Predicting the Presence of Groundwater-Influenced Ecosystems in the Northeastern United States with Ensembled Models
Abstract
:1. Introduction
- (1)
- Used different correlative distribution modeling methods to estimate landscape suitability for GIE occurrence;
- (2)
- Ensembled our model results to create consensus models;
- (3)
- Evaluated the sensitivity of model predictions to environmental variable inclusion; and
- (4)
- Used these consensus models to select locations to assess model performance with on-site validation and to compare the model accuracy of GIE predictions to determine the reliability of the consensus models.
2. Methods
2.1. Study Area
2.2. Allocation of Presence Dataset
2.3. Environmental Covariates
2.4. Model Development and Evaluation
2.5. Model Validation
2.6. Model Agreement
2.7. Ensemble Model
3. Results
3.1. Classical Regression Models (GAM, GLM)
3.2. Machine Learning Models (MaxEnt, Random Forest)
3.3. Evaluation of Models
3.4. Model Agreement
3.5. Ensembled Model
4. Discussion
4.1. Development and Reliability of Ensembled Correlative Distribution Models
4.2. Important Variables in Ensembled Models
4.3. Prediction of Wetlands with Groundwater Influence
4.4. Predictions of Groundwater-Influenced Thermal Refugia in Streams and Rivers
4.5. Future Applications and Improvements
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Brown, J.; Bach, L.; Aldous, A.; Wyers, A.; DeGagné, J. Groundwater-Dependent Ecosystems in Oregon: An Assessment of their Distribution and Associated Threats. Front. Ecol. Environ. 2011, 9, 97–102. [Google Scholar] [CrossRef]
- Pérez Hoyos, I.C.; Krakauer, N.Y.; Khanbilvardi, R.; Armstrong, R.A. A Review of Advances in the Identification and Characterization of Groundwater Dependent Ecosystems Using Geospatial Technologies. Geosciences 2016, 6, 17. [Google Scholar] [CrossRef]
- Glasser, S.; Gauthier-Warinner, J.; Keely, J.; Gurrieri, J.; Tucci, P.; Summers, P.; Wireman, M.; McCormack, K. Technical Guide to Managing Ground Water Resources; United States Department of Agiculture: Washington, DC, USA, 2007.
- Rohde, M.M.; Froend, R.; Howard, J. A Global Synthesis of Managing Groundwater Dependent Ecosystems under Sustainable Groundwater Policy. Groundwater 2017, 55, 293–301. [Google Scholar] [CrossRef]
- Van Grinsven, M.; Mayer, A.; Huckins, C. Estimation of Streambed Groundwater Fluxes Associated with Coaster Brook Trout Spawning Habitat. Groundwater 2012, 50, 432–441. [Google Scholar] [CrossRef]
- Rosenberry, D.O.; Briggs, M.A.; Voytek, E.B.; Lane, J.W. Influence of Groundwater on Distribution of Dwarf Wedgemussels (Alasmidonta heterodon) in the Upper Reaches of the Delaware River, Northeastern USA. Hydrol. Earth Syst. Sci. 2016, 20, 4323–4339. [Google Scholar] [CrossRef]
- Kurylyk, B.L.; MacQuarrie, K.T.B.; Linnansaari, T.; Cunjak, R.A.; Curry, R.A. Preserving, Augmenting, and Creating Cold-Water Thermal Refugia in Rivers: Concepts Derived from Research on the Miramichi River, New Brunswick (Canada). Ecohydrology 2015, 8, 1095–1108. [Google Scholar] [CrossRef]
- Eamus, D. Identifying Groundwater Dependent Ecosystems: A Guide for Land and Water Managers; Land & Water Australia: Perth, Australia, 2009. [Google Scholar]
- Hoyos, I.C.P. Identification of Phreatophytic Groundwater Dependent Ecosystems Using Geospatial Technologies. Ph.D. Thesis, The City College of New York, New York, NY, USA, 2016. [Google Scholar]
- Fauvet, G.; Claret, C.; Marmonier, P. Influence of Benthic and Interstitial Processes on Nutrient Changes along a Regulated Reach of a Large River (Rhône River, France). Hydrobiologia 2001, 445, 121–131. [Google Scholar] [CrossRef]
- Howard, J.; Merrifield, M. Mapping Groundwater Dependent Ecosystems in California. PLoS ONE 2010, 5, e11249. [Google Scholar] [CrossRef]
- Aldous, A.; Bach, L. Protecting Groundwater-Dependent Ecosystems: Gaps and Opportunities. Natl. Wetl. Newsl. 2011, 33, 19–22. [Google Scholar]
- Le Maitre, D.C.; Colvin, C.A. Assessment of the Contribution of Groundwater Discharges to Rivers Using monthly flow statistics and Flow Seasonality. Water SA 2008, 34, 549–564. [Google Scholar] [CrossRef]
- Güntner, A.; Stuck, J.; Werth, S.; Döll, P.; Verzano, K.; Merz, B. A Global Analysis of Temporal and Spatial Variations in Continental Water Storage. Water Resour. Res. 2007, 43, W05416. [Google Scholar] [CrossRef]
- Kløve, B.; Ala-aho, P.; Bertrand, G.; Boukalova, Z.; Ertürk, A.; Goldscheider, N.; Ilmonen, J.; Karakaya, N.; Kupfersberger, H.; Kvœrner, J.; et al. Groundwater Dependent Ecosystems. Part I: Hydroecological Status and Trends. Environ. Sci. Policy 2011, 14, 770–781. [Google Scholar] [CrossRef]
- Kløve, B.; Ala-Aho, P.; Bertrand, G.; Gurdak, J.J.; Kupfersberger, H.; Kværner, J.; Muotka, T.; Mykrä, H.; Preda, E.; Rossi, P.; et al. Climate Change Impacts on Groundwater and Dependent Ecosystems. J. Hydrol. 2014, 518, 250–266. [Google Scholar] [CrossRef]
- Barron, O.V.; Emelyanova, I.; Van Niel, T.G.; Pollock, D.; Hodgson, G. Mapping Groundwater-Dependent Ecosystems Using Remote Sensing Measures of Vegetation and Moisture Dynamics. Hydrol. Process. 2014, 28, 372–385. [Google Scholar] [CrossRef]
- Barclay, J.R.; Briggs, M.A.; Moore, E.M.; Starn, J.J.; Hanson, A.E.H.; Helton, A.M. Where Groundwater Seeps: Evaluating Modeled Groundwater Discharge Patterns with Thermal Infrared Surveys at the River-Network Scale. Adv. Water Resour. 2022, 160, 104108. [Google Scholar] [CrossRef]
- Doody, T.M.; Barron, O.V.; Dowsley, K.; Emelyanova, I.; Fawcett, J.; Overton, I.C.; Pritchard, J.L.; Van Dijk, A.I.; Warren, G. Continental Mapping of Groundwater Dependent Ecosystems: A Methodological Framework to Integrate Diverse Data and Expert Opinion. J. Hydrol. Reg. Stud. 2017, 10, 61–81. [Google Scholar] [CrossRef]
- Hoyos, I.P.; Krakauer, N.; Khanbilvardi, R. Random Forest for Identification and Characterization of Groundwater Dependent Ecosystems. WIT Trans. Ecol. Environ. 2015, 196, 89–100. [Google Scholar]
- Santos, M.J.; Smith, A.B.; Dekker, S.C.; Eppinga, M.B.; Leitão, P.J.; Moreno-Mateos, D.; Morueta-Holme, N.; Ruggeri, M. The Role of Land Use and Land Cover Change in Climate Change Vulnerability Assessments of Biodiversity: A Systematic Review. Landsc. Ecol. 2021, 36, 3367–3382. [Google Scholar] [CrossRef]
- Werstak, C.E.; Housman, I.; Maus, P.; Fisk, H.; Gurrieri, J.; Carlson, C.P.; Johnston, B.C.; Stratton, B.; Hurja, J.C. Groundwater-Dependent Ecosystem Inventory Using Remote Sensing; United States Department of Agriculture: Washington, DC, USA, 2012.
- Elmore, A.J.; Mustard, J.F.; Manning, S.J. Regional Patterns of Plant Community Response to Changes in Water: Owens Valley, California. Ecol. Appl. 2003, 13, 443–460. [Google Scholar] [CrossRef]
- U.S. Geological Survey. National Hydrography Dataset (ver. USGS National Hydrography Dataset (NHD)). 2016. Available online: https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products (accessed on 21 November 2022).
- Adhikary, P.P.; Dash, C.J. Comparison of Deterministic and Stochastic Methods to Predict Spatial Variation of Groundwater Depth. Appl. Water Sci. 2017, 7, 339–348. [Google Scholar] [CrossRef]
- Jarnevich, C.S.; Stohlgren, T.J.; Kumar, S.; Morisette, J.T.; Holcombe, T.R. Caveats for Correlative Species Distribution Modeling. Ecol. Inform. 2015, 29, 6–15. [Google Scholar] [CrossRef]
- Shabani, F.; Kumar, L.; Ahmadi, M. A Comparison of Absolute Performance of Different Correlative and Mechanistic Species Distribution Models in an Independent Area. Ecol. Evol. 2016, 6, 5973–5986. [Google Scholar] [CrossRef]
- McCullagh, P.; Nelder, J.A. Binary Data. In Generalized Linear Models; Springer: Berlin/Heidelberg, Germany, 1989; pp. 98–148. [Google Scholar]
- Elith, J.; Phillips, S.J.; Hastie, T.; Dudík, M.; Chee, Y.E.; Yates, C.J. A Statistical Explanation of MaxEnt for Ecologists. Divers. Distrib. 2011, 17, 43–57. [Google Scholar] [CrossRef]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum Entropy Modeling of Species Geographic Distributions. Ecol. Model. 2006, 190, 231–259. [Google Scholar] [CrossRef]
- Elith, J.; Graham, C.H.; Anderson, R.P.; Dudík, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A.; et al. Novel Methods Improve Prediction of Species’ Distributions from Occurrence Data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef]
- Duan, R.-Y.; Kong, X.-Q.; Huang, M.-Y.; Fan, W.-Y.; Wang, Z.-G. The Predictive Performance and Stability of Six Species Distribution Models. PLoS ONE 2014, 9, e112764. [Google Scholar] [CrossRef]
- Qiao, H.; Soberón, J.; Peterson, A.T. No Silver Bullets in Correlative Ecological Niche Modelling: Insights from Testing among Many Potential Algorithms for Niche Estimation. Methods Ecol. Evol. 2015, 6, 1126–1136. [Google Scholar] [CrossRef]
- Araújo, M.B.; New, M. Ensemble Forecasting of Species Distributions. Trends Ecol. Evol. 2007, 22, 42–47. [Google Scholar] [CrossRef]
- Grenouillet, G.; Buisson, L.; Casajus, N.; Lek, S. Ensemble Modelling of Species Distribution: The Effects of Geographical and Environmental Ranges. Ecography 2011, 34, 9–17. [Google Scholar] [CrossRef]
- Ramirez-Reyes, C.; Nazeri, M.; Street, G.; Jones-Farrand, D.T.; Vilella, F.J.; Evans, K.O. Embracing Ensemble Species Distribution Models to Inform At-Risk Species Status Assessments. J. Fish Wildl. Manag. 2021, 12, 98–111. [Google Scholar] [CrossRef]
- Noss, R.F.; LaRoe, E.T.; Scott, J.M. Endangered Ecosystems of the United States: A Preliminary Assessment of Loss and Degradation; US Department of the Interior, National Biological Service: Washington, DC, USA, 1995; Volume 28.
- Lamptey, B.L.; Barron, E.J.; Pollard, D. Impacts of Agriculture and urbaNization on the Climate of the Northeastern United States. Glob. Planet. Chang. 2005, 49, 203–221. [Google Scholar] [CrossRef]
- Ferguson, G.; Gleeson, T. Vulnerability of Coastal Aquifers to Groundwater Use and Climate Change. Nat. Clim. Chang. 2012, 2, 342. [Google Scholar] [CrossRef]
- Blevins, E.; Aldous, A. Biodiversity Value of Groundwater-Dependent Ecosystems. Nat. Conserv. 2011, 7, 18–24. [Google Scholar]
- Boulton, A.J.; Hancock, P.J. Rivers as Groundwater-Dependent Ecosystems: A Review of Degrees of Dependency, Riverine Processes and Management Implications. Aust. J. Bot. 2006, 54, 133–144. [Google Scholar] [CrossRef]
- Humphreys, W.F. Aquifers: The Ultimate Groundwater-Dependent Ecosystems. Aust. J. Bot. 2006, 54, 115. [Google Scholar] [CrossRef]
- Murray, B.R.; Hose, G.C.; Eamus, D.; Licari, D. Valuation of Groundwater-Dependent Ecosystems: A Functional Methodology Incorporating Ecosystem Services. Aust. J. Bot. 2006, 54, 221–229. [Google Scholar] [CrossRef]
- Omernik, J.M.; Griffith, G.E. Ecoregions of the Conterminous United States: Evolution of a Hierarchical Spatial Framework. Environ. Manag. 2014, 54, 1249–1266. [Google Scholar] [CrossRef]
- Gillin, C.P.; Bailey, S.W.; McGuire, K.J.; Gannon, J.P. Mapping of Hydropedologic Spatial Patterns in a Steep Headwater Catchment. Soil Sci. Soc. Am. J. 2015, 79, 440–453. [Google Scholar] [CrossRef]
- Soller, D.R.; Reheis, M.C.; Garrity, C.P.; Van Sistine, D.R. Map Database for Surficial Materials in the Conterminous United States. U.S. Geological Survey Data Series 425. 2009. Available online: https://pubs.usgs.gov/ds/425/ (accessed on 21 November 2022).
- McGarigal, K.; Compton, B.W.; Plunkett, E.B.; DeLuca, W.V.; Grand, J. Designing Sustainable Landscapes Products, Including Technical Documentation and Data Products 2017. Available online: https://scholarworks.umass.edu/designing_sustainable_landscapes/ (accessed on 21 November 2022).
- Martínez-Santos, P.; Díaz-Alcaide, S.; De la Hera-Portillo, A.; Gómez-Escalonilla, V. Mapping Groundwater-Dependent Ecosystems by Means of Multi-Layer Supervised Classification. J. Hydrol. 2021, 603, 126873. [Google Scholar] [CrossRef]
- Eamus, D.; Froend, R. Groundwater-Dependent Ecosystems: The Where, What and Why of GDEs. Aust. J. Bot. 2006, 54, 91. [Google Scholar] [CrossRef]
- Palmer, W.C. Meteorological Drought; U.S. Department of Commerce, Weather Bureau: Washington, DC, USA, 1965.
- Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
- Elith, J.; Graham, C.H. Do they? How Do They? Why Do They Differ? On Finding Reasons for Differing Performances of Species Distribution Models. Ecography 2009, 32, 66–77. [Google Scholar] [CrossRef]
- Leathwick, J.R.; Elith, J.; Hastie, T. Comparative Performance of Generalized Additive Models and Multivariate Adaptive Regression Splines for Statistical Modelling of Species Distributions. Ecol. Model. 2006, 199, 188–196. [Google Scholar] [CrossRef]
- Hao, T.; Elith, J.; Lahoz-Monfort, J.J.; Guillera-Arroita, G. Testing Whether Ensemble Modelling Is Advantageous for Maximising Predictive Performance of Species Distribution Models. Ecography 2020, 43, 549–558. [Google Scholar] [CrossRef]
- Kaky, E.; Nolan, V.; Alatawi, A.; Gilbert, F. A Comparison between Ensemble and MaxEnt Species Distribution Modelling Approaches for Conservation: A Case Study with Egyptian Medicinal Plants. Ecol. Inform. 2020, 60, 101150. [Google Scholar] [CrossRef]
- González-Irusta, J.; González-Porto, M.; Sarralde, R.; Arrese, B.; Almón, B.; Martín-Sosa, P. Comparing Species Distribution models: A Case Study of Four Deep Sea Urchin Species. Hydrobiologia 2014, 745, 43–57. [Google Scholar] [CrossRef]
- Wood, S.N. Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models: Estimation of Semiparametric Generalized Linear Models. J. R. Stat. Soc. Ser. B Stat. Methodol. 2011, 73, 3–36. [Google Scholar] [CrossRef]
- Liaw, A.; Wiener, M. Classification and Regression by randomForest. R News 2002, 2, 18–22. [Google Scholar]
- Hijmans, R.J.; Phillips, S.; Leathwick, J.; Elith, J. DISMO: Species Distribution Modeling, R package version 1.0-12; The R Foundation for Statistical Computing: Vienna, Austria, 2015. [Google Scholar]
- Lobo, J.M.; Jiménez-Valverde, A.; Real, R. AUC: A Misleading Measure of the Performance of Predictive Distribution Models. Glob. Ecol. Biogeogr. 2008, 17, 145–151. [Google Scholar] [CrossRef]
- Baldwin, R.A. Use of Maximum Entropy Modeling in Wildlife Research. Entropy 2009, 11, 854–866. [Google Scholar] [CrossRef]
- Cohen, J. A Coefficient of Agreement for Nominal Scales. Educ. Psychol. Meas. 1960, 20, 37–46. [Google Scholar] [CrossRef]
- Viera, A.J.; Garrett, J.M. Understanding Interobserver Agreement: The Kappa Statistic. Fam. Med. 2005, 37, 360–363. [Google Scholar] [PubMed]
- Allouche, O.; Tsoar, A.; Kadmon, R. Assessing the Accuracy of Species Distribution Models: Prevalence, Kappa and the True Skill Statistic (TSS). J. Appl. Ecol. 2006, 43, 1223–1232. [Google Scholar] [CrossRef]
- Batelaan, O.; Witte, J.P.M. Ecohydrology and Groundwater Dependent Terrestrial Ecosystems. In Proceedings of the 28th Annual Conference of the International Association of Hydrogeologists (Irish Group), Tullamore, Ireland, 22–23 April 2008; pp. 22–23. [Google Scholar]
- Richardson, S.; Irvine, E.; Froend, R.; Boon, P.; Barber, S.; Bonneville, B. Australian Groundwater Dependent Ecosystems Toolbox Part 1: Assessment Framework; Waterlines Report; National Water Commission: Canberra, Australia, 2011. [Google Scholar]
- Briggs, M.A.; Buckley, S.F.; Bagtzoglou, A.C.; Werkema, D.D.; Lane, J.W., Jr. Actively Heated High-Resolution Fiber-Optic-Distributed Temperature Sensing to Quantify Streambed Flow Dynamics in Zones of Strong Groundwater Upwelling. Water Resour. Res. 2016, 52, 5179–5194. [Google Scholar] [CrossRef]
- Vepraskas, M.J. History of the Concept of Hydric Soil. In Wetland Soils; CRC Press: Boca Raton, FL, USA, 2016; ISBN 978-0-429-18431-4. [Google Scholar]
- Raney, P.A.; Leopold, D.J. Fantastic Wetlands and Where to Find Them: Modeling Rich Fen Distribution in New York State with Maxent. Wetlands 2018, 38, 81–93. [Google Scholar] [CrossRef]
- Golkarian, A.; Rahmati, O. Use of a Maximum Entropy Model to Identify the Key Factors That Influence Groundwater Availability on the Gonabad Plain, Iran. Environ. Earth Sci. 2018, 77, 369. [Google Scholar] [CrossRef]
- Winter, T.C.; Harvey, J.W.; Franke, O.L.; Alley, W.M. Ground Water and Surface Water: A Single Resource; US Geological Survey Circular 1139; Diane Publishing: Collingdale, PA, USA, 1998; Volume 50. [Google Scholar]
- Salvacion, A.R. Groundwater Potential Mapping Using Maximum Entropy. In Water Resources Management and Sustainability; Kumar, P., Nigam, G.K., Sinha, M.K., Singh, A., Eds.; Advances in Geographical and Environmental Sciences; Springer Nature: Singapore, 2022; pp. 239–256. ISBN 9789811665738. [Google Scholar]
- Stolt, M.H.; Baker, J.C. Evaluation of National Wetland Inventory maps to inventory wetlands in the southern Blue Ridge of Virginia. Wetlands 1995, 15, 346–353. [Google Scholar] [CrossRef]
- Kudray, G.M.; Gale, M.R. Evaluation of National Wetland Inventory Maps in a Heavily Forested Region in the Upper Great Lakes. Wetlands 2000, 20, 581–587. [Google Scholar] [CrossRef]
- Dvorett, D.; Bidwell, J.; Davis, C.; DuBois, C. Developing a Hydrogeomorphic Wetland Inventory: Reclassifying National Wetlands Inventory Polygons in Geographic Information Systems. Wetlands 2012, 32, 83–93. [Google Scholar] [CrossRef]
- Cowardin, L.M. Wetland Classification in the United States. J. For. 1978, 76, 666–668. [Google Scholar]
- Brinson, M.M. A Hydrogeomorphic Classification for Wetlands; U.S. Army Corps of Engineers, Wetlands Research Program: Washington, DC, USA, 1993; 103p. [Google Scholar]
- Hare, D.K.; Helton, A.M.; Johnson, Z.C.; Lane, J.W.; Briggs, M.A. Continental-Scale Analysis of Shallow and Deep Groundwater Contributions to Streams. Nat. Commun. 2021, 12, 1450. [Google Scholar] [CrossRef]
- DeWeber, J.T.; Wagner, T. Predicting Brook Trout Occurrence in Stream Reaches throughout Their Native Range in the Eastern United States. Trans. Am. Fish. Soc. 2015, 144, 11–24. [Google Scholar] [CrossRef]
- Lombard, P.J.; Dudley, R.W.; Collins, M.J.; Saunders, R.; Atkinson, E. Model Estimated Baseflow for Streams with Endangered Atlantic Salmon in Maine, USA. River Res. Appl. 2021, 37, 1254–1264. [Google Scholar] [CrossRef]
- MacCrimmon, H.R.; Campbell, J.S. World Distribution of Brook Trout, Salvelinus Fontinalis. J. Fish. Board Can. 1969, 26, 1699–1725. [Google Scholar] [CrossRef]
- Stuckey, R.L.; Denny, G.L. Prairie Fens and Bog Fens in Ohio: Floristic Similarities, Differences, and Geographical Affinitiesp; Springer: Berlin/Heidelberg, Germany, 1981; pp. 1–33. [Google Scholar]
- Bedford, B.L.; Godwin, K.S. Fens of the United States: Distribution, Characteristics, and Scientific Connection versus Legal Isolation. Wetlands 2003, 23, 608–629. [Google Scholar] [CrossRef]
- Heidel, B.; Rodemaker, E. Inventory of Peatland Systems in the Beartooth Mountains; Shoshone National Forest: Park County, WY, USA, 2008. [Google Scholar]
- Lewis, M.; White, D.; Gotch, T. Spatial Survey and Remote Sensing of Artesian Springs of the Western Great Artesian Basin. In Allocating Water and Maintaining Springs in the Great Artesian Basin; National Water Commission: Canberra, Australia, 2013; Volume IV. [Google Scholar]
- Stohlgren, T.J. Measuring Plant Diversity: Lessons from the Field; OUP: Cary, NC, USA, 2007. [Google Scholar]
- Johnson, C.J.; Gillingham, M.P. An Evaluation of Mapped Species Distribution Models Used for Conservation Planning. Environ. Conserv. 2005, 32, 117–128. [Google Scholar] [CrossRef]
State | Natural Community Type or Vegetative Community |
---|---|
Connecticut | Circumneutral Northern White Cedar Swamp |
Circumneutral Spring Fen | |
Medium Fen | |
Poor Fen | |
Rich Fen | |
Sea Level Fen | |
Maine | Atlantic White Cedar Swamp |
Black Ash Swamp | |
Circumneutral Fen | |
Evergreen Seepage Forest | |
Hardwood Seepage Forest | |
Northern White Cedar Swamp | |
Open Cedar Fen | |
Pocket Swamp | |
Riverside Seep | |
Massachusetts | Acidic Graminoid Fen |
Acidic Graminoid Fen-Spillway Fen | |
Acidic Shrub Fen | |
Alluvial Atlantic White Cedar Swamp | |
Alluvial Red Maple Swamp | |
Calcareous Basin Fen | |
Calcareous Seepage Marsh | |
Calcareous Sloping Fen | |
Interdunal Marsh/Swale | |
Kettlehole Wet Meadow | |
Red Maple- Black Ash- Bur Oak Swamp | |
Red Maple- Black Ash- Tamarack Calcareous Seepage Swamp | |
Red Maple- Black Ash Swamp | |
Red Maple Swamp | |
Rich Conifer Swamp | |
Riverside Seep Community | |
Sea-Level Fen | |
New Hampshire | Acidic Riverside Seep |
Alder-lake Sedge Intermediate Fen | |
Calcareous Riverside Seep | |
Calcareous Sedge- Moss Fen | |
Calcareous Sloping Fen System | |
Circumneutral Hardwood Forest Seep | |
Forest Seep/Seepage Forest System | |
Herbaceous Seepage Marsh | |
Lake Sedge Seepage Marsh | |
Larch-Mixed Conifer Swamp | |
Montane Sloping Fen System | |
Montane/near-Boreal Minerotrophic Peat Swamp System | |
Northern Hardwood-Black Ash-Conifer Swamp | |
Northern Hardwood Seepage Forest | |
Northern White Cedar/Balsam Fir Swamp | |
Northern White Cedar-Hemlock Swamp | |
Northern White Cedar Seepage Forest | |
Patterned Fen System | |
Red Maple-Black Ash Swamp | |
Sand Plain Basin Marsh System | |
Subacid Forest Seep | |
New York | Coastal Plain Pond |
Coastal Plain Poor Fen | |
Hemlock-Hardwood Swamp | |
Marl Fen | |
Medium Fen | |
Northern White Cedar Swamp | |
Pine Barrens Vernal Pond | |
Red Maple-Tamarack Peat Swamp | |
Rich Graminoid Fen | |
Rich Hemlock-Hardwood Peat Swamp | |
Rich Shrub Fen | |
Rich Sloping Fen | |
Sea Level Fen | |
Sedge Meadow | |
Spruce-Fir Swamp | |
Pennsylvania | Hemlock-Mixed Hardwood Palustrine Forest |
Hemlock-Mixed Hardwood Palustrine Woodland | |
Red Maple- Black Gum Palustrine Forest | |
Red Spruce- Mixed Hardwood Palustrine Forest | |
Red Spruce Palustrine Forest | |
Elm-Ash- Maple Lake plain Forest | |
Alder-Leaved Buckthorn-Inland Sedge-Golden Ragwort Shrub Fen | |
Sedge-Mixed Forb Fen | |
Golden Saxifrage- Pennsylvania Bitter-cress Spring Run | |
Golden Saxifrage- Sedge Rich Seep | |
Serpentine Seep | |
Skunk-cabbage-Golden Saxifrage Seep | |
Rhode Island | Graminoid Fen |
Red Maple Swamp | |
Seal Level Fen | |
Seeps, Springs | |
Vermont | Calcareous Red Maple-Tamarack Swamp |
Calcareous Riverside Seep | |
Hemlock-Balsam Fir- Black Ash Seepage Swamp | |
Intermediate Fen | |
Northern Hardwood Seepage Forest | |
Poor Fen | |
Red Maple-Black Ash Seepage swamp | |
Rich Fen | |
Woodland Seep |
Environmental Variables | Data Source | Data Description | Data Category |
---|---|---|---|
Elevation (dem) | https://datagateway.nrcs.usda.gov/, accessed on 1 December 2021 | National Elevation Dataset | Topography |
Topographic Wetness Index (TWI) | https://datagateway.nrcs.usda.gov/, accessed on 1 December 2021 | National Elevation Dataset—[46] protocol | Topography |
Landforms (landform) | https://www.sciencebase.gov/catalog/item/5373c501e4b0497061278a4f, accessed on 1 December 2021 | Physical form of feature of the land surface | Topography |
Slope (slope) | https://datagateway.nrcs.usda.gov/, accessed on 1 December 2021 | Percent slope of the land surface derived from Elevation dataset | Topography |
Terrain Roughness Index (terrain) | https://datagateway.nrcs.usda.gov/, accessed on 1 December 2021 | National Elevation Dataset—Roughness tool—ArcGIS | Topography |
Surficial Materials (surfmat) | https://pubs.usgs.gov/of/2003/of03-275/, accessed on 1 December 2021 | Map database of the surficial materials in the conterminous United States [47] | Geology |
Hydric Soils (hydric) | https://datagateway.nrcs.usda.gov/, accessed on 1 December 2021 | Soil Survey Database—Gridded SSURGO | Geology |
Soil Water Holding Capacity | http://umassdsl.org/, accessed on 1 December 2021 | Total volume of water available to plants at field capacity [48] | Geology |
Soil Depth (soildep) | http://umassdsl.org/, accessed on 1 December 2021 | Measures the depth of soils to resistant layer [48] | Geology |
Soil Permeability Normalized Difference Vegetation Index | https://water.usgs.gov/GIS/metadata/usgswrd/XML/ussoils.xml#Metadata_Reference_Information, accessed on 1 December 2021 Landsat 8 NDVI Imagery (https://developers.google.com/earth-engine/datasets/catalog/landsat-8, accessed on 1 December 2021) | Inches of infiltration per hour Measure of the state of plant health | Geology Vegetation |
Parametric Coefficients: | |||||
---|---|---|---|---|---|
Estimate | Std. Error | z Value | Pr(>|z|) | Significance | |
(Intercept) | −1.92 × 102 | 1.50 × 107 | 0 | 0.99999 | |
surfmat3 1 | 1.64 × 10 | 7.35 × 10−1 | 2.231 | 0.02566 | * |
surfmat29 | 2.18 × 10 | 8.44 × 10−1 | 2.587 | 0.00969 | * |
Approximate significance of smoothed terms: | |||||
edf 2 | Ref. df 3 | Chi. sq | p-value | significance | |
s(elevation) | 3.283 | 4.054 | 10.226 | 0.03929 | * |
s(hydric soil) | 5.041 | 5.971 | 135.493 | <2 × 10−16 | * |
s(TWI) 4 | 4.345 | 5.257 | 16.862 | 0.00627 | * |
s(soil water holding capacity) | 4.678 | 5.743 | 18.569 | 0.00383 | * |
s(soil permeability) | 2.539 | 3.158 | 36.472 | 9.22 × 10−8 | * |
R-sq.(adj) = 0.292 | Deviance explained = 45% | ||||
UBRE 5 = −0.8056 | Scale est. = 1 | n = 8271 |
Coefficients: | |||||
---|---|---|---|---|---|
Estimate | Std. Error | z Value | Pr(>|z|) | Significance | |
(Intercept) | −1.78 × 101 | 1.97 × 103 | −0.009 | 0.992778 | |
Elevation | 1.66 × 10−3 | 4.80 × 10−4 | 3.471 | 0.000518 | * |
Hydric soil | 1.78 × 10−2 | 1.40 × 10−3 | 12.742 | <2.00 × 10−16 | * |
surfmat3 1 | 1.59 × 10 | 7.16 × 10−1 | 2.214 | 0.026845 | * |
surfmat29 | 1.92 × 10 | 8.31 × 10−1 | 2.313 | 0.020729 | * |
Soil permeability | 8.82 × 10−2 | 1.40 × 10−2 | 6.314 | 2.72 × 10−10 | * |
Null deviance: 2684.5 on 8270 degrees of freedom | |||||
Residual deviance: 1601.9 on 8217 degrees of freedom | |||||
AIC 2: 2643.9 | |||||
Number of Fisher Scoring iterations: 17 |
Parametric Coefficients: | |||||
---|---|---|---|---|---|
Approximate significance of smoothed terms: | |||||
Edf 1 | Ref. df 2 | Chi. sq | p-value | significance | |
s(elevation) | 4.145 | 5.127 | 51.377 | 9.77 × 10−10 | * |
s(hydric soil) | 6.543 | 7.509 | 130.372 | <2.00 × 10−16 | * |
s(terrain roughness) | 1.002 | 1.003 | 4.473 | 0.034661 | * |
s(TWI) 3 | 5.395 | 6.383 | 76.138 | 6.00 × 10−14 | * |
s(slope) | 1.001 | 1.001 | 5.484 | 0.019225 | * |
s(soil depth) | 6.194 | 7.216 | 29.612 | 0.000141 | * |
s(soil water holding capacity) | 4.763 | 5.8 | 41.116 | 2.49 × 10−7 | * |
s(NDVI) 4 | 2.641 | 3.414 | 24.682 | 3.84 × 10−5 | * |
R-sq.(adj) = 0.281 | Deviance explained = 36.90% | ||||
UBRE 5 = −2022.6 | Scale est. = 1 | n = 9205 |
Coefficients: | |||||
---|---|---|---|---|---|
Estimate | Std. Error | z Value | Pr(>|z|) | Significance | |
(Intercept) | −4.65 × 10−1 | 9.58 × 10−1 | −0.486 | 0.627119 | |
Elevation | 1.08 × 10−3 | 2.65 × 10−4 | 4.066 | 4.78 × 10−5 | * |
Hydric soil | 1.23 × 10−2 | 1.02 × 10−3 | 12.006 | <2.00 × 10−16 | * |
surfmaterials32 1 | −1.14 × 10 | 5.61 × 10−1 | −2.026 | 0.042747 | * |
Terrain roughness | 9.26 × 10−3 | 3.04 × 10−3 | 3.045 | 0.002324 | * |
Slope | −4.88 × 10−2 | 8.54 × 10−3 | −5.715 | 1.10 × 10−8 | * |
Soil water holding capacity | −1.28 × 10−1 | 3.65 × 10−2 | −3.499 | 0.000468 | * |
NDVI 2 | −5.14 × 10 | 7.34 × 10−1 | −7.007 | 2.44 × 10−12 | * |
Null deviance: 3888.4 on 9204 degrees of freedom | |||||
Residual deviance: 2749.1 on 9161 degrees of freedom | |||||
AIC 3: 4423.1 | |||||
Number of Fisher Scoring iterations: 14 |
Ecoregion | Model | AUC | TSS | Kappa | Sensitivity | Specificity | Deviance |
---|---|---|---|---|---|---|---|
Mixed Wood Plains Ecoregion | GAM | 0.87 | 0.62 | 0.37 | 0.73 | 0.89 | 0.31 |
GLM | 0.87 | 0.64 | 0.28 | 0.82 | 0.82 | 0.34 | |
Random Forest | 0.93 | 0.73 | 0.36 | 0.87 | 0.86 | 0.25 | |
Maxent | 0.9 | 0.65 | 0.31 | 0.81 | 0.84 | 0.48 | |
Atlantic Highlands Ecoregion | GAM | 0.85 | 0.56 | 0.25 | 0.84 | 0.73 | 0.41 |
GLM | 0.85 | 0.57 | 0.29 | 0.79 | 0.78 | 0.44 | |
Random Forest | 0.9 | 0.65 | 0.33 | 0.86 | 0.79 | 0.36 | |
Maxent | 0.88 | 0.65 | 0.3 | 0.89 | 0.75 | 0.63 |
Atlantic Highlands Ecoregion | 0.70 | Natural Breaks |
---|---|---|
Models in agreement | Hectares (% of ecoregion) | Hectares (% of ecoregion) |
1 | 865,654 (5.7) | 674,708 (4.5) |
2 | 422,683 (2.8) | 304,189 (2.02) |
3 | 329,426 (2.2) | 249,430 (1.7) |
4 | 22,027 (0.2) | 61,710 (0.4) |
Mixed Wood Plains Ecoregion | ||
Models in agreement | Hectares (% of ecoregion) | Hectares (% of ecoregion) |
1 | 1,135,298 (6.2) | 837,498 (4.6) |
2 | 191,663 (1.1) | 198,025 (1.1) |
3 | 319,445 (1.7) | 243,666 (1.3) |
4 | 16,431 (0.1) | 53,285 (0.3) |
State | Predicted Suitable Area (Hectares) | Proportion of Ecoregion (%) |
---|---|---|
Atlantic Highlands Ecoregion Connecticut | 6188 | 0.04 |
Maine | 6385 | 0.04 |
Massachusetts | 21,471 | 0.14 |
New Hampshire | 40,733 | 0.27 |
New Jersey | 1154 | 0.01 |
New York | 33,024 | 0.22 |
Pennsylvania | 12,431 | 0.08 |
Vermont | 47,501 | 0.31 |
Total | 168,887 | 1.11 |
Mixed Wood Plains Ecoregion | ||
Connecticut | 18,335 | 0.10 |
Maine | 2175 | 0.01 |
Massachusetts | 58,528 | 0.32 |
New Hampshire | 8934 | 0.05 |
New York | 42,333 | 0.23 |
Pennsylvania | 6968 | 0.04 |
Rhode Island | 9683 | 0.05 |
Vermont | 6693 | 0.04 |
Total | 153,649 | 0.84 |
Mixed Wood Plains Ecoregion | ||||
---|---|---|---|---|
NWI Wetland Type | Number of Mapped NWI Wetlands Containing Predicted GIEs | Hectares | Proportion of Predicted Groundwater-Influenced Area in Mapped NWI Wetland Type (%) | Proportion of Mapped NWI Wetlands in Ecoregion (%) |
Estuarine and Marine Deepwater | 302 | 48 | 0.03 | 0.1 |
Estuarine and Marine Wetland | 2736 | 2755 | 2 | 5 |
Freshwater Emergent Wetland | 26,429 | 24,551 | 18 | 11 |
Freshwater Forested/Scrub Wetland | 2 | 16 | 0.01 | 5 |
Freshwater Forested/Shrub Wetland | 76,959 | 105,145 | 76 | 8 |
Freshwater Pond | 9074 | 1608 | 1 | 2 |
Lake | 1283 | 712 | 0.5 | 0.1 |
Other | 283 | 900 | 1 | 10 |
Riverine | 25,722 | 2428 | 2 | 1 |
Total | 142,790 | 138,163 | 5 | |
Atlantic Highlands Ecoregion | ||||
NWI Wetland Type | Number of mapped NWI wetlands containing predicted GIEs | Hectares | Proportion of predicted groundwater-influenced area in mapped NWI wetland type (%) | Proportion of mapped NWI wetlands in Ecoregion (%) |
Estuarine and Marine Deepwater | 10 | 3 | 0.00 | 0.2 |
Estuarine and Marine Wetland 1 | 18 | 102 | 0.1 | 56 |
Freshwater Emergent Wetland | 38,200 | 29,823 | 21 | 25 |
Freshwater Forested/Shrub Wetland | 94,970 | 102,315 | 73 | 14 |
Freshwater Pond | 14,764 | 2762 | 2 | 5 |
Lake | 2505 | 1033 | 1 | 0.2 |
Other | 170 | 36 | 0.03 | 6 |
Riverine | 38,002 | 4745 | 3 | 3 |
Total | 188,639 | 140,819 | 9 |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Snyder, S.D.; Loftin, C.S.; Reeve, A.S. Predicting the Presence of Groundwater-Influenced Ecosystems in the Northeastern United States with Ensembled Models. Water 2023, 15, 4035. https://doi.org/10.3390/w15234035
Snyder SD, Loftin CS, Reeve AS. Predicting the Presence of Groundwater-Influenced Ecosystems in the Northeastern United States with Ensembled Models. Water. 2023; 15(23):4035. https://doi.org/10.3390/w15234035
Chicago/Turabian StyleSnyder, Shawn D., Cynthia S. Loftin, and Andrew S. Reeve. 2023. "Predicting the Presence of Groundwater-Influenced Ecosystems in the Northeastern United States with Ensembled Models" Water 15, no. 23: 4035. https://doi.org/10.3390/w15234035