Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008–12

Baker, Nancy T.; Stone, Wesley W.

doi:10.3133/ds907ds

Cited by 49 publications

(62 citation statements)

References 3 publications

(10 reference statements)

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“…In Ohio, the use of neonicotinoids rapidly increased after 2004 when they became widely used on corn and soybeans (92,93). The mechanistic link between neonicotinoid insecticides and insect declines is established and observational studies have shown widespread impacts of their use (94–96).…”

Section: Discussionmentioning

confidence: 99%

Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA

Wepprich

Adrion

Ries

et al. 2019

Preprint

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Section: Discussionmentioning

confidence: 99%

Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA

Wepprich

Adrion

Ries

et al. 2019

Preprint

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“…We estimated county-level glyphosate application (percentage of corn and soybeans sprayed; glyApp j,t ) in Illinois from 1994-2012 from data on glyphosate purchases and land cover from the U.S. Geological Survey (USGS; Stone 2013, Baker andStone 2015). We used the 2012 values for 2013 because these data were unavailable post-2012.…”

Section: Summer Land Cover and Land Use Datamentioning

confidence: 99%

Local and cross‐seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus

et al. 2017

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Quantifying how climate and land use factors drive population dynamics at regional scales is complex because it depends on the extent of spatial and temporal synchrony among local populations, and the integration of population processes throughout a species’ annual cycle. We modeled weekly, site‐specific summer abundance (1994–2013) of monarch butterflies Danaus plexippus at sites across Illinois, USA to assess relative associations of monarch abundance with climate and land use variables during the winter, spring, and summer stages of their annual cycle. We developed negative binomial regression models to estimate monarch abundance during recruitment in Illinois as a function of local climate, site‐specific crop cover, and county‐level herbicide (glyphosate) application. We also incorporated cross‐seasonal covariates, including annual abundance of wintering monarchs in Mexico and climate conditions during spring migration and breeding in Texas, USA. We provide the first empirical evidence of a negative association between county‐level glyphosate application and local abundance of adult monarchs, particularly in areas of concentrated agriculture. However, this association was only evident during the initial years of the adoption of herbicide‐resistant crops (1994–2003). We also found that wetter and, to a lesser degree, cooler springs in Texas were associated with higher summer abundances in Illinois, as were relatively cool local summer temperatures in Illinois. Site‐specific abundance of monarchs averaged approximately one fewer per site from 2004–2013 than during the previous decade, suggesting a recent decline in local abundance of monarch butterflies on their summer breeding grounds in Illinois. Our results demonstrate that seasonal climate and land use are associated with trends in adult monarch abundance, and our approach highlights the value of considering fine‐resolution temporal fluctuations in population‐level responses to environmental conditions when inferring the dynamics of migratory species.

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“…Changing herbicide practices in central North America have largely eliminated milkweed hostplants from agricultural field crops, with fairly consistent, low levels of milkweed on the landscape starting from about 2003-2005(Zaya et al 2017. Although glyphosate tolerant soybeans and maize were introduced to the US market in 1996 and 1998 respectively (Duke and Powles 2009), actual glyphosate use lagged behind, with dramatic increases in use of the pesticide in 1998-2003 in soybean, and 2007-2008 in maize (Baker 2017).…”

Section: Monarch Butterflies In Mexican Overwintering Groundsmentioning

confidence: 99%

The Dynamic Shift Detector: an algorithm to identify changes in parameter values governing populations

Bahlai

Zipkin

2019

Preprint

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Environmental factors interact with internal rules of population regulation, sometimes perturbing systems to alternate dynamics though changes in parameter values. Yet, pinpointing when such changes occur in naturally fluctuating populations is difficult. An algorithmic approach that can identify the timing and magnitude of parameter shifts would facilitate understanding of abrupt ecological transitions with potential to inform conservation and management of species.The “Dynamic Shift Detector” is an algorithm to identify changes in parameter values governing temporal fluctuations in populations with nonlinear dynamics. The algorithm examines population time series data for the presence, location, and magnitude of parameter shifts. It uses an iterative approach to fitting subsets of time series data, then ranks the fit of break point combinations using model selection, assigning a relative weight to each break. We examined the performance of the Dynamic Shift Detector with simulations and two case studies. Under low environmental/sampling noise, the break point sets selected by the Dynamic Shift Detector contained the true simulated breaks with 70-100% accuracy. The weighting tool generally assigned breaks intentionally placed in simulated data (i.e., true breaks) with weights averaging >0.8 and those due to sampling error (i.e., erroneous breaks) with weights averaging <0.2. In our case study examining an invasion process, the algorithm identified shifts in population cycling associated with variations in resource availability. The shifts identified for the conservation case study highlight a decline process that generally coincided with changing management practices affecting the availability of hostplant resources.When interpreted in the context of species biology, the Dynamic Shift Detector algorithm can aid management decisions and identify critical time periods related to species’ dynamics. In an era of rapid global change, such tools can provide key insights into the conditions under which population parameters, and their corresponding dynamics, can shift.Author SummaryPopulations naturally fluctuate in abundance, and the rules governing these fluctuations are a result of both internal (density dependent) and external (environmental) processes. For these reasons, pinpointing when changes in populations occur is difficult. In this study, we develop a novel break-point analysis tool for population time series data. Using a density dependent model to describe a population’s underlying dynamic process, our tool iterates through all possible break point combinations (i.e., abrupt changes in parameter values) and applies information-theoretic decision tools (i.e. Akaike’s Information Criterion corrected for small sample sizes) to determine best fits. Here, we develop the approach, simulate data under a variety of conditions to demonstrate its utility, and apply the tool to two case studies: an invasion of multicolored Asian ladybeetle and declining monarch butterflies. The Dynamic Shift Detector algorithm identified parameter changes that correspond to known environmental change events in both case studies.

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Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008–12

Cited by 49 publications

References 3 publications

Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA

Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA

Local and cross‐seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus

The Dynamic Shift Detector: an algorithm to identify changes in parameter values governing populations

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