Using a multi-model ensemble approach to determine biodiversity hotspots with limited occurrence data in understudied areas: An example using freshwater mussels in Mexico

被引:9
|
作者
Kiser, Alexander H. [1 ]
Cummings, Kevin S. [2 ]
Tiemann, Jeremy S. [2 ]
Smith, Chase H. [3 ]
Johnson, Nathan A. [4 ]
Lopez, Roel R. [1 ,5 ]
Randklev, Charles R. [1 ,5 ]
机构
[1] Texas A&M Nat Resources Inst, Texas A&M AgriLife Res Ctr Dallas, Dallas, TX 77843 USA
[2] Illinois Nat Hist Survey, Urbana, IL USA
[3] Univ Texas Austin, Dept Integrat Biol, Austin, TX 78712 USA
[4] US Geol Survey, Wetland & Aquat Res Ctr, Gainesville, FL USA
[5] Texas A&M Univ, Nat Resources Inst, College Stn, TX USA
来源
ECOLOGY AND EVOLUTION | 2022年 / 12卷 / 05期
关键词
climate; conservation; habitat; maxent; mycetopodidae; random forest; species distribution model; unionidae; SPECIES DISTRIBUTION MODELS; LAND-USE; HABITAT CONFIGURATION; CLIMATE SURFACES; PANUCO BASIN; PREDICTION; SEDIMENT; FISH; STRATEGIES; RIVERS;
D O I
10.1002/ece3.8909
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Species distribution models (SDMs) are an increasingly important tool for conservation particularly for difficult-to-study locations and with understudied fauna. Our aims were to (1) use SDMs and ensemble SDMs to predict the distribution of freshwater mussels in the Panuco River Basin in Central Mexico; (2) determine habitat factors shaping freshwater mussel occurrence; and (3) use predicted occupancy across a range of taxa to identify freshwater mussel biodiversity hotspots to guide conservation and management. In the Panuco River Basin, we modeled the distributions of 11 freshwater mussel species using an ensemble approach, wherein multiple SDM methodologies were combined to create a single ensemble map of predicted occupancy. A total of 621 species-specific observations at 87 sites were used to create species-specific ensembles. These predictive species ensembles were then combined to create local diversity hotspot maps. Precipitation during the warmest quarter, elevation, and mean temperature were consistently the most important discriminatory environmental variables among species, whereas land use had limited influence across all taxa. To the best of our knowledge, our study is the first freshwater mussel-focused research to use an ensemble approach to determine species distribution and predict biodiversity hotspots. Our study can be used to guide not only current conservation efforts but also prioritize areas for future conservation and study.
引用
收藏
页数:14
相关论文
共 7 条
  • [1] Using a multi-model ensemble forecasting approach to identify key marine protected areas for seabirds in the Portuguese coast
    Pereira, Jorge M.
    Kruger, Lucas
    Oliveira, Nuno
    Meirinho, Ana
    Silva, Alexandra
    Ramos, Jaime A.
    Paiva, Vitor H.
    OCEAN & COASTAL MANAGEMENT, 2018, 153 : 98 - 107
  • [2] Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method
    Ruiz- Alvarez, Marcos
    Gomariz-Castillo, Francisco
    Alonso-Sarria, Francisco
    WATER, 2021, 13 (02)
  • [3] An Alternative Ensemble Streamflow Prediction Approach Using Improved Subseasonal Precipitation Forecasts from the North America Multi-Model Ensemble Phase II
    Zhang, Lujun
    Yang, Tiantian
    Gao, Shang
    Fan, Ming
    Lu, Dan
    Xu, Hongmei
    Xiao, Chan
    JOURNAL OF HYDROMETEOROLOGY, 2025, 26 (03) : 309 - 326
  • [4] Data-driven prediction of building energy consumption using an adaptive multi-model fusion approach
    Lin, Penghui
    Zhang, Limao
    Zuo, Jian
    APPLIED SOFT COMPUTING, 2022, 129
  • [5] Projecting the Impact of Climate Change on the Spatial Distribution of Six Subalpine Tree Species in South Korea Using a Multi-Model Ensemble Approach
    Lee, Sanghyuk
    Jung, Huicheul
    Choi, Jaeyong
    FORESTS, 2021, 12 (01): : 1 - 13
  • [6] Probabilistic multi-model ensemble prediction of Indian summer monsoon rainfall using general circulation models: A non-parametric approach
    Acharya, Nachiketa
    Mohanty, Uma Charan
    Sahoo, Lokanath
    COMPTES RENDUS GEOSCIENCE, 2013, 345 (03) : 126 - 135
  • [7] Email-Based Cyberstalking Detection On Textual Data Using Multi-Model Soft Voting Technique Of Machine Learning Approach
    Gautam, Arvind Kumar
    Bansal, Abhishek
    JOURNAL OF COMPUTER INFORMATION SYSTEMS, 2023, 63 (06) : 1362 - 1381