Search USGS⌕ Search

Geology topics

Carrie Brown-Lima

Publications and source records attributed to Carrie Brown-Lima.

8 recordsLinked to original sources
USGS2025

The future of biocontrol in the Anthropocene: A review of climate change impacts on biocontrol agents and their hosts

Biological control, the practice of using one species (biocontrol agent) to control the population of another (a host or prey species, hereafter target), can be a successful method to reduce pest species in agricultural and natural systems worldwide. Successful biocontrol agents often share a deep evolutionary history with their targets that results in high target specificity and synchronized phenology. However, with rapidly changing climatic conditions, users of biocontrol agents have questioned how climate change will affect both well-established and more recent biological control relationships. Using a meta-analysis of data collected from a systematic literature review, we evaluated the evidence for the impact of changing temperatures on the efficacy of biocontrol agents and corresponding responses in their targets. Overall, most studies of climate change impacts on biocontrol agents take place in the laboratory and focus on arthropod agents that are parasitoids. Results from our meta-analysis reveal that changes in temperature are projected to impact biocontrol agents and their targets similarly, with no overall significant changes to biocontrol agent or target performance. However, our results also show that temperature responses vary widely across study systems, as illustrated by case studies showing both positive, neutral, and negative effects of temperature on biocontrol agent efficacy, as well as variation in responses across the three core biological control measures of success: survival, reproduction, and efficacy. Our work highlights important knowledge gaps including how climate change will affect both biocontrol agents and their targets simultaneously. Additionally, we find that most current studies of climate impacts examined temperature relationships, predominantly of agricultural biocontrol agents. Increasing the breadth of studies is crucial for understanding the potential for climate change to affect the success of current and future biological control programs.

Ecological Applications↗
USGS2024

New York State climate impacts assessment chapter 05: Ecosystems

The people of New York have long benefited from the state's diversity of ecosystems, which range from coastal shorelines and wetlands to extensive forests and mountaintop alpine habitat, and from lakes and rivers to greenspaces in heavily populated urban areas. These ecosystems provide key services such as food, water, forest products, flood prevention, carbon storage, climate moderation, recreational opportunities, and other cultural services. This chapter examines how changes in climatic conditions across the state are affecting different types of ecosystems and the services they provide, and considers likely future impacts of projected climate change. The chapter emphasizes how climate change is increasing the vulnerability of ecosystems to existing stressors, such as habitat fragmentation and invasive species, and highlights opportunities for New Yorkers to adapt and build resilience.

New York↗
USGS2021

Translational invasion ecology: Bridging research and practice to address one of the greatest threats to biodiversity

Effective natural resource management and policy is contingent on information generated by research. Conversely, the applicability of research depends on whether it is responsive to the needs and constraints of resource managers and policy makers. However, many scientific fields including invasion ecology suffer from a disconnect between research and practice. Despite strong socio-political imperatives, evidenced by extensive funding dedicated to addressing invasive species, the pairing of invasion ecology with stakeholder needs to support effective management and policy is lacking. As a potential solution, we propose translational invasion ecology (TIE). As an extension of translational ecology, as a framework to increase collaboration among scientists, practitioners, and policy makers to reduce negative impacts of invasive species. As an extension of translational ecology, TIE is an approach that embodies an intentional and inclusive process in which researchers, stakeholders, and decision makers collaborate to develop and implement ecological research via joint consideration of the ecological, sociological, economic, and/or political contexts in order to improve invasive species management. TIE ideally results in improved outcomes as well as shared benefits between researchers and managers. We delineate the steps of our proposed TIE approach and describe successful examples of ongoing TIE projects from the US and internationally. We suggest practical ways to begin incorporating TIE into research and management practices, including supporting boundary-spanning organizations and activities, expanding networks, sharing translational experiences, and measuring outcomes. We find that there is a need for strengthened boundary spanning, as well as funding and recognition for advancing translational approaches. As climate change and globalization exacerbate invasive species impacts, TIE provides a promising approach to generate actionable ecological research while improving outcomes of invasive species management and policy decisions.

Biological Invasions↗
USGS2021

Learning augmented methods for matching: Improving invasive species management and urban mobility

With the success of machine learning, integrating learned models into real-world systems has become a critical chal- lenge. Naively applying predictions to combinatorial opti- mization problems can incur high costs, which has motivated researchers to consider learning augmented algorithms that can make use of faulty or incomplete predictions. Inspired by two matching problems in computational sustainability where data are abundant, we consider the learning augmented min weight matching problem where some nodes are revealed online while others are known a priori, e.g., by being pre- dicted by machine learning. We develop an algorithm that is able to make use of this extra information and provably im- proves upon pessimistic online algorithms. We evaluate our algorithm on two settings from computational sustainability – the coordination of opportunistic citizen scientists for inva- sive species management and the matching between taxis and riders under uncertain trip duration predictions. In both cases, we perform extensive experiments on real-world datasets and find that our method outperforms baselines, showing how learning augmented algorithms can reliably improve solu- tions for problems in computational sustainability

New York↗
USGS2021

Accelerating ecological sciences from above: Spatial contrastive learning for remote sensing

The rise of neural networks has opened the door for automatic analysis of remote sensing data. A challenge to using this machinery for computational sustainability is the necessity of massive labeled data sets, which can be cost-prohibitive for many non-profit organizations. The primary motivation for this work is one such problem; the efficient management of invasive species -- invading flora and fauna that are estimated to cause damages in the billions of dollars annually. As an ongoing collaboration with the New York Natural Heritage Program, we consider the use of unsupervised deep learning techniques for dimensionality reduction of remote sensing images, which can reduce sample complexity for downstream tasks and decreases the need for large labeled data sets. We consider spatially augmenting contrastive learning by training neural networks to correctly classify two nearby patches of a landscape as such. We demonstrate that this approach improves upon previous methods and naive classification for a large-scale data set of remote sensing images derived from invasive species observations obtained over 30 years. Additionally, we simulate deployment in the field via active learning and evaluate this method on another important challenge in computational sustainability -- landcover classification -- and again find that it outperforms previous baselines.

Proceedings of the AAAI Conference on Artificial I↗