The role of artificial light at night and road density in predicting the seasonal occurrence of nocturnally migrating birds

The Leon Levy Foundation; The Wolf Creek Charitable Foundation; Lyda Hill Philanthropies; Amon G. Carter Foundation; National Science Foundation, Grant/Award Number: ABI sustaining DBI-1939187 and ICER-1927743. Computing support was provided by the National Science Foundation, Grant/Award Number: CNS-1059284 and CCF-1522054, and the Extreme Science and Engineering Discovery Environment (XSEDE), National Science Foundation, Grant/Award Number: ACI-1548562, through allocation TG-DEB200010 run on Bridges at the Pittsburgh Supercomputing Center.Aim: Artificial light at night (ALAN) and roads are known threats to nocturnally migrating birds. How associations with ALAN and roads are defined in combination for these species at the population level across the full annual cycle has not been explored. Location: Western Hemisphere. Methods: We estimated range‐wide exposure, predictor importance and the prevalence of positive associations with ALAN and roads at a weekly temporal resolution for 166 nocturnally migrating bird species in three orders: Passeriformes (n = 104), Anseriformes (n = 27) and Charadriiformes (n = 35). We clustered Passeriformes based on the prevalence of positive associations. Results: Positive associations with ALAN and roads were more prevalent for Passeriformes during migration when exposure and importance were highest. Positive associations with ALAN and roads were more prevalent for Anseriformes and Charadriiformes during the breeding season when exposure was lowest. Importance was uniform for Anseriformes and highest during migration for Charadriiformes. Our cluster analysis identified three groups of Passeriformes, each having similar associations with ALAN and roads. The first occurred in eastern North America during migration where exposure, prevalence, and importance were highest. The second wintered in Mexico and Central America where exposure, prevalence and importance were highest. The third occurred throughout North America where prevalence was low, and exposure and importance were uniform. The first and second were comprised of dense habitat specialists and long‐distance migrants. The third was comprised of open habitat specialists and short distance migrants. Main conclusions: Our findings suggest ALAN and roads pose the greatest risk during migration for Passeriformes and during the breeding season for Anseriformes and Charadriiformes. Our results emphasise the close relationship between ALAN and roads, the diversity of associations dictated by taxonomy, exposure, migration strategy and habitat and the need for more informed and comprehensive mitigation strategies where ALAN and roads are treated as interconnected threats.Publisher PDFPeer reviewe

A Double Machine Learning Trend Model for Citizen Science Data

1. Citizen and community-science (CS) datasets have great potential for estimating interannual patterns of population change given the large volumes of data collected globally every year. Yet, the flexible protocols that enable many CS projects to collect large volumes of data typically lack the structure necessary to keep consistent sampling across years. This leads to interannual confounding, as changes to the observation process over time are confounded with changes in species population sizes. 2. Here we describe a novel modeling approach designed to estimate species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double Machine Learning, a statistical framework that uses machine learning methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. Additionally, we develop a simulation method to identify and adjust for residual confounding missed by the propensity scores. Using this new method, we can produce spatially detailed trend estimates from citizen science data. 3. To illustrate the approach, we estimated species trends using data from the CS project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends in the face of real-world confounding. Results showed that the trend estimates distinguished between spatially constant and spatially varying trends at a 27km resolution. There were low error rates on the estimated direction of population change (increasing/decreasing) and high correlations on the estimated magnitude. 4. The ability to estimate spatially explicit trends while accounting for confounding in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species, regions, or seasons without rigorous monitoring data.Comment: 28 pages, 6 figure

Mapping the planet’s critical natural assets

Sustaining the organisms, ecosystems and processes that underpin human wellbeing is necessary to achieve sustainable development. Here we define critical natural assets as the natural and semi-natural ecosystems that provide 90% of the total current magnitude of 14 types of nature’s contributions to people (NCP), and we map the global locations of these critical natural assets at 2 km resolution. Critical natural assets for maintaining local-scale NCP (12 of the 14 NCP) account for 30% of total global land area and 24% of national territorial waters, while 44% of land area is required to also maintain two global-scale NCP (carbon storage and moisture recycling). These areas overlap substantially with cultural diversity (areas containing 96% of global languages) and biodiversity (covering area requirements for 73% of birds and 66% of mammals). At least 87% of the world’s population live in the areas benefitting from critical natural assets for local-scale NCP, while only 16% live on the lands containing these assets. Many of the NCP mapped here are left out of international agreements focused on conserving species or mitigating climate change, yet this analysis shows that explicitly prioritizing critical natural assets and the NCP they provide could simultaneously advance development, climate and conservation goals.We thank all the participants of two working groups hosted by Conservation International and the Natural Capital Project for their insights and intellectual contributions. For further advice or assistance, we thank A. Adams, K. Brandon, K. Brauman, A. Cramer, G. Daily, J. Fisher, R. Gould, L. Mandle, J. Montgomery, A. Rodewald, D. Rossiter, E. Selig, A. Vogl and T. M. Wright. The two working groups that provided the foundation for this analysis were funded by support from the Marcus and Marianne Wallenberg Foundation to the Natural Capital Project (R.C.-K. and R.P.S.) and the Betty and Gordon Moore to Conservation International (R.A.N. and P.M.C.)

Accounting for long-term persistence of multiple species in systematic conservation planning

Protected areas form the cornerstone of global efforts to conserve biodiversity. The goal of systematic conservation planning is to design protected area networks that secure the long term persistence of biodiversity. However, most current methods focus on maximizing the representation of species and don’t explicitly plan for the persistence of those species in the protected landscapes into the future. In this thesis, I present a new tool for systematic reserve design that optimizes the configuration of reserve networks to maximize persistence across multiple species. This method is based on metapopulation capacity, a relative, asymptotic metric of persistence derived from a spatially explicit metapopulation model. This metric requires few parameters to calculate, and incorporates the size and spatial configuration of reserves as well as species-specific dispersal dynamics among them. I demonstrate this method using a case study in Indonesian New Guinea with 114 terrestrial mammal species. Compared to Marxan, the most popular representation-based reserve design tool, my persistence-based method led to a 2.3-times increase in mean metapopulation capacity across all species. At the level of individual species, I identified two distinct groups: those that experienced significant benefits from the persistence-based approach and those for which the Marxan solution was nearly as good or slightly better. This thesis demonstrates that systematic reserve design can account for species persistence in an ecologically meaningful way, and that this approach can yield significant gains compared to traditional methods.Science, Faculty ofZoology, Department ofGraduat

Latent Structure in Linear Prediction and Corpora Comparison

202 pagesThis work first studies the finite-sample properties of the risk of the minimum-norm interpolating predictor in high-dimensional regression models. If the effective rank of the covariance matrix of the p regression features is much larger than the sample size n, we show that the min-norm interpolating predictor is not desirable, as its risk approaches the risk of trivially predicting the response by 0. However, our detailed finite-sample analysis reveals, surprisingly, that this behavior is not present when the regression response and the features are jointly low-dimensional, following a widely used factor regression model. Within this popular model class, and when the effective rank of the covariance matrix is smaller than n, while still allowing for p >> n, both the bias and the variance terms of the excess risk can be controlled, and the risk of the minimum-norm interpolating predictor approaches optimal benchmarks. Moreover, through a detailed analysis of the bias term, we exhibit model classes under which our upper bound on the excess risk approaches zero, while the corresponding upper bound in the recent work [arXiv:1906.11300] diverges. Furthermore, we show that the minimum-norm interpolating predictor analyzed under the factor regression model, despite being model-agnostic and devoid of tuning parameters, can have similar risk to predictors based on principal components regression and ridge regression, and can improve over LASSO based predictors, in the high-dimensional regime. The second part of this work extends the analysis of the minimum-norm interpolating predictor to a larger class of linear predictors of a real-valued response Y. Our primary contribution is in establishing finite sample risk bounds for prediction with the ubiquitous Principal Component Regression (PCR) method, under the factor regression model, with the number of principal components adaptively selected from the data---a form of theoretical guarantee that is surprisingly lacking from the PCR literature. To accomplish this, we prove a master theorem that establishes a risk bound for a large class of predictors, including the PCR predictor as a special case. This approach has the benefit of providing a unified framework for the analysis of a wide range of linear prediction methods, under the factor regression setting. In particular, we use our main theorem to recover the risk bounds for the minimum-norm interpolating predictor, and a prediction method tailored to a subclass of factor regression models with identifiable parameters. This model-tailored method can be interpreted as prediction via clusters with latent centers. To address the problem of selecting among a set of candidate predictors, we analyze a simple model selection procedure based on data-splitting, providing an oracle inequality under the factor model to prove that the performance of the selected predictor is close to the optimal candidate. In the third part of this work, we shift from the latent factor model to developing methodology in the context of topic models, which also rely on latent structure. We provide a new, principled, construction of a distance between two ensembles of independent, but not identically distributed, discrete samples, when each ensemble follows a topic model. Our proposal is a hierarchical Wasserstein distance, that can be used for the comparison of corpora of documents, or any other data sets following topic models. We define the distance by representing a corpus as a discrete measure theta over a set of clusters corresponding to topics. To a cluster we associate its center, which is itself a discrete measure over topics. This allows for summarizing both the relative weight of each topic in the corpus (represented by the components of theta) and the topic heterogeneity within the corpus in a single probabilistic representation. The distance between two corpora then follows naturally as a hierarchical Wasserstein distance between the probabilistic representations of the two corpora. We demonstrate that this distance captures differences in the content of the topics between two corpora and their relative coverage. We provide computationally tractable estimates of the distance, as well as accompanying finite sample error bounds relative to their population counterparts. We demonstrate the usage of the distance with an application to the comparison of news sources.2023-09-0

Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems

Repository accompanying this pre-print: https://www.biorxiv.org/content/10.1101/847632v1 Abstract: The resources available for conserving biodiversity are limited, and so protected areas need to be established in places that will achieve objectives for minimal cost. Two of the main algorithms for solving systematic conservation planning problems are Simulated Annealing (SA) and exact integer linear programming (EILP) solvers. Using a case study in British Columbia, Canada, we compare the cost-effectiveness and processing times of SA used in Marxan versus EILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on EILP algorithms were 12 to 30% cheaper than plans of Marxan using SA, due to EILP’s ability to find optimal solutions as opposed to approximations. The best EILP solver we examined was on average 1071 times faster than the Marxan SA algorithm tested. The performance advantages of EILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using EILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of EILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process