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1. Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes

   https://doi.org/10.1007/s11336-020-09743-0  

   Ulitzsch, Esther; He, Qiwei; Ulitzsch, Vincent; Molter, Hendrik; Nichterlein, André; Niedermeier, Rolf; Pohl, Steffi (March 2021, Psychometrika)

   Abstract Complex interactive test items are becoming more widely used in assessments. Being computer-administered, assessments using interactive items allow logging time-stamped action sequences. These sequences pose a rich source of information that may facilitate investigating how examinees approach an item and arrive at their given response. There is a rich body of research leveraging action sequence data for investigating examinees’ behavior. However, the associated timing data have been considered mainly on the item-level, if at all. Considering timing data on the action-level in addition to action sequences, however, has vast potential to support a more fine-grained assessment of examinees’ behavior. We provide an approach that jointly considers action sequences and action-level times for identifying common response processes. In doing so, we integrate tools from clickstream analyses and graph-modeled data clustering with psychometrics. In our approach, we (a) provide similarity measures that are based on both actions and the associated action-level timing data and (b) subsequently employ cluster edge deletion for identifying homogeneous, interpretable, well-separated groups of action patterns, each describing a common response process. Guidelines on how to apply the approach are provided. The approach and its utility are illustrated on a complex problem-solving item from PIAAC 2012. 

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2. A Bayesian nonparametric approach for handling item and examinee heterogeneity in assessment data

   https://doi.org/10.1111/bmsp.12322  

   Pan, Tianyu; Shen, Weining; Davis‐Stober, Clintin P.; Hu, Guanyu (February 2024, British Journal of Mathematical and Statistical Psychology)

   Abstract We propose a novel nonparametric Bayesian item response theory model that estimates clusters at the question level, while simultaneously allowing for heterogeneity at the examinee level under each question cluster, characterized by a mixture of binomial distributions. The main contribution of this work is threefold. First, we present our new model and demonstrate that it is identifiable under a set of conditions. Second, we show that our model can correctly identify question‐level clusters asymptotically, and the parameters of interest that measure the proficiency of examinees in solving certain questions can be estimated at a rate (up to a log term). Third, we present a tractable sampling algorithm to obtain valid posterior samples from our proposed model. Compared to the existing methods, our model manages to reveal the multi‐dimensionality of the examinees' proficiency level in handling different types of questions parsimoniously by imposing a nested clustering structure. The proposed model is evaluated via a series of simulations as well as apply it to an English proficiency assessment data set. This data analysis example nicely illustrates how our model can be used by test makers to distinguish different types of students and aid in the design of future tests. 

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3. Studying collective animal behaviour with drones and computer vision

   https://doi.org/10.1111/2041-210X.70128  

   Kline, Jenna; Afridi, Saadia; Rolland, Edouard_G_A; Maalouf, Guy; Laporte‐Devylder, Lucie; Stewart, Christopher; Crofoot, Margaret; Stewart, Charles_V; Rubenstein, Daniel_I; Berger‐Wolf, Tanya (August 2025, Methods in Ecology and Evolution)

   Abstract Drones are increasingly popular for collecting behaviour data of group‐living animals, offering inexpensive and minimally disruptive observation methods. Imagery collected by drones can be rapidly analysed using computer vision techniques to extract information, including behaviour classification, habitat analysis and identification of individual animals. While computer vision techniques can rapidly analyse drone‐collected data, the success of these analyses often depends on careful mission planning that considers downstream computational requirements—a critical factor frequently overlooked in current studies.We present a comprehensive summary of research in the growing AI‐driven animal ecology (ADAE) field, which integrates data collection with automated computational analysis focused on aerial imagery for collective animal behaviour studies. We systematically analyse current methodologies, technical challenges and emerging solutions in this field, from drone mission planning to behavioural inference. We illustrate computer vision pipelines that infer behaviour from drone imagery and present the computer vision tasks used for each step. We map specific computational tasks to their ecological applications, providing a framework for future research design.Our analysis reveals AI‐driven animal ecology studies for collective animal behaviour using drone imagery focus on detection and classification computer vision tasks. While convolutional neural networks (CNNs) remain dominant for detection and classification tasks, newer architectures like transformer‐based models and specialized video analysis networks (e.g. X3D, I3D, SlowFast) designed for temporal pattern recognition are gaining traction for pose estimation and behaviour inference. However, reported model accuracy varies widely by computer vision task, species, habitats and evaluation metrics, complicating meaningful comparisons between studies.Based on current trends, we conclude semi‐autonomous drone missions will be increasingly used to study collective animal behaviour. While manual drone operation remains prevalent, autonomous drone manoeuvrers, powered by edge AI, can scale and standardise collective animal behavioural studies while reducing the risk of disturbance and improving data quality. We propose guidelines for AI‐driven animal ecology drone studies adaptable to various computer vision tasks, species and habitats. This approach aims to collect high‐quality behaviour data while minimising disruption to the ecosystem. 

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4. Challenges and opportunities for using natural history collections to estimate insect population trends

   https://doi.org/10.1111/1365-2656.13763  

   Davis, Courtney L.; Guralnick, Robert P.; Zipkin, Elise F. (June 2022, Journal of Animal Ecology)

   Abstract Natural history collections (NHC) provide a wealth of information that can be used to understand the impacts of global change on biodiversity. As such, there is growing interest in using NHC data to estimate changes in species' distributions and abundance trends over historic time horizons when contemporary survey data are limited or unavailable.However, museum specimens were not collected with the purpose of estimating population trends and thus can exhibit spatiotemporal and collector‐specific biases that can impose severe limitations to using NHC data for evaluating population trajectories.Here we review the challenges associated with using museum records to track long‐term insect population trends, including spatiotemporal biases in sampling effort and sparse temporal coverage within and across years. We highlight recent methodological advancements that aim to overcome these challenges and discuss emerging research opportunities.Specifically, we examine the potential of integrating museum records and other contemporary data sources (e.g. collected via structured, designed surveys and opportunistic citizen science programs) in a unified analytical framework that accounts for the sampling biases associated with each data source. The emerging field of integrated modelling provides a promising framework for leveraging the wealth of collections data to accurately estimate long‐term trends of insect populations and identify cases where that is not possible using existing data sources. 

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5. Proximate determinants of Taylor's law slopes

   https://doi.org/10.1111/1365-2656.12931  

   Zhao, Lei; Sheppard, Lawrence W.; Reid, Philip C.; Walter, Jonathan A.; Reuman, Daniel C.; Kuparinen, ed., Anna (January 2019, Journal of Animal Ecology)

   Abstract Taylor's law (TL), a commonly observed and applied pattern in ecology, describes variances of population densities as related to mean densities via log(variance) = log(a) + b*log(mean). Variations among datasets in the slope,b, have been associated with multiple factors of central importance in ecology, including strength of competitive interactions and demographic rates. But these associations are not transparent, and the relative importance of these and other factors for TL slope variation is poorly studied. TL is thus a ubiquitously used indicator in ecology, the understanding of which is still opaque.The goal of this study was to provide tools to help fill this gap in understanding by providingproximate determinants of TL slopes, statistical quantities that are correlated to TL slopes but are simpler than the slope itself and are more readily linked to ecological factors.Using numeric simulations and 82 multi‐decadal population datasets, we here propose, test and apply two proximate statistical determinants of TL slopes which we argue can become key tools for understanding the nature and ecological causes of TL slope variation.We find that measures based on population skewness, coefficient of variation and synchrony are effective proximate determinants. We demonstrate their potential for application by using them to help explain covariation in slopes of spatial and temporal TL (two common types of TL).This study provides tools for understanding TL, and demonstrates their usefulness. 

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