The influence of anthropogenic shoreline changes on the littoral abundance of fish species in German lowland lakes varying in depth as determined by boosted regression trees

AbstractBackground: Although pelagic seabirds are broadly recognised as indicators of the health of marine systems, numerous gaps exist in knowledge of their at-sea distributions at the species level. These gaps have profound negative impacts on the robustness of marine conservation policies. Correlative modelling techniques have provided some information, but few studies have explored model development for non-breeding pelagic seabirds. Here, I present a first phase in developing robust niche models for highly mobile species as a baseline for further development. Methodology: Using observational data from a 12-year time period, 217 unique model parameterisations across three correlative modelling algorithms (boosted regression trees, Maxent and minimum volume ellipsoids) were tested in a time-averaged approach for their ability to recreate the at-sea distribution of non-breeding Wandering Albatrosses (Diomedea exulans) to provide a baseline for further development. Principle Findings/Results: Overall, minimum volume ellipsoids outperformed both boosted regression trees and Maxent. However, whilst the latter two algorithms generally overfit the data, minimum volume ellipsoids tended to underfit the data. Conclusions: The results of this exercise suggest a necessary evolution in how correlative modelling for highly mobile species such as pelagic seabirds should be approached. These insights are crucial for understanding seabird-environment interactions at macroscales, which can facilitate the ability to address population declines and inform effective marine conservation policy in the wake of rapid global change.

Download Full-text

Insights into fish-anthropogenic pressures relationships using machine learning techniques: the case of Castilla-La Mancha (Spain)

10.5194/egusphere-egu21-7119 ◽

2021 ◽

Author(s):

Carlotta Valerio ◽

Graciela Gómez Nicola ◽

Rocío Aránzazu Baquero Noriega ◽

Alberto Garrido ◽

Lucia De Stefano

Keyword(s):

Machine Learning ◽

Fish Species ◽

Conservation Status ◽

Boosted Regression Trees ◽

Machine Learning Techniques ◽

Freshwater Species ◽

Anthropogenic Pressures ◽

La Mancha ◽

Learning Techniques ◽

Starting Point

<p>Since 1970 the number of freshwater species has suffered a decline of 83% worldwide and anthropic activities are considered to be major drivers of ecosystems degradation. Linking the ecological response to the multiple anthropogenic stressors acting in the system is essential to effectively design policy measures to restore riverine ecosystems. However, obtaining quantitative links between stressors and ecological status is still challenging, given the non-linearity of the ecosystem response and the need to consider multiple factors at play. This study applies machine learning techniques to explore the relationships between anthropogenic pressures and the composition of fish communities in the river basins of Castilla-La Mancha, a region covering nearly 79 500 km&#178; in central Spain. During the past two decades, this region has experienced an alarming decline of the conservation status of native fish species. The starting point for the analysis is a 10x10 km grid that defines for each cell the presence or absence of several fish species before and after 2001. This database was used to characterize the evolution of several metrics of fish species richness over time, accounting for the species origin (native or alien), species features (e.g. pollution tolerance) and habitat preferences. Random Forest and Gradient Boosted Regression Trees algorithms were used to relate the resulting metrics to the stressor variables describing the anthropogenic pressures acting in the rivers, such as urban wastewater discharges, land use cover, hydro-morphological degradation and the alteration of the river flow regime. The study provides new, quantitative insights into pressures-ecosystem relationships in rivers and reveals the main factors that lead to the decline of fish richness in Castilla-La Mancha, which could help inform environmental policy initiatives.</p>

Download Full-text

Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

Sustainability ◽

10.3390/su12041396 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1396

Author(s):

Shufang Wang ◽

Xiyun Jiao ◽

Liping Wang ◽

Aimin Gong ◽

Honghui Sang ◽

...

Keyword(s):

Land Use ◽

Cellular Automata ◽

Markov Model ◽

Land Use Changes ◽

Regression Trees ◽

Boosted Regression Trees ◽

Utilization Rate ◽

Protection Measures ◽

Unused Land ◽

The Government

The simulation and prediction of the land use changes is generally carried out by cellular automata—Markov (CA-Markov) model, and the generation of suitable maps collection is subjective in the simulation process. In this study, the CA-Markov model was improved by the Boosted Regression Trees (BRT) to simulate land use to make the model objectively. The weight of ten driving factors of the land use changes was analyzed in BRT, in order to produce the suitable maps collection. The accuracy of the model was verified. The outcomes represent a match of over 84% between simulated and actual land use in 2015, and the Kappa coefficient was 0.89, which was satisfactory to approve the calibration process. The land use of Hotan Oasis in 2025 and 2035 were predicted by means of this hybrid model. The area of farmland, built-up land and water body in Hotan Oasis showed an increasing trend, while the area of forestland, grassland and unused land continued to show a decreasing trend in 2025 and 2035. The government needs to formulate measures to improve the utilization rate of water resources to meet the growth of farmland, and need to increase ecological environment protection measures to curb the reduction of grass land and forest land for the ecological health.

Download Full-text

Case fatality ratio estimates for the 2013–2016 West African Ebola epidemic: application of Boosted Regression Trees for imputation

International Journal of Infectious Diseases ◽

10.1016/j.ijid.2018.11.313 ◽

2019 ◽

Vol 79 ◽

pp. 128 ◽

Cited By ~ 3

Author(s):

A. Forna ◽

P. Nouvellet ◽

I. Dorigatti ◽

C.A. Donnelly

Keyword(s):

Case Fatality ◽

Regression Trees ◽

West African ◽

Boosted Regression Trees ◽

Case Fatality Ratio

Download Full-text

Insights into the BRT (Boosted Regression Trees) Method in the Study of the Climate-Growth Relationship of Masson Pine in Subtropical China

Forests ◽

10.3390/f10030228 ◽

2019 ◽

Vol 10 (3) ◽

pp. 228 ◽

Cited By ~ 8

Author(s):

Hongliang Gu ◽

Jian Wang ◽

Lijuan Ma ◽

Zhiyuan Shang ◽

Qipeng Zhang

Keyword(s):

Tree Growth ◽

General Circulation ◽

Growth Response ◽

Circulation Model ◽

Regression Trees ◽

Pinus Massoniana ◽

Boosted Regression Trees ◽

Climate Factors ◽

Masson Pine ◽

Hydroclimatic Variability

Dendroclimatology and dendroecology have entered mainstream dendrochronology research in subtropical and tropical areas. Our study focused on the use of the chronology series of Masson pine (Pinus massoniana Lamb.), the most widely distributed tree species in the subtropical wet monsoon climate regions in China, to understand the tree growth response to ecological and hydroclimatic variability. The boosted regression trees (BRT) model, a nonlinear machine learning method, was used to explore the complex relationship between tree-ring growth and climate factors on a larger spatial scale. The common pattern of an asymptotic growth response to the climate indicated that the climate-growth relationship may be linear until a certain threshold. Once beyond this threshold, tree growth will be insensitive to some climate factors, after which a nonlinear relationship may occur. Spring and autumn climate factors are important controls of tree growth in most study areas. General circulation model (GCM) projections of future climates suggest that warming climates, especially temperatures in excess of those of the optimum growth threshold (as estimated by BRT), will be particularly threatening to the adaptation of Masson pine.

Download Full-text