Spatial model predicts bumblebee exposure to pesticide use

Field experiments were conducted using yellow-faced bumblebees, a species native to the West Coast and an important pollinator.

By Carol Clark

It has long been known that agricultural pesticides are one of the greatest threats to bees and other essential pollinators. What farmers have lacked is an understanding of how different pesticides, applied at various times on a variety of crops, affect the risk of exposure to bees living near the fields. 

Researchers have drawn from real-world data to try to address this gap, developing and testing a spatial model for predicting pesticide exposure in bumblebees. The journal Science of the Total Environment published the work, based on the interactions of the yellow-faced bumblebee (Bombus vosnesenskii) with crops in California. 

“We were able to explain nearly 75% of the spatial variation in pesticide exposure among the bumblebee hives using our model,” says Eric Lonsdorf, first author of the study and assistant professor in Emory’s Department of Environmental Sciences. 

Relatively simple models were more effective at preventing exposures than the researchers expected.

“Our results suggest that simply data on where and when a pesticide was sprayed is all that you need to make a good prediction for the threat to nearby hives,” Lonsdorf says. 

Including data on how long a particular chemical lingers in the landscape or how attractive the flowers in a particular crop are to the bees did not make a significant difference in the model’s predictive power. 

“We found that even if a crop is not that attractive to the bees, the chemicals from that crop are still going to be found in their pollen,” Lonsdorf says. “The bees may be picking up the chemical due to drift of the pesticide onto nearby weeds where they are foraging.” 

Providing tools for conservation 

Lonsdorf studies natural capital, or nature’s contributions to humans. He translates ecological principles and knowledge into predictive models that enable industry leaders and policymakers to better manage natural resources. 

He’s currently using models he developed to help the U.S. Fish and Wildlife Service identify bee conservation priority areas in the United States. 

More research is needed, Lonsdorf says, to determine whether the bumblebee risk-prediction model will scale up across different landscapes and for different species of bees. The current study also did not delve into how the amount of a particular pesticide found in the pollen translated into toxicity for the bees. 

Co-authors of the paper include Neal Williams from the University of California, Davis, and Maj Rundlöf and Charlie Nicholson, who are affiliated with the University of California, Davis, and Lund University in Sweden. 

Drawing from fine-scaled data 

The researchers began with experiments set amid a variety of crops in northern California’s Yolo County. Fourteen pairs of yellow-faced bumblebee colonies were placed around the agricultural landscape. This species of bumblebee is native to the West Coast and the most abundant wild species of bee in this range, found in both urban and agricultural areas. 

Pollen that bees in each hive collected were sampled at six different times during the growing season. The pollen samples were then assessed for exposure to 52 different active ingredients encompassing a range of pesticides. 

Data from these experiments were combined with field-level data from the California Department of Pesticide Regulation on what pesticides were sprayed and what days they were sprayed. 

“California is unique in providing such fine-scaled, public data,” Lonsdorf says. “In most places in the United States, information on what pesticides are being sprayed is only collected at the county level and summarized on an annual basis.” 

The detailed data allowed the researchers to consider a range of factors in their predictive model to identify those factors with the most predictive power. 

“Our risk-prediction model marks another step toward evaluating pollinator-conservation issues to help guide policies for pollinator landscapes,” Lonsdorf says. “The next step is to do a field-toxicity assessment to get a better understanding of how pesticides are affecting bee health.” 

He and colleagues are now conducting such a study with honeybees, he adds. 

The current paper was supported by the National Science Foundation, California Department of Food and Agriculture, Almond Board of California, KIND Foundation Fund for Pollinator Health and the Swedish Research Council.

Related:

Analyzing ways to help golden eagle populations weather wind-energy growth

Antibiotic used on food crops affects bumblebee behavior, lab study finds

Pollinator extinctions alter structure of ecological networks

Computer scientists create simple method to speed cache sifting

"Computer performance fascinates me," says Emory graduate student Yazhuo Zhang, co-first author of the discovery, shown on a visit to Switzerland. Set to receive her PhD in May, Zhang accepted a post-doctroal fellowship at the Federal Institute of Technology Zurich (ETH Zurich).

By Carol Clark

Computer scientists have invented a highly effective, yet incredibly simple, algorithm to decide which items to toss from a web cache to make room for new ones. Known as SIEVE, the new open-source algorithm holds the potential to transform the management of web traffic on a large scale. 

SIEVE is a joint project of computer scientists at Emory University, Carnegie Mellon University and the Pelikan Foundation. The team’s paper on SIEVE will be presented at the 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI) in Santa Clara, California, in April. 

A preprint of the paper is already making waves. SIEVE became a hot topic on Hacker News and the subject of a feature in the influential tech newsletter TLDR, driving tens of thousands of visits to the SIEVE website. 

“SIEVE is bigger and greater than just us,” says Yazhuo Zhang, an Emory PhD student and co-first author of the paper. “It is already performing well but we are getting a lot of good suggestions to make it even better. That’s the beauty of the open-source world.” 

Zhang shares first authorship of the paper with Juncheng (Jason) Yang, who received his master’s degree in computer science at Emory and is now a PhD candidate at Carnegie Mellon. 

“SIEVE is an easy improvement of a tried-and-true cache-eviction algorithm that’s been in use for decades — which is literally like centuries in the world of computing,” says Ymir Vigfusson, associate professor in Emory’s Department of Computer Science. 

Vigfusson is co-senior author of the paper, along with Rashmi Vinayak, an associate professor in Carnegie Mellon’s computer science department. Yao Yue, a computer engineer at the Pelikan Foundation, is also a co-author. 

In addition to its speed and effectiveness, a key factor sparking interest in SIEVE is its simplicity, lending it scalability. 

“Simplicity is the ultimate sophistication,” Vigfusson says. “The simpler the pieces are within a system designed to serve billions of people within a fraction of a second, the easier it is to efficiently implement and maintain that system.” 

Keeping ‘hot objects’ handy 

Many people understand the value of regularly reorganizing their clothing closet. Items that are never used can be tossed and those that are rarely used can be moved to the attic or some other remote location. That leaves the items most commonly worn within easy reach so they can be found quickly, without rummaging around. 

A cache is like a well-organized closet for computer data. The cache is filled with copies of the most popular objects requested by users, or “hot objects” in IT terminology. The cache maintains this small collection of hot objects separately from a computer network’s main database, which is like a vast warehouse filled with all the information that could be served by the system. 

Caching hot objects allows a networked system to run more efficiently, rapidly responding to requests from users. A web application can effectively handle more traffic by popping into a handy closet to grab most of the objects users want rather than traveling down to the warehouse and searching through a massive database for each request. 

“Caching is everywhere,” Zhang says. “It’s important to every company, big or small, that is using web applications. Every website needs a cache system.” 

And yet, caching is relatively understudied in the computer science field. 

A logo for SIEVE, designed by Zhang, portrays hotter objects in shades of red and colder objects in shades of blue. Zhang also designed a web site for SIEVE, including a motion graphic demonstrating how it works.

A sense of wonder 

Zhang, who received her undergraduate and master’s degrees at universities in her hometown of Guangzhou, China, started off majoring in software engineering. “It’s fun to code and to make a website,” she says, “but it’s not fundamentally challenging once you learn how to do it. I wanted to gain more understanding of the backbone of technology. Computer performance fascinates me.” 

Zhang applied to Emory to work with Vigfusson given his focus on fundamental topics such as computer security and caching, and his skill at talking about them in simple terms. “It’s important to make complex ideas easy to understand,” she says. 

In turn, Vigfusson appreciates how Zhang approaches intractable problems with a sense of wonder. “She’s doing science for all the right reasons,” he says. “She is delighted by the process of exploration and by traversing the frontiers of the unknown.” 

In 2016, Vigfusson received a National Science Foundation Faculty Early Career Development Program (CAREER) grant to explore cache systems. Yang took the lead on the project while he was an Emory master’s student. As a PhD student at Carnegie Mellon, Yang continued to collaborate with Vigfusson and helped to mentor Zhang when she arrived at Emory in 2019. 

How caching works 

While caching can be thought of as a well-organized closet for a computer, it is difficult to know what should go into that closet when millions of people, with constantly changing needs, are using it. 

The fast memory of the cache is expensive to run yet critical to a good experience for web users. The goal is to keep the most useful, future information within the cache. Other objects must be continuously winnowed out, or “evicted” in tech terminology, to make room for the changing array of hot objects.

Cache-eviction algorithms determine what objects to toss and when to do so. 

FIFO, or “first-in, first-out,” is a classic eviction algorithm developed in the 1960s. Imagine objects lined up on a conveyor belt. Newly requested objects enter on the left and the oldest objects get evicted when they reach the end of the line on the right. 

In the LRU, or “least recently used,” algorithm the objects also move along the line towards eviction at the end. However, if an object is requested again while it moves down the conveyor belt, it gets moved back to the head of the line. 

Hundreds of variations of eviction algorithms exist but they have tended to take on greater complexity to gain efficiency. That generally means they are opaque to reason about and require high maintenance, especially when dealing with massive workloads. 

“If an algorithm is very complicated, it tends to have more bugs, and all of those bugs need to be fixed,” Zhang explains. 

A simple idea 

Like LRU and some other algorithms, SIEVE makes a simple tweak on the basic FIFO scheme. 

SIEVE initially labels a requested object as a “zero.” If the object is requested again as it moves down the belt, its status changes to “one.” When an object labeled “one” makes it to the end of the line it is automatically reset to “zero” and evicted. 

A pointer, or “moving hand,” also scans the objects as they travel down the line. The pointer starts at the end of the line and then jumps to the head, moving in a continuous circle. Anytime the pointer hits an object labeled “zero,” the object is evicted. 

“It’s important to evict unpopular objects as quickly as possible, and SIEVE is very fast at this task,” Zhang says. 

In addition to this quick demotion of objects, SIEVE manages to maintain popular objects in the cache with minimal computational effort, known as “lazy promotion” in computer terminology. The researchers believe that SIEVE is the simplest cache-eviction algorithm to effectively achieve both quick demotion and lazy promotion. 

A lower miss ratio 

The purpose of caching is to achieve a low miss ratio — the fraction of requested objects that must be fetched from “the warehouse.” 

To evaluate SIEVE, the researchers conducted experiments on open-source web-cache traces from Meta, Wikimedia, X and four other large datasets. The results showed that SIEVE achieves a lower miss ratio than nine state-of-the-art algorithms on more than 45% of the traces. The next best algorithm has a lower miss ratio on only 15%. 

The ease and simplicity of SIEVE raise the question of why no one came up with the method before. The SIEVE team’s focus on how patterns of web traffic have changed in recent years may have made the difference, Zhang theorizes. 

“For example,” she says, “new items now become ‘hot’ quickly but also disappear quickly. People continuously lose interest in things because new things keep coming up.” 

Web-cache workloads tend to follow what are known as generalized Zipfian distributions, where a small subset of objects account for a large proportion of requests. SIEVE may have hit a Zipfian sweet spot for current workloads. 

“It is clearly a transformative moment for our understanding of web-cache eviction,” Vigfusson says. “It changes a construct that’s been used blindly for so long.” 

Marquee companies that manage massive amounts of web traffic are making inquiries, he notes, adding, “Even a tiny improvement in a web-caching system can save millions of dollars at a major data center.”

Zhang and Yang are on track to receive their PhDs in May. 

“They are doing incredible work,” Vigfusson says. “It’s safe to say that both of them are now among the world experts on web-cache eviction.”

Related:

Anonymous cell phone data can quantify behavioral changes for flu-like illnesses

CDC funds project to automate analysis of mixed strains of antibiotic-resistant bacteria