Citizen science biodiversity data present great opportunities for ecology and conservation across vast spatial and temporal scales. However, the opportunistic nature of these data lacks the sampling structure required by modeling methodologies that address a pervasive challenge in ecological data collection: imperfect detection, i.e., the likelihood of under-observing species on field surveys. Occupancy modeling is an example of an approach that accounts for imperfect detection by explicitly modeling the observation process separately from the biological process of habitat selection. This produces species distribution models that speak to the pattern of the species on a landscape after accounting for imperfect detection in the data, rather than the pattern of species observations corrupted by errors. To achieve this benefit, occupancy models require multiple surveys of a site across which the site's status (i.e., occupied or not) is assumed constant. Since citizen science data are not collected under the required repeated-visit protocol, observations may be grouped into sites post hoc. Existing approaches for constructing sites discard some observations and/or consider only geographic distance and not environmental similarity. In this study, we compare ten approaches for site construction in terms of their impact on downstream species distribution models for 31 bird species in Oregon, using observations recorded in the eBird database. We find that occupancy models built on sites constructed by spatial clustering algorithms perform better than existing alternatives.

Deep learning models commonly benefit from data augmentation techniques to diversify the set of training images. When working with satellite imagery, it is common for practitioners to apply a limited set of transformations developed for natural images (e.g., flip and rotate) to expand the training set without overly modifying the satellite images. There are many techniques for natural image data augmentation, but given the differences between the two domains, it is not clear whether data augmentation methods developed for natural images are well suited for satellite imagery. This paper presents an extensive experimental study on three classification and three regression tasks over four satellite image datasets. We compare common computer vision data augmentation techniques and propose three novel satellite-specific data augmentation strategies. Across tasks and datasets, we find that geometric transformations are beneficial for satellite imagery while color transformations generally are not. Additionally, our novel Sat-SlideMix, Sat-CutMix, and Sat-Trivial methods all exhibit strong performance across all tasks and datasets.

Under-counted matrix completion (UC-MC) has many important applications, especially in epidemiology and ecology where the observed data are often smaller than the actual numbers. Existing works model the under-counting effects using entry-wise miss detection probabilities, which are usually formulated as functions of detection-related side information or features (e.g., weather conditions for observing a certain species). However, such features are not always available. This work proposes a model for UC-MC that circumvents using such side information. By assuming that the detection probabilities for a large proportion of entries are similar, the under-counting probabilities are approximated by a specially structured (i.e., rank-one plus sparse) matrix. This way, a structure-regularized UC-MC formulation is attained, and no detection-related side information is used. A re-parameterization-based implementation is proposed, allowing us to employ off-the-shelf gradient-based optimizers to tackle the loss function of interest. Simulations are used to demonstrate the effectiveness of the proposed approach.

Systematic under-counting effects are observed in data collected across many disciplines, e.g., epidemiology and ecology. Under-counted tensor completion (UC-TC) is well-motivated for many data analytics tasks, e.g., inferring the case numbers of infectious diseases at unobserved locations from under-counted case numbers in neighboring regions. However, existing methods for similar problems often lack supports in theory, making it hard to understand the underlying principles and conditions beyond empirical successes. In this work, a low-rank Poisson tensor model with an expressive unknown nonlinear side information extractor is proposed for under-counted multi-aspect data. A joint low-rank tensor completion and neural network learning algorithm is designed to recover the model. Moreover, the UC-TC formulation is supported by theoretical analysis showing that the fully counted entries of the tensor and each entry's under-counting probability can be provably recovered from partial observations---under reasonable conditions. To our best knowledge, the result is the first to offer theoretical supports for under-counted multi-aspect data completion. Simulations and real-data experiments corroborate the theoretical claims.

This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM). In SDM, the occurrence pattern of a species on a landscape is predicted by environmental features based on observations at a set of locations. At first, SDM may appear to be a binary classification problem, and one might be inclined to employ classic tools (e.g., logistic regression, support vector machines, neural networks) to tackle it. However, wildlife surveys introduce structured noise (especially under-counting) in the species observations. If unaccounted for, these observation errors systematically bias SDMs. To address the unique challenges of SDM, this paper proposes a framework called StatEcoNet. Specifically, this work employs a graphical generative model in statistical ecology to serve as the skeleton of the proposed computational framework and carefully integrates neural networks under the framework. The advantages of StatEcoNet over related approaches are demonstrated on simulated datasets as well as bird species data. Since SDMs are critical tools for ecological science and natural resource management, StatEcoNet may offer boosted computational and analytical powers to a wide range of applications that have significant social impacts, e.g., the study and conservation of threatened species.

Matrix completion based collaborative filtering is considered scalable and effective for online service link prediction (e.g., movie recommendation) but does not meet the challenges of link prediction in ecological networks. A unique challenge of ecological networks is that the observed data are subject to systematic imperfect detection, due to the difficulty of accurate field sampling. In this work, we propose a new framework customized for ecological bipartite network link prediction. Our approach starts with incorporating the Poisson N-mixture model, a widely used framework in statistical ecology for modeling imperfect detection of a single species in field sampling. Despite its extensive use for single species analysis, this model has never been considered for link prediction between different species, perhaps because of the complex nature of both link prediction and N-mixture model inference. By judiciously combining the Poisson N-mixture model with a probabilistic nonnegative matrix factorization (NMF) model in latent space, we propose an intuitive statistical model for the problem of interest. We also offer a scalable and convergence-guaranteed optimization algorithm to handle the associated maximum likelihood identification problem. Experimental results on synthetic data and two real-world ecological networks data are employed to validate our proposed approach.

Plant-pollinator interaction networks are bipartite networks representing the mutualistic interactions between a set of plant species and a set of pollinator species. Data on these networks are collected by field biologists, who count visits from pollinators to flowers. Ecologists study the structure and function of these networks for scientific, conservation, and agricultural purposes. However, little research has been done to understand the underlying mechanisms that determine pairwise interactions or to predict new links from networks describing the species community. This paper explores the use of latent factor models to predict interactions that will occur in new contexts (e.g. a different distribution of the set of plant species) based on an observed network. The analysis draws on algorithms and evaluation strategies developed for recommendation systems and introduces them to this new domain. The matrix factorization methods compare favorably against several baselines on a pollination dataset collected in montane meadows over several years. Incorporating both positive and negative implicit feedback into the matrix factorization methods is particularly promising.

Species distribution models relate the geographic occurrence pattern of a species to environmental features and are used for a variety of scientific and management purposes. One source of data for building species distribution models is citizen science, in which volunteers report locations where they observed (or did not observe) sets of species. Since volunteers have variable levels of expertise, citizen science data may contain both false positives and false negatives in the location labels (present vs. absent) they provide, but many common modeling approaches for this task do not address these sources of noise explicitly. In this paper, we propose to formulate the species distribution modeling task as a classification problem with class-conditional noise. Our approach builds on other applications of class-conditional noise models to crowdsourced data, but we focus on leveraging features of the noise processes that are distinct from the class features. We describe the conditions under which the parameters of our proposed model are identifiable and apply it to simulated data and data from the eBird citizen science project.

Data quality is a common source of concern for large-scale citizen science projects like eBird. In the case of eBird, a major cause of poor quality data is the misidentification of bird species by inexperienced contributors. A proactive approach for improving data quality is to discover commonly misidentified bird species and to teach inexperienced birders the differences between these species. To accomplish this goal, we develop a latent variable graphical model that can identify groups of bird species that are often confused for each other by eBird participants. Our model is a multi-species extension of the classic occupancy-detection model in the ecology literature. This multi-species extension requires a structure learning step as well as a computationally expensive parameter learning stage which we make efficient through a variational approximation. We show that our model can not only discover groups of misidentified species, but by including these misidentifications in the model, it can also achieve more accurate predictions of both species occupancy and detection.

Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit models of the ecological phenomenon of interest and the process by which it is observed. However, existing latent variable methods rely on hand-formulated parametric models, which are expensive to design and require extensive preprocessing of the data. Nonparametric methods (such as regression trees) automate these decisions and produce highly accurate models. However, existing tree methods learn direct mappings from inputs to outputs — they cannot be applied to latent variable models. This paper describes a methodology for integrating nonparametric tree methods into probabilistic latent variable models by extending functional gradient boosting. The approach is presented in the context of occupancy-detection (OD) modeling, where the goal is to model the distribution of a species from imperfect detections. Experiments on 12 real and 3 synthetic bird species compare standard and tree-boosted OD models (latent variable models) with standard and tree-boosted logistic regression models (without latent structure). All methods perform similarly when predicting the observed variables, but the OD models learn better representations of the latent process. Most importantly, tree-boosted OD models learn the best latent representations when nonlinearities and interactions are present.

Citizen scientists, who are volunteers from the community that participate as field assistants in scientific studies, enable research to be performed at much larger spatial and temporal scales than trained scientists can cover. Species distribution modeling, which involves understanding species-habitat relationships, is a research area that can benefit greatly from citizen science. The eBird project is one of the largest citizen science programs in existence. By allowing birders to upload observations of bird species to an online database, eBird can provide useful data for species distribution modeling. However, since birders vary in their levels of expertise, the quality of data submitted to eBird is often questioned. In this paper, we develop a probabilistic model called the Occupancy-Detection-Expertise (ODE) model that incorporates the expertise of birders submitting data to eBird. We show that modeling the expertise of birders can improve the accuracy of predicting observations of a bird species at a site. In addition, we can use the ODE model for two other tasks: predicting birder expertise given their history of eBird checklists and identifying bird species that are difficult for novices to detect.

We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling hidden cognitive processes in the brain, given functional Magnetic Resonance Imaging (fMRI) data. fMRI data is sparse, high-dimensional, non-Markovian, and often involves prior knowledge of the form "hidden event A occurs n times within the interval [t,t′]." HPMs provide a generalization of the widely used General Linear Model approaches to fMRI analysis, and HPMs can also be viewed as a subclass of Dynamic Bayes Networks.