An ~80% decline in the eastern population of the monarch butterfly (Danaus plexippus) has prompted conservation efforts to increase summer reproductive success in the Midwest United States. Implementation of conservation practices will create a patchwork of milkweed (mainly Asclepias spp.) habitat within agricultural landscapes dominated by corn and soybean production. Since the monarch butterfly is a vagile species, reproductive success is, in part, a function of both the amount and spatial arrangement of habitat patches in a fragmented landscape. To inform conservation planning we developed a spatially-explicit, agentbased model for summer breeding, non-migratory female monarch butterfly movement and egg-laying on an Iowa, USA landscape. Our model employs a unique movement algorithm when monarch agents encounter habitat edges that incorporates monarch perceptual range to their host plant and spatial memory of previously visited habitat. These behavioral factors are rarely incorporated into animal movement algorithms; however, they can influence estimates of resource utilization. Model exploration assessed the distribution and density of eggs laid on a spatially-explicit 148,665 ha landscape comprised of 17 land cover classes with varying milkweed densities. Uncertainty analysis was undertaken by sampling 25 combinations of perceptual range, spatial memory, flight step length and flight directionality parameters from a total of 256 (44) possible combinations. Movement paths simulated with our new movement algorithm show preferential use of high density milkweed areas that would not be simulated using a correlated random walk. Increasing perceptual range caused a decrease in the area used by monarch agents and caused a skewed egg distribution where most eggs were laid in relatively few habitat patches. Increasing spatial memory caused an increase in the area used but decreased the median number of eggs laid in roadside habitat. Current national and regional monarch conservation goals assume a uniform distribution of milkweed in different land cover classes. Translating these goals into spatially-explicit, heterogeneous habitat patches is essential for predicting realized fecundity in the landscape. Our model provides the foundation to link national and regional monarch conservation goals to fine scale spatial configurations of habitat patches in defined landscapes.

Survival probability is fundamental for understanding population dynamics. Methods for estimating survival probability from field data typically require marking individuals, but marking methods are not possible for arthropod species that molt their exoskeleton between life stages. We developed a novel Bayesian state-space model to estimate arthropod larval survival probability from stage-structured count data. We performed simulation studies to evaluate estimation bias due to detection probability, individual variation in stage duration, and study design (sampling frequency and sample size). Estimation of cumulative survival probability from oviposition to pupation was robust to potential sources of bias. Our simulations also provide guidance for designing field studies with minimal bias. We applied the model to the monarch butterfly (Danaus plexippus), a declining species in North America for which conservation programs are being implemented. We estimated cumulative survival from egg to pupation from monarch counts conducted at 18 field sites in three landcover types in Iowa, USA, and Ontario, Canada: road rightof-ways, natural habitats (gardens and restored meadows), and agricultural field borders. Mean predicted survival probability across all landcover types was 0.014 (95% CI: 0.004-0.024), four times lower than previously published estimates using an ad hoc estimator. Estimated survival probability ranged from 0.002 (95% CI: 7.0EÀ7 to 0.034) to 0.058 (95% CI: 0.013-0.113) at individual sites. Among landcover types, agricultural field borders in Ontario had the highest estimated survival probability (0.025 with 95% CI: 0.008-0.043) and natural areas had the lowest estimated survival probability (0.008 with 95% CI: 0.009-0.024). Monarch production was estimated as adults produced per milkweed stem by multiplying survival probabilities by eggs per milkweed at these sites. Monarch production ranged from 1.0 (standard deviation [SD] = 0.68) adult in Ontario natural areas in 2016 to 29.0 (SD = 10.42) adults in Ontario agricultural borders in 2015 per 6809 milkweed stems. Survival estimates are critical to monarch population modeling and habitat restoration efforts. Our model is a significant advance in estimating survival probability for monarch butterflies and can be readily adapted to other arthropod species with stage-structured life histories.

Conservation risks and benefits of establishing monarch butterfly (Danaus plexippus) breeding habitat in close proximity to maize and soybean fields in the North Central U.S.: A landscape-scale analysis of foliar insecticide impacts on nonmigratory monarch butterfly populations

Models are an integral part of the scientific endeavor, whether they be conceptual, mathematical, statistical, or simulation models. Models of appropriate complexity facilitate comprehension and improve understanding of the variables driving system processes. In the context of conservation planning decision-making or research efforts, a useful model can aid interpretation and avoid overfitting by including only essential elements. Models can serve two related, but different purposes: understanding and prediction of future system behavior. Predictive models can require several iterations of refinement and empirical data gathering to be useful for conservation planning. Models with less predictive ability can be used to enhance understanding of system function and generate hypotheses for empirical evaluation. Modeling monarch butterfly systems, whether it be landscape-scale movement in breeding habitats, migratory behavior, or population dynamics at monthly or yearly timeframes, is challenging because the systems encompass complex spatial and temporal interactions across nested scales that are difficult, if not impossible, to empirically observe or comprehend without simplification. We review mathematical, statistical, and simulation models that have provided insights into monarch butterfly systems. Mathematical models have provided understanding of underlying processes that may be driving monarch systems. Statistical models have provided understanding of patterns in empirical data, which may represent underlying mechanisms. Simulations models have provided understanding of mechanisms driving systems and provide the potential to link mechanisms with data to build more predictive models. As an example, recently published agent-based models of non-migratory eastern North American monarch butterfly movement and egg-laying may provide the means to explore how different spatial patterns of habitat, habitat quality, and the interaction of stressors can influence future adult recruitment. The migratory process, however, has not been addressed with agent-based modeling. Using western monarch migration as an example, we describe how modeling could be used to provide insights into migratory dynamics. Future integration of migratory models with non-migratory and population dynamics models may provide better understanding and ultimately prediction of monarch butterfly movement and population dynamics at a continental scale.

We present a method for creating a video mosaic, a twodimensional arrangement of small source videos (tiles) that suggests a larger, unified target video. We develop a distance measure to assess the match between source and target based on average color and also three-dimensional wavelet decomposition signatures in the YIQ color space. We also introduce a dynamic programming algorithm that automatically chooses the smaller tiling sub-sequences from a large collection of candidate source video sequences to best match the target video. After the selection process, the color in the tiling videos is automatically adjusted to better suggest the target video. Finally, our method also supports the use of different tiling shapes to create an additional level of visual interest.