Testing the Predictive Performance of Distribution Models

Distribution models are used to predict the likelihood of occurrence or abundance of a species at locations where census data are not available. An integral part of modelling is the testing of model performance. We compared different schemes and measures for testing model performance using 79 species from the North American Breeding Bird Survey. The four testing schemes we compared featured increasing independence between test and training data: resubstitution, random data hold-out and two spatially segregated data hold-out designs. The different testing measures also addressed different levels of information content in the dependent variable: regression R2 for absolute abundance, squared correlation coefficient r2 for relative abundance and AUC/Somer’s D for presence/absence. We found that higher levels of independence between test and training data lead to lower assessments of prediction accuracy. Even for data collected independently, spatial autocorrelation leads to dependence between random hold-out test data and training data, and thus to inflated measures of model performance. While there is a general awareness of the importance of autocorrelation to model building and hypothesis testing, its consequences via violation of independence between training and testing data have not been addressed systematically and comprehensively before. Furthermore, increasing information content (from correctly classifying presence/absence, to predicting relative abundance, to predicting absolute abundance) leads to decreasing predictive performance. The current tests for presence/absence distribution models are typically overly optimistic because a) the test and training data are not independent and b) the correct classification of presence/absence has a relatively low information content and thus capability to address ecological and conservation questions compared to a prediction of abundance. Meaningful evaluation of model performance requires testing on spatially independent data, if the intended application of the model is to predict into new geographic or climatic space, which arguably is the case for most applications of distribution models

Pushing the pace of tree species migration

Plants and animals have responded to past climate changes by migrating with habitable environments, sometimes shifting the boundaries of their geographic ranges by tens of kilometers per year or more. Species migrating in response to present climate conditions, however, must contend with landscapes fragmented by anthropogenic disturbance. We consider this problem in the context of wind-dispersed tree species. Mechanisms of long-distance seed dispersal make these species capable of rapid migration rates. Models of species-front migration suggest that even tree species with the capacity for long-distance dispersal will be unable to keep pace with future spatial changes in temperature gradients, exclusive of habitat fragmentation effects. Here we present a numerical model that captures the salient dynamics of migration by long-distance dispersal for a generic tree species. We then use the model to explore the possible effects of assisted colonization within a fragmented landscape under a simulated tree-planting scheme. Our results suggest that an assisted-colonization program could accelerate species-front migration rates enough to match the speed of climate change, but such a program would involve an environmental-sustainability intervention at a massive scale

Bayesian Inference on a Mixed-Effects Location-Scale Model with Normal and Skewed Error Distributions

In handling dependent data, mixed-effects models are commonly used. These models allow for each individual in the population to vary randomly about an overall population location. Most methods focus on modeling the mean structure and treat the resulting between- and within-subject variances as nuisance parameters. Hedeker has extended these models to allow for simultaneous modeling of both the mean and variance components, each with appropriate random effects. His work has focused on data with large amounts of repeated observations (30-50) from a one-week period. His Marginal Maximum Likelihood estimation approach provides unbiased estimates in those situations, but oftentimes fails to provide feasible results for these mixed-effects locationscale models in other situations. By implementing a Bayesian Markov chain Monte-Carlo I am able to fit these models in a more general setting that can include repeat observations collected over a two-year span. I have also adapted this model to utilize the skew-normal distribution which allows for skewed-error distributions. In applying these techniques to data from a bipolar clinical trial, I am able to explain how different treatments impact the resulting scores for depression and mania in both their mean and variance. These techniques lend themselves to addressing many research questions that would focus on stabilizing the mood in their subjects

Demographic Amplification of Climate Change Experienced by the Contiguous United States Population during the 20th Century

Better understanding of the changing relationship between human populations and climate is a global research priority. The 20th century in the contiguous United States offers a particularly well-documented example of human demographic expansion during a period of radical socioeconomic and environmental change. One would expect that as human society has been transformed by technology, we would become increasingly decoupled from climate and more dependent on social infrastructure. Here we use spatially-explicit models to evaluate climatic, socio-economic and biophysical correlates of demographic change in the contiguous United States between 1900 and 2000. Climate-correlated variation in population growth has caused the U.S. population to shift its realized climate niche from cool, seasonal climates to warm, aseasonal climates. As a result, the average annual temperature experienced by U.S. citizens between 1920 and 2000 has increased by more than 1.5°C and the temperature seasonality has decreased by 1.1°C during a century when climate change accounted for only a 0.24°C increase in average annual temperature and a 0.15°C decrease in temperature seasonality. Thus, despite advancing technology, climate-correlated demographics continue to be a major feature of contemporary U.S. society. Unfortunately, these demographic patterns are contributing to a substantial warming of the climate niche during a period of rapid environmental warming, making an already bad situation worse

The Limitations of Hierarchical Organization

The concept of hierarchical organization is commonplace in science. Subatomic particles compose atoms, which compose molecules; cells compose tissues, which compose organs, which compose organisms; etc. Hierarchical organization is particularly prominent in ecology, a field of research explicitly arranged around levels of ecological organization. The concept of levels of organization is also central to a variety of debates in philosophy of science. Yet many difficulties plague the concept of discrete hierarchical levels. In this paper, we show how these difficulties undermine various implications ascribed to hierarchical organization, and we suggest the concept of scale as a promising alternative to levels. Investigating causal processes at different scales offers a way to retain a notion of quasi-levels that avoids the difficulties inherent in the classic concept of hierarchical levels of organization. Throughout, our focus is on ecology, but the results generalize to other invocations of hierarchy in science and philosophy of science

Managing the middle ground: forests in the transition zone between cities and remote areas

In many parts of the world there are extensive landscapes where forests and people strongly intermingle, notably in the suburbs and exurbs of cities. This landscape of transitional forest generally receives limited attention from policy makers and researchers who tend to be rooted in traditions centered on either urban planning or management of natural resources in rural areas. The transitional forest is on the periphery of both perspectives, but it is a large area that provides numerous important values (biodiversity, ecosystem function, forest products, and amenities) to the people that live in them and their neighboring cities. Here we argue for increased attention to transitional forests, identify major challenges, and suggest changes to planning and management practices needed to ensure that the values of these forests are sustained

SkySat Block 3 Launch Campaign

Planet currently manages the largest commercial fleet in the world with multiple constellations and more than two hundred satellites in active operation. The SkySats are high resolution imaging spacecraft operating in a Low Earth Orbit; their mission is to image the Earth. After the recent launch of six satellites, the SkySat Constellation is complete with a total of twenty one satellites. This ‘block’ of spacecraft launched on two separate launches; it was the most ambitious and challenging set of SkySat launches and commissioning campaigns Planet has executed. The satellites launched into an inclined and extremely low altitude and required a new concept of operations, a new ground network, and several new system and automation workflows. All of these activities needed to be performed on a tight deadline, and during a worldwide pandemic (which added a multitude of unique challenges to the assembly, integration, and test of the spacecraft). Each previous SkySat launch inserted the satellites into a sun synchronous orbit at an altitude greater than or equal to 500km; these launches inserted the satellites into an inclined orbit at an altitude of 375 km x 208 km. At this altitude, our models predicted the SkySats would re-enter the atmosphere in less than a month. The operations workflows were reimagined, improved, and further automated to support rapid commissioning and maneuvering of the satellites in order to prevent reentry. Many of the initial bus calibration activities that were previously executed prior to the first on orbit maneuver were either executed during the ground campaign or postponed until a safe altitude was reached. Instead of rushing towards a first light image on the newly launched satellites, the team now raced towards the first of many orbit raising maneuvers. The orbit raising campaign consisted of a series of conjoined altitude raising, orbit circularization, and phasing maneuvers. Each satellite’s final position was phased to a relative in-tracks pacing that maximized coverage and spatial separation between all six satellites. Special care was taken to minimize the risk of close approaches between the two orbit planes. This campaign was affected and delayed by multiple hardware failures that pushed the team to implement new procedures to resume operations

Pothole Reporting System

The purpose of this project is to create a pothole detection device that can be attached to the underside of a commercial vehicle. Potholes cost motorists around 6.4 billion dollars annually, thus demonstrating the need for a system to aid with the detection and reporting of potholes. The four systems we needed to consider for the implementation of this project were the power system, the sensing system, the data processing system, and the reporting and logging system. Power pulled from the vehicle will enable the sensors and data processing module. The data processing module will analyze the readings from the sensors and output pothole data to the logging and reporting system. The logging and reporting system, located on an android mobile device, will store the pothole locations on a cloud server