation. We then implement these models in a GIS to generate probabilistic surfaces of the likelihood conservation action to identify where conservation is likely in the future.