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Whether wild herbivores confer biotic resistance to invasion by exotic plants remains a key question in ecology. There is evidence that wild herbivores can impede invasion by exotic plants, but it is unclear whether and how this generalises across ecosystems with varying wild herbivore diversity and functional groups of plants, particularly over long-term (decadal) time frames. Using data from three long-term (13- to 26-year) exclosure experiments in central Kenya, we tested the effects of wild herbivores on the density of exotic invasive cacti, Opuntia stricta and O. ficus-indica (collectively, Opuntia), which are among the worst invasive species globally. We also examined relationships between wild herbivore richness and elephant occurrence probability with the probability of O. stricta presence at the landscape level (6150 km2). Opuntia densities were 74% to 99% lower in almost all plots accessible to wild herbivores compared to exclosure plots. Opuntia densities also increased more rapidly across time in plots excluding wild herbivores. These effects were largely driven by megaherbivores (≥1000 kg), particularly elephants. At the landscape level, modelled Opuntia stricta occurrence probability was negatively correlated with estimated species richness of wild herbivores and elephant occurrence probability. On average, O. stricta occurrence probability fell from ~0.56 to ~0.45 as wild herbivore richness increased from 6 to 10 species and fell from ~0.57 to ~0.40 as elephant occurrence probability increased from ~0.41 to ~0.84. These multi-scale results suggest that any facilitative effects of Opuntia by wild herbivores (e.g. seed/vegetative dispersal) are overridden by suppression (e.g. consumption, uprooting, trampling). Synthesis. Our experimental and observational findings that wild herbivores confer resistance to invasion by exotic cacti add to evidence that conserving and restoring native herbivore assemblages (particularly megaherbivores) can increase commun

Resource selection functions are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because resource selection functions assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates. Data thinning, generalized estimating equations, and step selection functions have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data, and (in the case of step selection functions) scale-dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on resource selection functions. This method weights each observed location in an animal's movement track according to its level of autocorrelation, expanding confidence intervals to match an objective target (i.e., the effective sample size for Autocorrelated Gaussian Density Estimation) and accounting for bias that can arise when there are gaps in the movement track. In this study, we describe the mathematical principles underlying our method, demonstrate its practical advantages versus conventional approaches using simulations and empirical data on a water mongoose (\textit{Atilax paludinosus}), a caracal (\textit{Caracal caracal}), and a serval (\textit{Leptailurus serval}), and discuss pathways for further development of our method. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the \texttt{c

1. Bergmann's rule—which posits that larger animals live in colder areas—is thought to influence variation in body size within species across space and time, but evidence for this claim is mixed. 2. We used Bayesian hierarchical models to test four competing hypotheses for spatiotemporal variation in body size within 20 bat species across North America: (1) the heat conservation hypothesis, which posits that increased body size facilitates body heat conservation (and which is the traditional explanation for the mechanism underlying Bergmann's rule); (2) the heat mortality hypothesis, which posits that increased body size increases susceptibility to acute heat stress; (3) the resource availability hypothesis, which posits that increased body size is enabled in areas with more abundant food; and (4) the starvation resistance hypothesis, which posits that increased body size reduces susceptibility to starvation during acute food shortages. 3. Spatial variation in body mass was most consistently (and negatively) correlated with mean annual temperature, supporting the heat conservation hypothesis. Across time, variation in body mass was most consistently (and positively) correlated with net primary productivity, supporting the resource availability hypothesis. 4. Climate change could influence body size in animals through both changes in mean annual temperature and resource availability. Rapid reductions in body size associated with increasing temperatures have occurred in short-lived, fecund species, but such reductions will be obscured by changes in resource availability in longer-lived, less fecund species.

Data, code, and metadata that can be used to reproduce the analyses underlying 'Environmental drivers of body size in North American bats' by Alston et al. 2023 Functional Ecology (Preprint: https://doi.org/10.1101/2021.07.28.454183). Individual data owners are detailed in the data. Data may only be used to reproduce the analyses underlying Alston et al. (2023). Any other use requires the written permission of the individual data owners. For more detailed information, please visit the metadata file.

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal’s home range, and many methods of estimating home ranges have arisen over the past half century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as Kernel Density Estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian Bridge Movement Models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of applying these two classes of space use estimators appropriately and distinctly. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home-range estimates quantified using occurrence estimators are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use range estimators to estimate the area of home ranges and occurrence estimators to answer other questions in movement ecology, such as when and where an animal crosses a linear feature, visits a location of interest, or interacts with other animals.

The study of habitat selection is a foundational component of basic and applied animal ecology. Today, habitat selection in mammals is primarily studied using resource selection functions, a class of models that uses logistic regression to compare “used” to “available” habitat. However, these models have several statistical problems, including rampant pseudoreplication from failing to account for autocorrelation in modern animal movement data, no clear guidelines for sampling available habitat, and large amounts of numerical error from sampling too few available points. These problems are widely acknowledged but have no generally accepted solutions, so we propose three new methods for addressing them: likelihood weighting, Gaussian availability sampling, and numerical convergence checks. We demonstrate the practical advantages of these methods over conventional approaches using simulations and empirical data on a water mongoose (Atilax paludinosus), a caracal (Caracal caracal), and a serval (Leptailurus serval), and briefly demonstrate how to apply our methods to animal tracking data using the ‘ctmm’ R package. Broad uptake of these methods could substantially improve our estimates of habitat selection in mammals.

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal’s home range, and many methods of estimating home ranges have arisen over the past half century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as Kernel Density Estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian Bridge Movement Models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of applying these two classes of space use estimators appropriately and distinctly. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home-range estimates quantified using occurrence estimators are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use range estimators to estimate the area of home ranges and occurrence estimators to answer other questions in movement ecology, such as when and where an animal crosses a linear feature, visits a location of interest, or interacts with other animals.

1. Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. 2. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. 3. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. 4. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

Homeothermy requires increased metabolic rates as temperatures decline below the thermoneutral zone, so homeotherms typically select microhabitats within or near their thermoneutral zones during periods of inactivity. However, many mammals and birds are heterotherms that relax internal controls on body temperature when maintaining a high, stable body temperature is energetically costly. Such heterotherms should be less tied to microhabitats near their thermoneutral zones, and because heterotherms spend more time in torpor and expend less energy at colder temperatures, heterotherms may even select microhabitats in which temperatures are well below their thermoneutral zones. We studied how temperature and daily torpor influence selection of diurnal roosts by a heterothermic bat (Myotis thysanodes). We (1) quantified the relationship between ambient temperature and daily duration of torpor, (2) simulated daily energy expenditure over a range of microhabitat (roost) temperatures, and (3) quantified the influence of roost temperature on roost selection. While warm roosts substantially reduced energy expenditure of simulated homeothermic bats, heterothermic bats modulated their use of torpor to maintain a constant level of energy expenditure over the course of a day. Daily torpor expanded the range of energetically economical microhabitats, such that roost selection was independent of roost temperature. Our work adds to a growing literature documenting functions of torpor beyond its historical conceptualization as a last-resort measure to save energy during extended or acute energetic stress.

The movement of animals through landscapes worldwide drives ecological processes, influences disease transmission, and governs how humans and wildlife interact. High resolution animal tracking data have transformed our ability to understand when, where, how, and why animals move. However, these data come with formidable statistical challenges including strong autocorrelation and context-dependent location errors and fix rates. Overcoming these hurdles requires an interdisciplinary effort that combines ecology, physics, geostatistics, signal processing, and computer science. In this talk, I detail ongoing work at CASUS in animal movement research, covering statistical methods and software development as well as applications in ecology, wildlife management, and autonomous vehicles research. I also highlight the role that aggregated, multispecies tracking datasets play in understanding animal movement and its consequences at the global scale. Finally, I discuss future directions for this research program, outlining potential points of collaboration with researchers coming from different disciplines.

The movement of animals through landscapes worldwide drives ecological processes, influences disease transmission, and governs how humans and wildlife interact. High resolution animal tracking data have transformed our ability to understand when, where, how, and why animals move. However, these data come with formidable statistical challenges including strong autocorrelation and context-dependent location errors and fix rates. Overcoming these hurdles requires an interdisciplinary effort that combines ecology, physics, geostatistics, signal processing, and computer science. In this talk, I detail ongoing work at CASUS in animal movement research, covering statistical methods and software development as well as applications in ecology, wildlife management, and autonomous vehicles research. I also highlight the role that aggregated, multispecies tracking datasets play in understanding animal movement and its consequences at the global scale. Finally, I discuss future directions for this research program, outlining potential points of collaboration with researchers coming from different disciplines.

Bergmann’s Rule—which posits that larger animals live in colder areas—is thought to influence variation in body size within species across space and time, but evidence for this claim is mixed. We tested four competing hypotheses for spatio-temporal variation in body size within bat species during the past two decades across North America: (1) the heat conservation hypothesis, which posits that increased body size facilitates body heat conservation (and which is the traditional explanation for the mechanism underlying Bergmann’s Rule); (2) the heat mortality hypothesis, which posits that increased body size increases susceptibility to acute heat stress; (3) the resource availability hypothesis, which posits that increased body size is enabled in areas with more abundant food; and (4) the starvation resistance hypothesis, which posits that increased body size reduces susceptibility to starvation during acute food shortages. Bayesian hierarchical models revealed that spatial variation in body mass was most consistently (and negatively) correlated with mean annual temperature, supporting the heat conservation hypothesis. Across time, variation in body mass was most consistently (and positively) correlated with net primary productivity, supporting the resource availability hypothesis. Climate change could influence body size in animals through both changes in mean annual temperature and in resource availability. Rapid reductions in body size associated with increasing temperatures have occurred in short-lived, fecund species, but such reductions likely transpire more slowly in longer-lived species.

The study of habitat selection is a foundational component of basic and applied animal ecology. Today, habitat selection in mammals is primarily studied using resource selection functions, a class of models that uses logistic regression to compare “used” to “available” habitat. However, these models have several statistical problems, including rampant pseudoreplication from failing to account for autocorrelation in modern animal movement data, no clear guidelines for sampling available habitat, and large amounts of numerical error from sampling too few available points. These problems are widely acknowledged but have no generally accepted solutions, so we propose three new methods for addressing them: likelihood weighting, Gaussian availability sampling, and numerical convergence checks. We demonstrate the practical advantages of these methods over conventional approaches using simulations and empirical data on a water mongoose (Atilax paludinosus), a caracal (Caracal caracal), and a serval (Leptailurus serval), and briefly demonstrate how to apply our methods to animal tracking data using the ‘ctmm’ R package. Broad uptake of these methods could substantially improve our estimates of habitat selection in mammals.

Data and code that can be used to reproduce the analyses underlying 'Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting' by Alston, Fleming, et al. (Preprint: https://doi.org/10.1101/2022.04.21.489059) For more detailed information, please visit the README file.

This repository contains all data, code, and metadata required to reproduce the results detailed in "Daily torpor reduces the energetic consequences of microhabitat selection for a widespread bat" by Alston et al.

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal’s home range, and many methods of estimating home ranges have arisen over the past half century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as Kernel Density Estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian Bridge Movement Models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of applying these two classes of space use estimators appropriately and distinctly. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home-range estimates quantified using occurrence estimators are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use range estimators to estimate the area of home ranges and occurrence estimators to answer other questions in movement ecology, such as when and where an animal crosses a linear feature, visits a location of interest, or interacts with other animals.

1. Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. 2. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. 3. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. 4. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

In this presentation, we provided an introduction to the animal movement research we conduct at CASUS.

In this workshop, we presented an introduction to continuous-time movement models for modeling animal movement

1. Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. 2. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. 3. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. 4. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

1. The extinction of 80% of megaherbivore (>1,000 kg) species towards the end of the Pleistocene altered vegetation structure, fire dynamics, and nutrient cycling worldwide. Ecologists have proposed (re)introducing megaherbivores or their ecological analogues to restore lost ecosystem functions and reinforce extant but declining megaherbivore populations. However, the effects of megaherbivores on smaller herbivores are poorly understood. 2. We used long-term exclusion experiments and multispecies hierarchical models fitted to dung counts to test 1) the effect of megaherbivore (elephant and giraffe) presence-absence on the occurrence (dung presence) and use intensity (dung-pile density) of mesoherbivores (2–1,000 kg), and 2) the extent to which the responses of each mesoherbivore species was predictable based on their traits (diet and shoulder height) and phylogenetic relatedness. 3. Megaherbivores increased the predicted occurrence and use intensity of zebras but reduced the occurrence and use intensity of several other mesoherbivore species. The negative effect of megaherbivores on mesoherbivore occurrence was stronger for shorter species, regardless of diet or relatedness. 4. Megaherbivores substantially reduced the expected total use intensity (i.e., cumulative dung density of all species) of mesoherbivores, but only minimally reduced the expected species richness (i.e., cumulative predicted occurrence probabilities of all species) of mesoherbivores (by <1 species). 5. Simulated extirpation of megaherbivores altered use intensity by mesoherbivores, which should be considered during (re)introductions of megaherbivores or their ecological proxies. Species’ traits (in this case shoulder height) may be more reliable predictors of mesoherbivores’ responses to megaherbivores than phylogenetic relatedness, and may be useful for predicting responses of data-limited species.

Bergmann’s Rule—which posits that larger animals live in colder areas—is thought to influence variation in body size within species across space and time, but evidence for this claim is mixed. We tested four competing hypotheses for spatio-temporal variation in body size within bat species during the past two decades across North America. Bayesian hierarchical models revealed that spatial variation in body mass was most strongly correlated with mean annual temperature, supporting the heat conservation hypothesis (the mechanism historically believed to underlie Bergmann’s Rule). Across time, variation in body mass was most strongly correlated with net primary productivity, supporting the resource availability hypothesis. Climate change may influence body size in animals but will likely do so through both changes in mean annual temperature and in resource availability. Rapid reductions in body size alongside climate change have occurred in short-lived, fecund species, but such reductions may transpire more slowly in longer-lived species.

In this workshop, we presented an introduction to continuous-time movement models for modeling animal movement

In this presentation, we provided an introduction to the animal movement research we conduct at CASUS.

Data and code that can be used to reproduce the analyses underlying "Experimental evidence that effects of megaherbivores on mesoherbivore space use are influenced by species’ traits" by Wells et al. (2021) https://doi.org/10.1111/1365-2656.13565

Diverse communities of large mammalian herbivores (LMH), once widespread, are now rare. LMH exert strong direct and indirect effects on community structure and ecosystem functions, and measuring these effects is important for testing ecological theory and for understanding past, current, and future environmental change. This in turn requires long-term experimental manipulations, owing to the slow and often nonlinear responses of populations and assemblages to LMH removal. Moreover, the effects of particular species or body-size classes within diverse LMH guilds are difficult to pinpoint, and the magnitude and even direction of these effects often depends on environmental context. Since 2008, we have maintained the Ungulate Herbivory Under Rainfall Uncertainty (UHURU) experiment, a series of size-selective LMH exclosures replicated across a rainfall/productivity gradient in a semi-arid Kenyan savanna. The goals of the UHURU experiment are to measure the effects of removing successively smaller size classes of LMH (mimicking the process of size-biased extirpation) and to establish how these effects are shaped by spatial and temporal variation in rainfall. The UHURU experiment comprises three LMH-exclusion treatments and an unfenced control, applied to 9 randomized blocks of contiguous 1 ha plots (n = 36). The fenced treatments are: “MEGA” (exclusion of megaherbivores, elephant and giraffe); “MESO” (exclusion of herbivores ≥40 kg); and “TOTAL” (exclusion of herbivores ≥5 kg). Each block is replicated three times at three sites across the 20 km rainfall gradient, which has fluctuated over the course of the experiment. The first five years of data were published previously (Ecological Archives E095-064) and have been used in numerous studies. Since publication of this original data paper, we have (a) continued to collect data according to the original protocols, (b) improved the taxonomic resolution and accuracy of plant and small-mammal identifications, and (c) begun co

Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

1. Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. 2. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. 3. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. 4. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z$^\prime$ = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z$^\prime$ = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.

This item contains a number of different correspondence regarding the Okefenokee Refuge and proposed DuPont mining. Some are letters written to Congressman Saxby Chambliss, asking him not to allow progress to move forward. Others are to Congressman Charles Norwood, Dr. Leon Aronson, and DuPont shareholders and its CEO, among others. Also included are letters to the editors of various papers, articles from the Atlanta Constitution, the Georgia Times-Union, and other publications, as well as informational sheets speaking out against the proposed mining.

This item contains a number of different correspondence regarding the Okefenokee Refuge and proposed DuPont mining. Some are letters written to Congressman Saxby Chambliss, asking him not to allow progress to move forward. Others are to Congressman Charles Norwood, Dr. Leon Aronson, and DuPont shareholders and its CEO, among others. Also included are letters to the editors of various papers, articles from the Atlanta Constitution, the Georgia Times-Union, and other publications, as well as informational sheets speaking out against the proposed mining.

The National Museum of the Pacific War presents an interview with J.C. Alston. Alston was born in 1923. He quit high school and joined the Navy in August, 1941. After six months of boot training in San Diego, he was assigned as a deck-hand to USS California (BB-44). The ship left port in October 1940. He tells of the Japanese attack on Pearl Harbor and of hearing the order to abandon ship after it received torpedo and bomb damage. He recalls being temporarily assigned to USS Maryland (BB-46) before going aboard USS West Virginia (BB-48) on which he served as part of a gun crew. He remembers being involved in the invasions of Leyte, Iwo Jima and Okinawa and the ship being damaged by kamikazes. He also witnessed the hit on USS Franklin (CV-13). Alston returned to the US and was discharged in October, 1945.