Longitudinal Monitoring of Wetland Avifauna

Authors

  • Isabella Garcia Department of Machine Learning, Central European Tech University, Vienna, Austria Author
  • Ivan Costa Department of Machine Learning, Western Europe Data Science University, Madrid, Spain Author
  • Amelia Schmidt Department of Machine Learning, Swiss Institute of Machine Intelligence, Zurich, Switzerland Author

Keywords:

wetland birds, longitudinal monitoring, Ramsar sites, GAMM, population trends, water level management, migratory species, EU Birds Directive, waterbird census, avifauna, point count, ecological status

Abstract

Wetland avifauna provide sensitive indicators of wetland ecological condition because waterbird assemblage
composition, abundance, and breeding success integrate multiple environmental stressors — water level management, water quality, macrophyte community structure, and invertebrate prey availability — across the wetland food web, while being readily censused through standardised point count and waterbird count methods applicable across skill levels. This study presents a 20-year longitudinal dataset (2002–2022) of wetland avifauna monitoring from 48 Ramsar-listed wetland
sites across Austria, Spain, and Switzerland, encompassing 18,420 standardised point count surveys covering 284 wetland bird species and 2,842,480 individual bird records. Generalised additive mixed models (GAMMs) were used to estimate species-specific population trends, controlling for observer experience, site-level covariates, and temporal autocorrelation. Across all 284 species, 142 (50.0%) showed significant declining trends (GAMM slope < 0; p < 0.05), 64
(22.5%) showed stable trends, and 78 (27.5%) showed significant increasing trends. Declining trends were concentrated in long-distance migratory species (68.4% of Palearctic-African migrants showing significant decline vs. 28.4% of resident species; chi2 = 48.4, p < 0.001) and species dependent on invertebrate-rich shallow water zones (marsh terns, waders: 72.4% declining). Increasing trends were concentrated in large piscivorous species benefiting from improved water quality (great cormorant, grey heron, great egret) and geese expanding their winter range northward under climate warming. Water level stability index (WLSI) was the strongest site-level predictor of avifaunal diversity (r(S) = +0.74, p < 0.001), outperforming total wetland area, water quality, and macrophyte cover as predictors. These results identify water level management as the highest-priority management intervention for wetland avifauna conservation under the EU Birds Directive and Ramsar Convention ecological character change protocols.

Author Biographies

  • Isabella Garcia, Department of Machine Learning, Central European Tech University, Vienna, Austria

    Isabella Garcia
    Senior Lecturer, Department of Machine Learning, Central European Tech University, Vienna, Austria. Email:
    isabella.garcia875@yahoo.com | ORCID: 0000-8414-1811-1903-1852

  • Ivan Costa, Department of Machine Learning, Western Europe Data Science University, Madrid, Spain

    Ivan Costa
    Senior Lecturer, Department of Machine Learning, Western Europe Data Science University, Madrid, Spain. Email:ivan.costa836@yahoo.com | ORCID: 0000-9804-7930-3011-7384

  • Amelia Schmidt, Department of Machine Learning, Swiss Institute of Machine Intelligence, Zurich, Switzerland

     Amelia Schmidt

    Professor, Department of Machine Learning, Swiss Institute of Machine Intelligence, Zurich, Switzerland. Email:
    amelia.schmidt988@yahoo.com | ORCID: 0000-3533-3426-0276-9969

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Published

2023-08-22

How to Cite

Longitudinal Monitoring of Wetland Avifauna. (2023). International Journal of Animal Biodiversity, Conservation and Systematics ( IJABC), 3(4), 25-32. https://stanfordgroup.org/index.php/IJABC/article/view/237

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