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Leveraging modern data science methods to infer patterns in insect distributions

The global biodiversity information facility (GBIF) now stores over 2.5 billion occurrence records. This data largely comes from the mobilization of museum specimens, and community science projects such as iNaturalist, and therefore this data is highly heterogeneous. One of the biggest challenges in biodiversity science is how to make inferences using these types of data. In the EDS lab we develop and contribute to data cleaning pipelines, as well as statistical methods for integrating insect data. Specifically, occupancy models are an emerging tool to analyze historic and opportunistic records. We are developing new iterations of occupancy models that can deal with taxonomic bias. 

Identifying causes of changes in insect distributions

Insects are declining, and factors such as habitat loss, pesticide use, agricultural intensification, and climate change have been identified as the major drivers of this decline. Our team has contributed to identifying the extent to which climate change and pesticide use is contributing to the decline of native bees in North America. 


We are working with the Entomology department at the Natural History Museum of LA County (NHMLA). Together, we evaluated the drivers of insect biodiversity across Los Angeles. We are also interested in linking local changes in habitat transformation to continental level trends. Moving forward, our group will continue tackling the causes of insect distributional changes by combining past occurrence data and climate data, with present day whole genomes to evaluate community level adaptive responses to climate change in butterflies.

 Key Publications:


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Improving insect conservation

Given the extent of the insect biodiversity crisis, evaluating, improving and developing approaches that can expedite the broad protection for multiple species is a high priority. One of the major bottlenecks when protecting insects, is the sheer number of species that are in need of assessments. Our team is incorporating the use of occupancy models in the assessment process done by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). Our goal is to expedite the process of assessments for insects by using multi-species occupancy models.

Our team is evaluating the effectiveness of the Endangered Species Act at protecting insects, and further, the models that we develop in the lab are particularly useful to model changes in the abundances of species that have limited data, or only museum specimen data is available, which is the case for many insects.

We are also co-leading an NCEAS-funded Working Group, where we are developing a conservation tool to guide the planting of native plants for insect habitat restoration. We are doing this project in tight collaboration with the California Native Plant Society and Conservation Biology Institute, and this information will be incorporated directly into CalScape (a tool to help Californians restore nature and save water via gardening), in order to maximize its impact.

Key publications: 

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Leveraging ecological theory to understand changes in insect communities

The macroinvertebrate community that lives inside bromeliads form complex food webs that allow us to link ecological theory to understand how these insect food webs persist through space and time. Our team has used experiments, population genomics, observational surveys, and large scale meta-analyses to understand how ecological processes contribute to the persistence of these food webs. Moving forward, our team will explore how plant-pollinator networks can be stable across space and time.

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Conceptual and statistical advances in metacommunity ecology

Metacommunity theory is a branch of ecology that examines the conditions in which regional dynamics maintain coexistence of locally interacting species. Our team has worked on extending this theoretical framework to include trophic interactions and by linking local coexistence theory to spatial processes. Our team is also working on better integrating ecological theory with data. We have used large scale simulation approaches and machine learning to assign ecological dynamics to abundance data through time, and moving forward we are developing an Approximate Bayesian Computation approach to infer ecological processes from metacommunity abundance data.

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