Projects

The Global Change Lab at the Missouri Botanical Garden has a rolling list of projects.  All pertain to broad-scale effects of past, present, and anticipated aspects of global change including climate change, shifts in land use/land cover, invasive species, and so on. A dominant theme is the investigation of the role of intraspecific variation in responses to climate change and development of techniques to identify range-shaping factors.

Inferring the Invisible: Extending the Use of Natural History Museum and Herbarium Specimens Beyond the Locations and Times They Were Collected

Sponsored by: Institute for Museum and Library Services

Collaborators: Dr. Iván Jimenez (Missouri Botanical Garden), Prof. Steven Beissinger (UC Berkeley),

Synopsis: Natural history museums and herbaria collectively hold hundreds of millions of zoological, botanical, and paleontological specimens.  However, most collections occur sporadically in space and time; i.e., they are prone to the problem of “false absence” (a species is present but not collected) and thus offer an incomplete picture of species’ distributions.  Existing methods that infer the presence of a species in places and times where it has not been collected cannot correct for false absences.  Methods that do correct for false absences require very carefully-designed surveys with known amounts of search effort within strictly delineated sites.  However, the large majority of specimens in collections databases do not conform to these requirements.  Survey effort is rarely—if ever—recorded, and many older collections are locatable only to vaguely-defined sites (e.g., a county) and therefore are regularly discarded before geographic analysis, an extremely wasteful practice that devalues the thousands of person-years required to collect and curate these specimens. This project develops methods and software that use collections data to predict the presence of a species in times and places where it has not been collected, while accounting for false absence.

bayesLopod Model

The bayesLopod model juxtaposes records of a species (top left) with sampling intensity (bottom left) to estimate the range (main panel). The model can utilize badly georeferenced specimens (this species has only 90 accurately-referenced records but the model)

 

Developing General Methods of Inferring Vulnerability to Climate Change Using Well-Known Iconic Species in the American West

Sponsor: US Geological Survey

Collaborators: Erik Beever (USGS), and ~85 others!

Synopsis: Despite numerous studies documenting species’ responses to current and past climate change, we still understand very little about what makes species differ in their susceptibility to changes in climate. In contrast to common assumptions that species will move upward in elevation and latitude in response to increasing temperatures, a growing body of evidence demonstrates that species often move in “unexpected” directions and do so heterogeneously across their ranges.  These seemingly idiosyncratic responses confound traditional approaches to predicting vulnerability to climate change because most methods assume no differentiation within a species across the range.  In turn, this hinders researchers’ ability to predict future responses and provide actionable management and conservation recommendations to natural resource managers. Hence, to predict detailed responses to climate change, we need to understand how climate influences species’ distributions heterogeneously across their ranges. This project seeks to identify and characterize the climatic, biological, and landscape-context factors that make populations of a suite of iconic species susceptible to climate change.  The focal species include Ochotona princeps (American pika) and Artemisia tridentata (big sagebrush), both of which are emblematic of the American West and attract broad interest among resource managers and the public at large. These well-known species provide a rare opportunity to develop and validate diagnostic tools for evaluating vulnerability to climate change.

Spatially-sensitive Species Distribution Model

The relative importance of different climate factors in shaping the range of Ochotona princeps, inferred from a spatially-sensitive species distribution model.

 

Phenotypic, genotypic, and biogeographic responses of a dominant prairie grass to climate change

Collaborators: Prof. Loretta Johnson (Kansas State University), Dr. Mary Knapp (Kansas State University), Prof. Sara Baer (Southern Illinois University, Carbondale)

Synopsis: Although intraspecific variation can rival interspecific variation, most of our expectations of the effects of climate change on species is based on the assumption that they respond homogenously across their ranges. This project seeks to characterize and predict the responses of intraspecific genetic and phenotypic variation in a dominant prairie grass, Andropogon gerardii (Big Bluestem). In the core of its range A. gerardii can comprise up to 80% of overground biomass, and it is an important component of any prairie restoration in the region.

Change in biomass of Andropogon gerardii due to climate change

Change in biomass of Andropogon gerardii due to climate change

 

Threats to rare plants of the United States

Sponsor: The Alan Graham Fund in Global Change

Synopsis: Understanding the threats to rare and endangered species is crucial for developing plans to assist them. This project uses the most comprehensive database on rare and threatened plants of the United States to characterize the relative frequency of threats.  Our first analysis revealed that the most common threat is actually outdoor recreation, which affects 35% of all species (19% from off-road vehicles and 13% from hiking, biking, rock climbing, and related activities), followed by threats from livestock (33%), residential development, (28%), non-native invasives (27%), and roads (21%). This is the first such assessment for these species in ~20 years. Ongoing work utilizing the results of this work seeks tests the ability of “threat-mapping” exercises to actually predict observed threats to rare species.

Number of species threatened by recreation

Number of species threatened by outdoor recreation