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J., +English, P. A., & Barnett, L. A. K. (2022). sdmTMB: An R package for +fast, flexible, and user-friendly generalized linear mixed effects +models with spatial and spatiotemporal random fields [Preprint]. +Ecology. +https://doi.org/10.1101/2022.03.24.485545 + + + dynamicSDM: An r package for species +geographical distribution and abundance modelling at high spatiotemporal +resolution + Dobson + Methods in Ecology and +Evolution + 5 + 14 + 10.1111/2041-210X.14101 + 2041-210X + 2023 + Dobson, R., Challinor, A. J., Cheke, +R. A., Jennings, S., Willis, S. G., & Dallimer, M. (2023). +dynamicSDM: An r package for species geographical distribution and +abundance modelling at high spatiotemporal resolution. Methods in +Ecology and Evolution, 14(5), 1190–1199. +https://doi.org/10.1111/2041-210X.14101 + + + Species distribution models rarely predict +the biology of real populations + A. Lee‐Yaw + Ecography + 6 + 2022 + 10.1111/ecog.05877 + 0906-7590 + 2022 + A. Lee‐Yaw, J., L. McCune, J., +Pironon, S., & N. Sheth, S. (2022). Species distribution models +rarely predict the biology of real populations. Ecography, 2022(6), +e05877. https://doi.org/10.1111/ecog.05877 + + + + + + From efdeb78cfbedc7ecce93abe2c14eca72083c5759 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Fri, 23 Feb 2024 15:39:57 +0000 Subject: [PATCH 3/3] Creating 10.21105.joss.06158.jats --- joss.06158/10.21105.joss.06158.jats | 894 ++++++++++++++++++++++++++++ 1 file changed, 894 insertions(+) create mode 100644 joss.06158/10.21105.joss.06158.jats diff --git a/joss.06158/10.21105.joss.06158.jats b/joss.06158/10.21105.joss.06158.jats new file mode 100644 index 0000000000..02274358b3 --- /dev/null +++ b/joss.06158/10.21105.joss.06158.jats @@ -0,0 +1,894 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6158 +10.21105/joss.06158 + +stemflow: A Python Package for Adaptive Spatio-Temporal +Exploratory Model + + + + +Chen +Yangkang + + + + + + +Gu +Zhongru + + + + + + +Zhan +Xiangjiang + + + + + + + + +Key Laboratory of Animal Ecology and Conservation Biology, +Institute of Zoology, Chinese Academy of Sciences, Beijing, +China + + + + +University of Chinese Academy of Sciences, Beijing, +China + + + + +Cardiff University-Institute of Zoology Joint Laboratory +for Biocomplexity Research, Chinese Academy of Sciences, Beijing, +China + + + + +Center for Excellence in Animal Evolution and Genetics, +Chinese Academy of Sciences, Kunming, China + + + + +20 +9 +2023 + +9 +94 +6158 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +ecology +spatio-temporal big data +citizen science +modeling +seasonal migration + + + + + + Summary +

Stemflow is a user-friendly Python package for Adaptive + Spatio-Temporal Exploratory Model (AdaSTEM + (Fink + et al., 2013)). AdaSTEM is a modeling framework that adopts + “split-apply-combine” methodology + (Wickham, + 2011) – it adaptively splits data into spatiotemporal grids, + train models for each grid, and combines the models for ensemble + prediction. Models in stemflow follow the style of scikit-learn + BaseEstimator class + (Pedregosa + et al., 2011). It provides one-line model creation, fitting, + prediction, and evaluation. It implements spatio-temporal + train-test-split and cross-validation functions. After model training, + feature importance could be evaluated with spatio-temporal dynamics. + Stemflow also provides functions for visualizing ensembles structured + in model training and generating GIF file for predicted results to + animate the spatio-temporal movement of animal population.

+
+ + Statement of need +

Spatio-temporal big data is an increasingly valuable resource for + modern ecological studies + (Farley + et al., 2018). A large amount of spatio-temporal big data is + now derived from broad-scale surveys, such as citizen science projects + (Dickinson + et al., 2010). The intensity of survey activities grows rapidly + as more people are involved in citizen science in recent years, + resulted in exponential accumulation of observational data + (Di + Cecco et al., 2021; + Sullivan + et al., 2014). However, daily species observation records + uploaded by non-professionals in citizen science program are known to + have larger bias than professionally structured research, both in + terms of data veracity and spatio-temporal balance of the datasets, + which necessitates elaborate modeling methods to provide insights + (Dickinson + et al., 2010; + Farley + et al., 2018).

+

Some species distribution modeling (SDM) approaches were brought + forward to adjust for bias in citizen science and model on the + unobserved components + (Bird + et al., 2014). Still, many failed to account for the + autocorrelation of space and time + (F. + Dormann et al., 2007), which is especially crucial in modeling + inherently spatio-temporal biological events with variations at + different scales + (Chave, + 2013; + Levin, + 1992), such as seasonal migration. Adaptive Spatio-Temporal + Exploratory Model (AdaSTEM) is a semi-parameterized machine learning + model that leverages the spatio-temporal adjacency information of + sample points to model occurrence or abundance of species + (Fink + et al., 2013). A QuadTree algorithm + (Samet, + 1984) is implemented to split data into smaller spatio-temporal + grids (called stixels) conditional on the data abundance, with more + abundant data allowing stixels to be divided into finer resolution (up + to a maximum). Stixels with sample size less than a certain threshold + will not be modeled; instead, these stixels will be labeled as + unpredictable. This procedure controls the degree of model + extrapolation (known as “long-distance prediction” problem in spatial + settings) and reduces overfitting. A base model is trained for each + stixel, that is, targets are only modeled on their adjacent + information in space and time. Splitting-training is carried out + several times to generate multiple ensembles. Finally, prediction + results were aggregated across these ensembles.

+

AdaSTEM shows the capacity of supporting large scale + spatio-temporal ecological data modeling in many studies + (Fink + et al., 2020; + Fuentes + et al., 2023; + La + Sorte et al., 2022), especially for modeling animal abundance + at different scales + (Fink + et al., 2013). One well-known application of AdaSTEM is the + weekly abundance map of eBird Status and Trend product + (Fink + et al., 2022), which was widely used as data sources of + abundance data of bird populations + (Bird + et al., 2014; + Jarzyna + & Stagge, 2023; + Lin + et al., 2022). The application of AdaSTEM could be extended to + other fields with similar data structure and spatio-temporal + dependence, for example, epidemiology. Despite the foreseeable + significant role of spatio-temporal big data in the coming decades of + scientific research, the development of tools has not necessarily kept + pace.

+

Stemflow is positioned as a user-friendly Python package to meet + the need of general application of modeling spatio-temporal large + datasets. Scikit-learn style object-oriented modeling pipeline enables + concise model construction with compact parameterization at the user + end, while the rest of the modeling procedures are carried out under + the hood. Once the fitting method is called, the model class + recursively splits the input training data into smaller + spatio-temporal stixels using QuadTree algorithm. For each of the + stixels, a base model is trained only using data falls into that + stixel. Stixels are then aggregated and constitute an ensemble. In the + prediction phase, stemflow queries stixels for the input data + according to their spatial and temporal index, followed by + corresponding base model prediction. Finally, prediction results are + aggregated across ensembles to generate robust estimations (see Fink + et al. + (2013) + and stemflow documentation for details).

+

For survey projects that include abundance information like eBird + (Sullivan + et al., 2014), the targeted modeling values are often + zero-inflated, owning to the fact of low observation probability in + many species. Zero-inflation could lead to poor regression model + performance + (Campbell, + 2021). In stemflow, we implement hurdle model classes that + embed two sequential models: a classifier to classify the absence and + presence states, followed by a regressor to model the abundance for + prediction samples classified as presence. Hurdle model classes can be + conjunctively used with AdaSTEM model classes in two ways: Use hurdle + model as the base model for AdaSTEMRegressor (as in Johnston et al. + (2015)), + or use AdaSTEMClassifier and AdaSTEMRegressor as the classifier and + regressor in hurdle model. We demonstrate the comparison of these two + architectures in stemflow documentation.

+

One advantage of applying stemflow in scikit-learn style is that + there is a variety of “base models” to choose from scikit-learn or + scikit-learn-style repertoire. The choices vary from linear models to + boosting and bagging tree-based models. Maxent model + (C. + B. Anderson, 2023) is also supported to play the role of “base + model”, which largely expands the potential application for + presence-only modeling (see documentation).

+

While there exists many open source packages for species + distribution modeling (mostly in R, + (Norberg + et al., 2019); and currently one in Python + (C. + B. Anderson, 2023)), most of them solely leverage environmental + variables and do not support integration of spatio-temporal + information during model construction (but see C. B. Anderson + (2023); + S. C. Anderson et al. + (2022); + Dobson et al. + (2023)). + This disadvantage is usually noted along with the overconfidence of + the model extrapolation capacity both for Maxent-based and + ensemble-based models + (A. + Lee‐Yaw et al., 2022). To our knowledge, stemflow is the first + SDM package specifically crafted to address spatio-temporal + dependencies in samples while also accounting for biases in sample + distribution. With the rapid accumulation of data and development of + machine learning techniques, stemflow will exhibit greater advantages + in spatio-temporal modeling, and could be applied to other fields + (e.g., epidemiology and weather prediction) in future.

+
+ + Acknowledgements +

This project was based upon work supported by National Natural + Science Foundation of China grants 32125005 (to X.Z.), 32270455 (to + Z.G.), CAS Project for Young Scientists in Basic Research (YSBR-097 to + X.Z. and Z.G.). We thank Dr. Daniel Fink at Cornell Lab of Ornithology + for the R scripts of STEM model and QuadTree which inspired our + implementation in Python. We thank Dr. Taxing Zhang for advice and + software test.

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