The Kitzes Lab investigates species diversity and distributions in human-altered landscapes. Where are species found in these complex landscapes, and why? What is the role of both the intrinsic ecology of these communities and of disturbances, such as land use and climate change, in determining these spatial patterns? Our ultimate goal is to use the answers to these questions to provide a clear scientific foundation for conservation, particularly through land use planning and management in light of the pressures of global change.
Our specific research methods draw heavily from the field of spatial macroecology, with a particular focus on species scaling and turnover across communities. Much of our work involves at least one of several core metrics of spatial macroecology: species abundance distributions, species-area relationships, quadrat count distributions, or various metrics of beta diversity. We study these metrics from a theoretical perspective, using a variety of mathematical and computational modeling approaches, and in the field, using large-scale acoustic surveys of bird and bat populations.
Theory and models of spatial scaling
A core goal of my research is to develop mathematical theory and models that predict the relationships between species abundance and diversity across spatial scales. Such theory can be used to “upscale” plot-scale data to estimate species richness at large scales, to “downscale” regional diversity to estimate species loss following habitat loss and climate change, and to predict turnover in species composition, or beta diversity, across space.
Focusing on scaling biodiversity data up and down, we have combined global range maps, small plot census data, and body size scaling rules to infer the likely landscape scale biodiversity impacts caused by the construction of three hydroelectric dams in northern Borneo (Kitzes and Shirley 2016). Using a new theoretical form of the species-area relationship, we have also contributed to efforts to upscale small plot data to estimate regional tree and arthropod diversity in Panama (Harte and Kitzes 2015). Recent work has also more generally explored extended macroecological frameworks for biodiversity scaling (Wilber et al. 2015)
Focusing on scaling across space, we have several ongoing projects investigating the theoretical basis of species turnover and beta diversity. After contributing to empirical work documenting the failure of a previous theory to predict these patterns (McGlinn et al. 2015), we developed a mathematical approach that uses data from small plot censuses to infer the shape of a species’ pair correlation function, a spatial measure used frequently in cosmology (Kitzes and Harte in submission). I have also developed an extension of a well-known stochastic process model that predicts the relationship between plot area and species aggregation (Kitzes in prep). In support of this work, we created and continue to lead the development of the open source Python package macroeco, which supports the evaluation and prediction of diversity patterns in ecological communities (Kitzes and Wilber 2016).
The shape of these spatial scaling metrics has specific relevance to the prediction of species extinction risk and rates. Much of our work has explicitly focused on exploring the use of spatial theory and models for predicting extinction due to habitat loss and climate-driven range contraction.
The species-area relationship (SAR) is a widely used, general model for predicting species extinction risks and rates. After identifying several shortcomings of applying classic species-area relationships to extinction prediction (Harte and Kitzes 2012), we developed extended SAR frameworks that provide probabilistic extinction estimates based on an explicit minimum viable population size (Kitzes and Harte 2013) and that distinguish between immediate and time-delayed extinctions, known as extinction debt (Kitzes and Harte 2015). We have also explored the interactions of statistical SAR theories with taxonomic boundaries (Harte et al. 2013).
Investigating species populations in fragmented habitats, we have used stochastic metapopulation models parameterized from body size scaling relationships to uncover basic principles for reserve network design (Kitzes and Merenlender 2013). We have also assisted in developing a new approach to biodiversity conservation in patchy, production forest landscapes that is based on the maintenance of temporary, rotating refugia (Ramage et al. 2013a, 2013b). Focusing on global-scale conservation outcomes, we contributed to a widely cited perspective on global tipping points (Barnosky et al. 2012) and served as a lead author for the biodiversity chapter of the recent UNEP Global Environmental Outlook (GEO-5) report.
Field surveys of bird and bat distributions
Aside from the amount of available habitat and its spatial configuration, the quality of habitat in complex human-disturbed landscapes can vary greatly. My field research involves surveying biodiversity at landscape scales using autonomous acoustic sensors, which are able to efficiently and rapidly gather diversity and activity data at large spatial and temporal scales.
We have used acoustic recording devices to study bat and bird populations in urban and agricultural landscapes in northern California. In a two-year study, we demonstrated that bat activity levels are depressed by 50% near large highways and that this effect is consistent across species (Kitzes and Merenlender 2014). A second study in vineyard landscapes found that site-scale remnant vegetation significantly increased local bat activity, while surrounding landscape features had negligible local effects (Kelly et al. 2016).
Many researchers who use acoustic sensors analyze the resulting data by hand, a time consuming process that limits the scope of sensor-based field studies. To support the expansion of this acoustic research, we developed and released an open source software package, BatID, that quickly and automatically identifies recorded bat calls to the species level.
In addition to the three core areas described above, we also conduct research on global models of sustainable resource consumption and on frameworks for reproducible research.
With a team of ten researchers, we recently used spatial data on land cover and human appropriation of net primary productivity along with a global input-output analysis (Kitzes 2014) to create the first spatially-explicit global biodiversity model that links specific economics activities to species population declines (Kitzes et al in press). This work builds on Dr. Kitzes’s long history of global-scale modeling in the context of sustainability science and human ecology, including extensive work on “ecological footprint” accounts (Kitzes et al. 2008, 2009a, 2009b, 2009c) and contributions to global analyses of nitrogen footprints (Leach et al. 2012).
Previously while at the Berkeley Institute for Data Science, Dr. Kitzes was involved in several projects that aim to improve reproducibility in computational research. He was the lead editor of a book, titled The Practice of Reproducible Research, that will be the first major work to comprehensively document and teach the core, cross-disciplinary practices of reproducible data-intensive research.