Key Environmental Predictors as Drivers of Avifauna Assemblages in Ghana’s Coastal Ecosystems

doi:10.21203/rs.3.rs-5020028/v1

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Key Environmental Predictors as Drivers of Avifauna Assemblages in Ghana’s Coastal Ecosystems

https://doi.org/10.21203/rs.3.rs-5020028/v1

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Avian assemblages are not only influenced by spatial factors but also by temporal variations in environmental conditions, creating dynamic patterns of bird presence in a given area. This study examined environmental factors affecting bird assemblages in Ghana's coastal ecosystems. We conducted point transect sampling along a 15-km coastal stretch, analyzing bird abundance, richness, and diversity in relation to microclimatic variables and coastal boundary types. Cloud cover had a significant positive relationship with bird abundance (β = 0.02, p < 0.01), while time of day morning had a negative effect (β = -2.23, p < 0.001). Coastal boundary types significantly influenced bird richness and diversity. Lagoon bounded areas had a positive effect on richness (β = 0.23, p < 0.001) and diversity (β = 0.10, p < 0.01), while human settlement, and highway bounded areas had negative effects (β = -0.12, p = 0.026) and (β = -0.18, p = 0.016) respectively on bird richness. Indicator Species Analysis identified 19 species significantly associated with specific coastal boundary categories, with 11 species linked to lagoon bounded areas. These results show the relationship between environmental factors and avian assemblages in coastal ecosystems, highlighting impacts of human disturbance and climate-related factors.

coastal ecosystems

avian assemblages

environmental factors

Coastal ecosystems and their surrounding habitats significantly influence avian communities, with various bird species utilizing these areas in distinct ways. Some birds, such as herons and egrets, roost in coastal areas but primarily forage in estuaries. Others, including pelicans, terns, and cormorants, exploit intertidal rocky shores or open ocean waters for feeding [1]. A subset of coastal birds specializes in feeding on intertidal invertebrates exposed during low tide, adapting their distribution patterns to tidal rhythm, thereby, feeding at low tide and roosting at high tide [2]. The composition of coastal bird communities is also further shaped by anthropogenic and environmental factors. For instance, proximity to human settlements can increase crow populations, while the presence of nearby estuaries and lagoons tends to enhance overall bird diversity and abundance [3].

Seasonal variations and microclimate changes in coastal ecosystems and their adjacent environments significantly influence avian abundance and distribution patterns [3]. These temporal fluctuations can induce alterations in migratory behaviors [4], population sizes [5], and spatial distributions [6, 7]. When investigating avian ecology, temporal variations are crucial factors to consider, as they often account for the majority of observed variability in species richness and abundance [3, 8]. These temporal dynamics have broader implications, contributing to our understanding of avian community structure [9] and specific phenomena such as migratory patterns [10].

Coastal ecosystems in Ghana host a diverse avifauna which have been documented by several studies [11, 12], yet how temporal variations and other environmental predictors shape these avian community compositions remains unclear. Most research on avian species in coastal habitats in Ghana were to some extent conducted in wetland areas leaving a gap in the understanding of what environmental factors determine bird community and assemblage structure in the entire marine ecosystem and its surrounding habitats. This paper examined the effects of coastal surroundings and microclimatic variables on bird assemblages along the coast of Ghana's Central Region. Understanding relationships between habitat features, microclimate and bird community structure provides key knowledge into the drivers of avian distribution patterns in coastal systems. Findings of this study will aid in conservation efforts by identifying significant bird habitats in coastal Ghana and their population drivers. On a broader scale, this work contributes to knowledge of avian responses to environmental structure and variations in microclimate in vulnerable tropical coastal ecosystems worldwide.

2.1 Study Area

Ghana's coastline serves as a critical habitat for some terrestrial and water birds, including migratory species. Situated at the intersection of the East Atlantic and Mediterranean flyways, this stretch offers vital feeding, roosting, and nesting grounds [13, 14]. The coast features diverse habitats including mudflats, lagoons, beaches, and mangrove stands [15, 16]. Our study focused on a 15 km transect within this rich ecological zone, streching from the Cape Coast Metropolitan (5°17′32′′ N, 1°29′87′′ W) to the Komenda-Edina-Eguafo-Abirem Municipal (5°12′07′′ N, 1°46′28′′ W) Assemblies in Ghana's Central Region (Fig. 1).

2.2 Experimental Design

Using point transect sampling (Bibby, 2000), 30 points were established 500 m apart to minimize double counting [16]. At each point, birds within a 100-m radius were identified visually and aurally. Surveys were replicated in both wet (June, July, September) and dry (December, January, February) seasons, during morning (0600h-0900h) and evening (1500h-1800h) windows. This replication helped assess detection biases due to temporal variations in bird activity [17]. Incidental species were recorded but excluded from statistical analysis.

2.3 Bird Survey

Birds were observed for 10 minutes within a 100 m radius at each point, following standard protocols [18]. All visually or audibly detected species were recorded and counted. Visual identification utilized Focus 8×42 binoculars and a Western African bird field guide [19].

2.4 Microclimatic and Environmental Parameters

Prior to each bird survey, we recorded microclimatic variables including humidity, cloud cover, wind speed, and air temperature. These data were obtained from the Ghana Meteorological Service. Tidal level data were retrieved from www.worldtides.info to account for the influence of tidal cycles on bird distributions. To investigate the disparity between avian assemblages in areas of varying human disturbance, we selected coastal sites representing a disturbance gradient (Fig. 2). Study areas were categorized into two main types: 1. Highly disturbed sites: Coastal areas bounded by human settlements and highway roads 2. Less disturbed sites: Coastal areas bordered by natural vegetation (grass and wooded vegetation), estuaries, or lagoons.

2.5 Data Exploration and Statistical Analysis

All data were organized in Microsoft Excel 2019 and imported to R statistical software (version 4.3.3) for analysis. Bird species diversity, richness and abundance were calculated using the package “vegan” [20]. Shapiro Wilks test was used to check for normality of the response variables (abundance, richness, and diversity). The data were not normally distributed therefore, a GLM model with the quasi-family distribution was used for the analysis. A multicollinearity test was conducted on all explanatory variables to eliminate strongly correlated variables. The main predictors were the coast boundary and season while other covariates were air temperature, wind speed, cloud cover, humidity, time of day, tidal level. Using stepwise deletion, explanatory variables that had no significant relationship with the response variables were dropped from the model. Model residual plots from regression models were used to check whether all model assumptions were met. Indicator Species Analysis (ISA) in the “indicspecies” package [21] was used to determine species that were strongly associated with a particular coast boundary category.

3.1 Bird Abundance

We fitted a general linear model (quasi family, identity link) to predict the effect of the measured variables on bird abundance. The model was estimated using maximum likelihood. The model's Nagelkerke's R2 = 0.95. The model’s intercept, corresponding to cloud cover = 0, humidity = 0, wind speed = 0, time of day = evening and coast boundary = estuary, was positive at β = 10.87, 95% CI (6.34, 15.40), t (1175) = 4.70, p < 0.001. The effect of cloud cover is statistically significant and positive at β = 0.02, 95% CI (6.99e-03, 0.04), t (1175) = 2.82, p < 0.01 (Fig. 3a). The effect of humidity is statistically marginally significant and negative at β = -0.05, 95% CI (-0.09, 1.06e-03), t (1175) = -1.92, p = 0.056 (Fig. 3b). The effect of wind speed is statistically significant and negative at β = -0.08, 95% CI (-0.15, -1.97e-03), t (1175) = -2.01, p = 0.045 (Fig. 3c). The effect of time of day (morning) is statistically significant and negative at β = -2.23, 95% CI (-3.44, -1.03), t (1175) = -3.63, p < 0.001 (Fig. 3d). The effect of coast boundary (wooded vegetation) is statistically marginally significant and positive at β = 2.34, 95% CI (-0.09, 4.77), t (1175) = 1.89, p = 0.059 (Fig. 4).

3.2 Bird Richness

We fitted a general linear model (quasi family with, identity link) to predict Richness with tidal level, time of day, and coast boundary. The model was estimated using maximum likelihood. The model's Nagelkerke's R2 = 0.07. The model's intercept, corresponding to time of day = evening, tidal level = high and coast boundary = estuary, was at β = 1.15, 95% CI (1.04, 1.27), t (1177) = 20.07, p < 0.001). The effect of time of day (morning) is statistically significant and positive at β = 0.04, 95% CI (2.14e-04, 0.09), t (1177) = 1.97, p = 0.049 (Fig. 5a). The effect of tidal level (low) is statistically marginally significant and positive at β = 0.04, 95% CI (-2.60e-03, 0.09), t (1177) = 1.85, p = 0.064 (Fig. 5b). The effect of coast boundary (grass vegetation) is statistically significant and negative at β = -0.19, 95% CI of (-0.32, -0.06), t (1177) = -2.81, p < 0.01 (Fig. 6). The effect of coast boundary (highway) is statistically significant and negative at β = -0.18, 95% CI (-0.33, -0.03), t (1177) = -2.42, p = 0.016 (Fig. 6). The effect of coast boundary (human settlement) is statistically significant and negative at β = -0.12, 95% CI (-0.23, -0.01), t (1177) = -2.22, p = 0.026 (Fig. 6). The effect of coast boundary (lagoon) is statistically significant and positive at β = 0.23, 95% CI (0.10, 0.37), t (1177) = 3.39, p < 0.001 (Fig. 6). The effect of coast boundary (wooded vegetation) is statistically significant and negative at β = -0.15, 95% CI (-0.27, -0.04), t (1177) = -2.71, p < 0.01 (Fig. 6).

3.3 Bird Diversity

We fitted a general linear model (quasi family, identity link) to predict Diversity with tidal level and time of day coast boundary. The model was estimated using maximum likelihood. The model's Nagelkerke's R2 = 0.05. The model's intercept, corresponding to time of day = evening, tidal level = high and coast boundary = estuary, was at β = 0.08, 95% CI of (0.02, 0.14), t (1177) = 2.59, p = 0.010). The effect of time of day (morning) is statistically marginally significant and positive β = 0.02, 95% CI (-7.05e-04, 0.04), t (1177) = 1.90, p = 0.058 (Fig. 7a). The effect of tidal level (low) is statistically marginally significant and positive at β = 0.02, 95% CI (-7.27e-05, 0.05), t (1177) = 1.95, p = 0.051 (Fig. 7b). The effect of coast boundary (grass vegetation) is statistically significant and negative at β = -0.09, 95% CI (-0.16, -0.02), t (1177) = -2.62, p < 0.01 (Fig. 8). The effect of coast boundary (highway) is statistically significant and negative at β = -0.10, 95% CI (-0.18, -0.02), t (1177) = -2.57, p = 0.01 (Fig. 8). The effect of coast boundary (human settlement) is statistically significant and negative at β = -0.06, 95% CI (-0.11, -4.59e-04), t (1177) = -1.98, p = 0.048 (Fig. 8). The effect of coast boundary (lagoon) is statistically significant and positive at β = 0.10, at a 95% CI of (0.03, 0.17), t (1177) = 2.71, p < 0.01 (Fig. 8). The effect of coast boundary (wooded vegetation) is statistically significant and negative at β = -0.08, 95% CI (-0.13, -0.02), t (1177) = -2.53, p = 0.01 (Fig. 8).

3.4 Bird Species Associated with The Various Coast Boundary Categories

Indicator species analysis (ISA) of the birds’ association with the coast boundary categories, indicated that some species were significantly associated with specific coast boundary categories and others were associated with multiple categories (Table 1). Among the 64 species tested; 19 species were significantly associated with different substrate categories (Table 1). Eleven species were identified as significantly associated with lagoon bounded coastal areas, two species were significantly associated with estuary bounded coastal zones, and one species was significantly associated each with grass vegetation and wooded vegetation areas and two species were associated with both estuary bounded and lagoon bounded coastal areas (Table 1).

Table 1

Results of Indicator Species Analysis showing bird association with different categories of coast boundaries in the Central Region of Ghana
Coast Boundary Category	Species Common Name	Species Scientific Name	Test Statistic	P-value
Estuary	Little Swift	Apus affinis	0.67	< 0.01 **
	Eurasian Curlew	Numenius arquata	0.53	0.04 *
Grass Vegetation	Bronze Mannikin	Spermetes cucullata	0.64	< 0.01 **
Highway	Curlew Sandpiper	Calidrius ferruginea	0.63	< 0.01 **
Estuary + Lagoon	Common Sandpiper	Actitis hypoleucos	0.87	< 0.001 ***
	Wood Sandpiper	Tringa glareola	0.63	< 0.01 **
Wooded Vegetation	Village Weaver	Ploceus cucullatus	0.75	< 0.01**
Highway + Lagoon	Long-tailed Cormorant	Microcarbo africanus	0.52	0.03 *
Lagoon	Black-winged Stilt	Himantopus himantopus	0.97	< 0.001 ***
	Spur-winged Lapwing	Vanellus spinosus	0.93	< 0.001 ***
	Marsh Sandpiper	Tringa stagnatilis	0.91	< 0.001 ***
	Woodland Kingfisher	Halcyon senegalensis	0.91	< 0.001 ***
	Ethiopian Swallow	Hirundo aethiopica	0.90	< 0.001 ***
	Western-reef Heron	Egretta gularis	0.87	< 0.001 ***
	Cattle Egret	Bubulcus ibis	0.86	< 0.001 ***
	Little Egret	Egretta garzetta	0.74	< 0.01 **
	Squacco Heron	Ardeola ralloides	0.74	< 0.01 **
	African Jacana	Actophilornis africanus	0.69	< 0.01 **
	Red-eyed Dove	Streptopelia semitorquata	0.66	< 0.01 **

Our analysis revealed the patterns of bird abundance, richness, and diversity in response to various key environmental predictors [22]. Findings include the role of microclimatic variables on avian assemblage [23]. The differential impacts of coastal boundary types on avian communities [3], tidal cycles in shaping bird diversity [24, 25] among other factors that influence bird assemblage in the studied coastal habitats. These findings are important in the face of the ever-changing climate and intense anthropogenic activities being faced by coastal ecosystems in Ghana and on broader perspective, globally [7, 23, 26]. Tropical coastal ecosystems like the ones in Ghana are undergoing rapid degradation caused by anthropogenic climate change and sea level rise [27, 28], thereby threatening their ecological integrity which includes the bird species they support.

The significant positive association between cloud cover and bird abundance highlights the interactions between avian assemblage, microclimate, and coastal environmental dynamics. This relationship, corroborated by Tris et al. [29] in Mediterranean coastal systems, suggests a nuanced adaptation of coastal birds to local environmental conditions. Cloud cover may modulate solar radiation, creating a more favorable thermal environment for birds, particularly during peak daylight hours. This thermal buffering effect could be especially crucial in tropical and subtropical coastal areas where heat stress is a significant physiological challenge. For instance, Oswald et al. [30] demonstrated that even small changes in ambient temperature could significantly affect the foraging efficiency and energy budgets of coastal waders. In the context of global climate change, this association raises questions about the potential impacts of altered cloud patterns on coastal bird populations. Understanding these relationships can inform predictive models of species distribution and abundance under various climate scenarios.

The significant negative effect associated with morning observations in coastal bird abundance could represent a multifaceted phenomenon arising from complex ecological, behavioral, and environmental factors. Heightened predation risk from coastal raptors during early hours may drive prey species to adopt antipredator strategies, reducing their visibility [31]. Anthropogenic influences, particularly increased human activity in coastal areas during mornings, may further shape bird behavior and distribution patterns [31, 32]. This adaptability may prove crucial as these species face mounting challenges from increased human disturbance. The observed negative effect associated with morning observations in the coastal bird communities serves as a reminder of the balance these species must strike between safety, resource acquisition, and environmental constraints.

The differential effects of coastal boundary types on bird richness provides evidence into habitat-biodiversity relationships. The negative effect of coast boundary bounded by human settlements is a clear indication of the effect of anthropogenic expansions on bird assemblage contrasted with the positive effect of lagoons, which signifies the importance of pristine and less disturbed habitats in maintaining avian biodiversity. These findings align with recent landscape ecology research. For example, Hou et al. [33] employed high-resolution remote sensing data to demonstrate that coastal habitat complexity is a strong predictor of bird species richness. Furthermore, a study by Lafferty [3] revealed the effect of anthropogenic disturbance on avian communities, confirming the negative impact of anthropogenic boundaries (e.g., human settlements) on avian diversity and emphasizing the conservation value of natural coastal features like lagoons.

The marginally significant positive effect of low tide on bird diversity contributes to our understanding of tidal ecology in shaping avian communities. This might be due to the recent level rises along the coast of Ghana resulting into in the construction of sea defense along a significant stretch of Ghana’s coast altering the habitat in the process. However, a work by Fonseca et al. [25], showed that tidal cycles significantly influence the foraging patterns and habitat use of multiple shorebird species. Moreover, a study also by Burton et al. [24] also provided evidence, concluding that tidal regimes play a crucial role in structuring coastal bird communities by mediating access to foraging areas and influencing prey availability. This suggests need for integrated approaches to coastal management that consider both natural processes and human interventions, to inform effective conservation strategies in the face of global environmental change.

The Indicator Species Analysis (ISA) results, identifying 19 species significantly associated with specific coastal boundary categories (11 with lagoon-bounded areas), confirms the ecological significance of certain habitat types for coastal birds. This finding agrees with research conducted by Bellefontaine and Hamilton [34] on habitat specialization and niche partitioning in coastal avian communities. These findings have important implications for coastal conservation and management strategies. First, they highlight the need for targeted protection of lagoon-bounded areas to preserve critical habitats for specialized bird species. The results also suggest that alterations to coastal geomorphology, whether through natural processes or human intervention, could have significant impacts on avian community composition and distribution patterns.

This study offers compelling evidence of the dynamics shaping coastal avian communities in Ghana, with broader implications for tropical coastal ecosystems worldwide. Our findings demonstrate the relationships between bird assemblages and various environmental factors, including cloud cover, humidity, wind speed, time of day, coastal boundary types, and tidal cycles. As coastal ecosystems face mounting pressures from climate change, sea-level rise, and anthropogenic activities, these findings serve as a crucial foundation for informed conservation and management decisions. Moving forward, it is important that we adopt integrated approaches to coastal management that balance human needs with the preservation of ecological integrity. By doing so, we can work towards the conservation of this vital ecosystems and the rich avian biodiversity they support.

AUTHOR CONTRIBUTIONS

SAMUEL ETORNAM TAMEKLOE

Conceptualization-Lead, Data Curation-Lead, Formal Analysis-Lead, Funding Acquisition-Lead, Investigation-Lead, Methodology-Lead, Software-Lead, Visualization-Lead, Writing – original draft-Lead, Writing – review & editing-Lead

KWEKU ANSAH-MONNEY

Conceptualization- Supporting, Supervision- Supporting, Writing – original draft- Supporting, Writing – review & editing- Supporting, Supervision - Supporting

JUSTUS PRECIOUS DEIKUMAH

Conceptualization-Lead, Supervision-Lead, Writing – original draft-Lead, Writing – review &

editing-Lead, Supervision – Lead

STATEMENTS AND DECLARATIONS

Funding

This study was funded by the A.P. Leventis Ornithological Research Institute (APLORI).

ACKNOWLEDGEMENTS

We would like to thank the A.P. Leventis Ornithological Research Institute (APLORI) for funding

this research. Mr. Samuel Bridge Nkansah for his enormous contribution during the sampling

period as a field assistant.

ETHICS APPROVAL

No approval of research ethics committees was required to accomplish the goals of this study because the work was conducted entirely in the field and did not require trapping of any bird species.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

DATA AVAILABILTY

Data analysis was conducted using R version 4.3.3 and associated open-source packages. The datasets analyzed for this research and the complete annotated R codes (not novel) used for data processing, visualization, and statistical modelling will be made available on the Figshare data repository upon acceptance.

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Key Environmental Predictors as Drivers of Avifauna Assemblages in Ghana’s Coastal Ecosystems

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Abstract

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1.0 INTRODUCTION

2.0 MATERIALS AND METHODS

2.1 Study Area

2.2 Experimental Design

2.3 Bird Survey

2.4 Microclimatic and Environmental Parameters

2.5 Data Exploration and Statistical Analysis

3.0 RESULTS

3.1 Bird Abundance

3.2 Bird Richness

3.3 Bird Diversity

3.4 Bird Species Associated with The Various Coast Boundary Categories

4.0 DISCUSSIONS

5.0 CONCLUSIONS AND RECOMMENDATIONS

Declarations

References

Additional Declarations

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