Dataset Information

Machine learning of large-scale spatial distributions of wild turkeys with high-dimensional environmental data.

ABSTRACT: Species distribution modeling often involves high-dimensional environmental data. Large amounts of data and multicollinearity among covariates impose challenges to statistical models in variable selection for reliable inferences of the effects of environmental factors on the spatial distribution of species. Few studies have evaluated and compared the performance of multiple machine learning (ML) models in handling multicollinearity. Here, we assessed the effectiveness of removal of correlated covariates and regularization to cope with multicollinearity in ML models for habitat suitability. Three machine learning algorithms maximum entropy (MaxEnt), random forests (RFs), and support vector machines (SVMs) were applied to the original data (OD) of 27 landscape variables, reduced data (RD) with 14 highly correlated covariates being removed, and 15 principal components (PC) of the OD accounting for 90% of the original variability. The performance of the three ML models was measured with the area under the curve and continuous Boyce index. We collected 663 nonduplicated presence locations of Eastern wild turkeys (Meleagris gallopavo silvestris) across the state of Mississippi, United States. Of the total locations, 453 locations separated by a distance of ?2 km were used to train the three ML algorithms on the OD, RD, and PC data, respectively. The remaining 210 locations were used to validate the trained ML models to measure ML performance. Three ML models had excellent performance on the RD and PC data. MaxEnt and SVMs had good performance on the OD data, indicating the adequacy of regularization of the default setting for multicollinearity. Weak learning of RFs through bagging appeared to alleviate multicollinearity and resulted in excellent performance on the OD data. Regularization of ML algorithms may help exploratory studies of the effects of environmental factors on the spatial distribution and habitat suitability of wildlife.

SUBMITTER: Farrell A

PROVIDER: S-EPMC6540709 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Machine learning of large-scale spatial distributions of wild turkeys with high-dimensional environmental data.

Farrell Annie A Wang Guiming G Rush Scott A SA Martin James A JA Belant Jerrold L JL Butler Adam B AB Godwin Dave D

Ecology and evolution 20190424 10

Species distribution modeling often involves high-dimensional environmental data. Large amounts of data and multicollinearity among covariates impose challenges to statistical models in variable selection for reliable inferences of the effects of environmental factors on the spatial distribution of species. Few studies have evaluated and compared the performance of multiple machine learning (ML) models in handling multicollinearity. Here, we assessed the effectiveness of removal of correlated co ...[more]

PMID: 31161010

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Project description:Wild turkeys (Meleagris gallopavo; hereafter turkeys) are an important game animal whose popularity among hunters has increased in recent decades. Yet, the number of hunters pursuing turkeys appears to be in flux, patterns of hunter abundance have primarily been described at broad spatial scales, and the ability of management to impact hunter numbers in the post-restoration era of management through opportunity for quality hunting is unclear. We used county-scale estimates of turkey hunter numbers collected over a 14-year period (2001-2014) and time-series analyses to evaluate the spatial scales at which spring and fall turkey hunter populations fluctuate, and also used generalized linear mixed models to evaluate whether attributes related to quality turkey hunting explain recent patterns in hunter abundance. We found heterogeneity in turkey hunter population growth at finer spatial scales than has been previously described (i.e., counties and management units), and provide evidence for spatial structuring of hunter population dynamics among counties that did not always correspond with existing management units. Specifically, the directionality of hunter population change displays spatial structure along an east-west gradient in southern Michigan. We also found little evidence that factors providing opportunity for quality turkey hunting had meaningful impacts on recent spatial-temporal patterns of hunter numbers. Our results imply that providing quality turkey hunting opportunities alone may be insufficient for sustaining populations of turkey hunters in the future, and that modern determinants of hunter participation extend beyond the availability of abundant turkey populations. Moreover, our results demonstrate that interpretation of harvest data as indices of abundance for turkey populations is difficult in the absence of hunter effort data, as changes to turkey harvest are a function of potentially fine-scaled changes in populations of hunters, not simply changes to turkey populations.

Dataset Information

Machine learning of large-scale spatial distributions of wild turkeys with high-dimensional environmental data.

Publications

Machine learning of large-scale spatial distributions of wild turkeys with high-dimensional environmental data.

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