Machine learning applied to lentic habitat use by spawning walleye demonstrates the benefits of considering multiple spatial scales in aquatic research

2022 ◽  
Author(s):  
Douglas L Zentner ◽  
Joshua K Raabe ◽  
Timothy K Cross ◽  
Peter C Jacobson
Keyword(s):  
Machine Learning ◽  
Spatial Scales ◽  
Classification Tree ◽  
Aquatic Systems ◽  
Multi Scale ◽  
Predictive Variables ◽  
Egg Deposition ◽  
Random Forest Models ◽  
Minnesota Lakes ◽  
Scale Optimization

Scale and hierarchy have received less attention in aquatic systems compared to terrestrial. Walleye Sander vitreus spawning habitat offers an opportunity to investigate scale’s importance. We estimated lake-, transect-, and quadrat-scale influences on nearshore walleye egg deposition in 28 Minnesota lakes from 2016-2018. Random forest models (RFM) estimated importance of predictive variables to walleye egg deposition. Predictive accuracies of a multi-scale classification tree (CT) and a quadrat-scale CT were compared. RFM results suggested that five of our variables were unimportant when predicting egg deposition. The multi-scale CT was more accurate than the quadrat-scale CT when predicting egg deposition. Both model results suggest that in-lake egg deposition by walleye is regulated by hierarchical abiotic processes and that silt/clay abundance at the transect-scale (reef-scale) is more important than abundance at the quadrat-scale (within-reef). Our results show machine learning can be used for scale-optimization and potentially to determine cross-scale interactions. Further incorporation of scale and hierarchy into studies of aquatic systems will increase our understanding of species-habitat relationships, especially in lentic systems where multi-scale approaches are rarely used.

scholarly journals Machine Learning Prediction of Progression in FEV1 in the COPDGene Study

2022 ◽  
Author(s):  
Adel Boueiz ◽  
Zhonghui Xu ◽  
Yale Chang ◽  
Aria Masoomi ◽  
Andrew Gregory ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Progression ◽  
Model Performance ◽  
Roc Curves ◽  
Imaging Features ◽  
Predictive Variables ◽  
Increased Risk ◽  
Random Forest Models ◽  
Advanced Stages

Background: The heterogeneous nature of COPD complicates the identification of the predictors of disease progression and consequently the development of effective therapies. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features. Methods: We included 4,496 smokers with available data from their enrollment and 5-year follow-up visits in the Genetic Epidemiology of COPD (COPDGene) study. We constructed supervised random forest models to predict 5-year progression in FEV1 from 46 baseline demographic, clinical, physiologic, and imaging features. Using cross-validation, we randomly partitioned participants into training and testing samples. We also validated the results in the COPDGene 10-year follow-up visit. Results: Predicting the change in FEV1 over time is more challenging than simply predicting the future absolute FEV1 level. Nevertheless, the area under the ROC curves for the prediction of subjects in the top quartile of observed disease progression was 0.70 in the 10-year follow-up data. The model performance accuracy was best for GOLD1-2 subjects and it was harder to achieve accurate prediction in advanced stages of the disease. Predictive variables differed in their relative importance as well as for the predictions by GOLD grade. Conclusion: This state-of-the art approach along with deep phenotyping predicts FEV1 progression with reasonable accuracy. There is significant room for improvement in future models. This prediction model facilitates the identification of smokers at increased risk for rapid disease progression. Such findings may be useful in the selection of patient populations for targeted clinical trials.

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

scholarly journals Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Ashima Malik ◽  
Megha Rajam Rao ◽  
Nandini Puppala ◽  
Prathusha Koouri ◽  
Venkata Anil Kumar Thota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Curves ◽  
Data Driven ◽  
Northern California ◽  
Combined Model ◽  
Wildfire Risk ◽  
Study Results ◽  
Forest Models ◽  
Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

scholarly journals Complex community responses underpin biodiversity change following invasion

2021 ◽  
Author(s):  
Alessandra R. Kortz ◽  
Anne E. Magurran
Keyword(s):  
Invasive Species ◽  
Species Richness ◽  
Spatial Scales ◽  
Species Abundance ◽  
Ecological Systems ◽  
Multi Scale ◽  
Mechanistic Approach ◽  
Α Diversity ◽  
Biodiversity Change ◽  
Insight Into

AbstractHow do invasive species change native biodiversity? One reason why this long-standing question remains challenging to answer could be because the main focus of the invasion literature has been on shifts in species richness (a measure of α-diversity). As the underlying components of community structure—intraspecific aggregation, interspecific density and the species abundance distribution (SAD)—are potentially impacted in different ways during invasion, trends in species richness provide only limited insight into the mechanisms leading to biodiversity change. In addition, these impacts can be manifested in distinct ways at different spatial scales. Here we take advantage of the new Measurement of Biodiversity (MoB) framework to reanalyse data collected in an invasion front in the Brazilian Cerrado biodiversity hotspot. We show that, by using the MoB multi-scale approach, we are able to link reductions in species richness in invaded sites to restructuring in the SAD. This restructuring takes the form of lower evenness in sites invaded by pines relative to sites without pines. Shifts in aggregation also occur. There is a clear signature of spatial scale in biodiversity change linked to the presence of an invasive species. These results demonstrate how the MoB approach can play an important role in helping invasion ecologists, field biologists and conservation managers move towards a more mechanistic approach to detecting and interpreting changes in ecological systems following invasion.

scholarly journals Predicting Increased Blood Pressure Using Machine Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hudson Fernandes Golino ◽  
Liliany Souza de Brito Amaral ◽  
Stenio Fernando Pimentel Duarte ◽  
Cristiano Mauro Assis Gomes ◽  
Telma de Jesus Soares ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Predictive Power ◽  
Classification Tree ◽  
Training Group ◽  
Test Sample ◽  
Hip Circumference ◽  
Training Set ◽  
Learning Technique ◽  
Tree Data

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudoR2(.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudoR2(.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

scholarly journals A synthesis of scale-dependent ecology of the endangered mountain caribou in British Columbia, Canada

Rangifer ◽  
2008 ◽  
Vol 28 (1) ◽  
pp. 33
Author(s):  
Robert Serrouya ◽  
Bruce N. McLellan ◽  
Clayton D. Apps ◽  
Heiko U. Wittmer
Keyword(s):  
British Columbia ◽  
Rangifer Tarandus ◽  
Spatial Scales ◽  
Management Strategies ◽  
Population Level ◽  
Limiting Factors ◽  
Conservation Strategies ◽  
Integrative Approach ◽  
Rangifer Tarandus Caribou ◽  
Multi Scale

Mountain caribou are an endangered ecotype of woodland caribou (Rangifer tarandus caribou) that live in highprecipitation, mountainous ecosystems of southeastern British Columbia and northern Idaho. The distribution and abundance of these caribou have declined dramatically from historical figures. Results from many studies have indicated that mountain caribou rely on old conifer forests for several life-history requirements including an abundance of their primary winter food, arboreal lichen, and a scarcity of other ungulates and their predators. These old forests often have high timber value, and understanding mountain caribou ecology at a variety of spatial scales is thus required to develop effective conservation strategies. Here we summarize results of studies conducted at three different spatial scales ranging from broad limiting factors at the population level to studies describing the selection of feeding sites within seasonal home ranges of individuals. The goal of this multi-scale review is to provide a more complete picture of caribou ecology and to determine possible shifts in limiting factors across scales. Our review produced two important results. First, mountain caribou select old forests and old trees at all spatial scales, signifying their importance for foraging opportunities as well as conditions required to avoid alternate ungulates and their predators. Second, relationships differ across scales. For example, landscapes dominated by roads and edges negatively affect caribou survival, but appear to attract caribou during certain times of the year. This juxtaposition of fine-scale behaviour with broad-scale vulnerability to predation could only be identified through integrated multi-scale analyses of resource selection. Consequently we suggest that effective management strategies for endangered species require an integrative approach across multiple spatial scales to avoid a focus that may be too narrow to maintain viable populations. Abstract in Norwegian / Sammendrag:Skala-avhengig økologi og truet fjellvillrein i Britisk ColumbiaFjellvillreinen i de nedbørsrike fjellområdene i sørøstre Britisk Columbia og nordlige Idaho som er en truet økotype av skogsreinen (Rangifer tarandus caribou), har blitt kraftig redusert både i utbredelse og antall. Mange studier har vist at denne økotypen er avhengig av vinterføden hengelav i gammel barskog hvor det også er få andre klovdyr og dermed få predatorer. Slik skog er også viktige hogstområder, og å forstå økologien til fjellvillreinen i forskjellige skaleringer er derfor nødvendig for å utvikle forvaltningsstrategier som kan berge og ta vare på denne reinen. Artikkelen gir en oversikt over slike arbeider: fra studier av begrensende faktorer på populasjonsnivå til studier av sesongmessige beiteplasser på individnivå. Hensikten er å få frem et mer helhetlig perspektiv på fjellvillreinen og finne hvordan de begrensende faktorene varierer etter skaleringen som er benyttet i studiet. Oversikten vår frembragte to viktige resultater; 1) Uansett skalering så velger dyrene gammel skog og gamle trær. 2) Dyrenes bruk av et område kan variere med benyttet skalering, for eksempel vil landskap utbygd med veier og hogstflater være ufordelaktig for overlevelsen, men synes likevel å kunne tiltrekke fjellvillreinen til visse tider av året. Forholdet mellom atferd ut fra fin-skalering og stor-skalering sårbarhet hva gjelder predasjon, ville kun blitt avdekket ved flere-skaleringsanalyse av hvordan ressursene benyttes. Ut fra dette foreslår vi at forvaltningsstrategier for truete bestander som eksempelvis fjellvillreinen, må baseres på tilnærminger ut fra ulike skaleringer for å hindre at et for snevert perspektiv kan begrense muligheten for vedvarende levedyktighet.

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