Influence of Land Use and Land Cover Change on the Distribution and Foraging Ecology of Grey Crowned Cranes (Balearica regulorum gibbericeps) in the Rushebeya–Kanyabaha Wetland, Southwestern Uganda

Simple Summary

The study evaluated how changes in land use and land cover (LULC) affected the distribution and foraging ecology of Grey Crowned Cranes (Balearica regulorum gibbericeps) in the Rushebeya–Kanyabaha wetland watershed between 1986 and 2022. While subsistence farms and built-up areas have increased over the years, remote sensing studies have shown notable decreases in grassland, bushland, and wetland cover. Between 2010 and 2022, woodlots showed signs of recovery, although they also varied. According to field surveys, Grey Crowned Cranes mostly depend on subsistence farms for foraging, particularly in fields of sorghum, beans, and potatoes, but they prefer wetlands for breeding and roosting. Nest placement was influenced by vegetation height and closeness to water, and nesting sites were clustered close to wetland edges.

Abstract

This study examined the distribution and feeding ecology of Grey Crowned Cranes (GCCs) (Balearica regulorum gibbericeps) in the Rushebeya–Kanyabaha wetland watershed in southwestern Uganda, focusing on changes in land use and land cover (LULC) between 1986 and 2022. We documented crane distribution and foraging behaviors through field surveys and analyzed Landsat data of 1986, 1998, 2010, and 2022 using supervised classification. The findings revealed significant changes in LULC, with an increase in built-up areas and subsistence farms, while grassland, bushland, and wetland coverage steadily declined. As the human population increased, leading to a demand for food, subsistence farming emerged as the predominant land use starting in 1998. Data on crane distribution indicates that wetlands are a vital habitat for roosting and breeding; nests are typically located within 140 m of water, along the edges of wetlands, and in vegetation that averages 2.6 m in height. Subsistence farmland, primarily growing beans, potatoes, and sorghum, serves as a key food source for the cranes. The study highlights that while agricultural landscapes provide important foraging sites, crane populations are at risk due to ongoing habitat degradation and disturbances. To effectively conserve these populations, strategies that integrate sustainable land use planning within the catchment area and wetland protection will be essential.

1. Introduction

Land use pressure continues to rise globally, and due to the connection between ecosystems and human livelihoods, there are likely to be more negative effects of Land use/land cover changes (LULCs) in the future [1]. Understanding past, present, and future LULC patterns and trends is crucial in guiding current and future responsible natural resource use [2].

Cranes (Aves: Gruidae) are large, long-lived birds that depend on open, shallow wetlands and grassland habitats for their foraging, breeding, and roosting needs. Their diet includes tubers, seeds, seedlings, invertebrates, amphibians, and small vertebrates [3]. Historically, these birds inhabited a variety of wetland and grassland habitats, including marshes, savannas, and wet meadows [4]. Cranes coexisted alongside low-intensity, small-scale farming methods that preserved access to natural food sources in many areas. Cranes now have few food options and must rely more on farmed crops due to the recent growth of intensive agriculture, which has simplified croplands, decreased natural habitat patches, and increased chemical inputs [5].

The global population of this species is currently estimated at approximately 30,200–36,900 individuals and continues to decline due to habitat loss, agricultural expansion, urbanization, and wetland degradation [6,7]. The population of GCCs has faced various threats due to human activities and land use changes [6,8]. The species’ high risk of extinction if current trends continue is reflected in its listing as Endangered on the IUCN Red List of Threatened Species [7,9]. Although it has a high dependence on agricultural lands for food, it nevertheless uses shallow wetlands for roosting and breeding, establishing a dynamic relationship between natural and human-modified ecosystems [10]. GCCs exhibit a predominantly sedentary pattern, with individuals maintaining year-round territories within wetland-grasslands in East Africa [11,12,13,14,15]. Breeding activity typically coincides with periods of increased rainfall, with nesting initiation commonly reported between March and July, followed by chick rearing during the latter part of the wet season [16]. Pairs construct nests within shallow wetlands where water levels offer both protection and stable foraging conditions for adults and chicks [13].

To understand feeding preference for birds, central place foraging theory offers a framework for understanding how animals that repeatedly go back to a fixed location, like a nest or roost, allocate their foraging effort to maximize net energy gain while balancing travel costs and resource availability acquisition [17]. Under this framework, an individual’s likelihood of choosing a foraging site generally decreases as the distance from the central place increases, and selectivity for higher-quality resources may rise with distance to compensate for travel costs. This theoretical perspective has been successfully applied in bird studies, including research on cranes, where foraging site choice reflects a trade-off between energy gain and distance from the central location used for resting or nesting [18]. Applying central place foraging theory to Grey Crowned Cranes in the Rushebeya–Kanyabaha wetland, this study connects behavioural ecology with spatial patterns of habitat use, offering insights into how environmental change may affect foraging decisions in GCCs.

Rushebeya–Kanyabaha wetland has undergone human-induced land use/cover changes, mainly due to the expansion of agricultural land. The increasing human population around the wetland is driving the need for crop cultivation and the socio-economic development of the area [8]. The land use/cover transformations in the catchment areas have had a negative environmental impact, including increased surface runoff and sediment deposition from the adjacent uplands, which leads to increased nutrient loading from crop fields and flooding in the lowlands [19].

Little is known about the distribution and foraging habits of GCCs in the southwestern part of Uganda, despite the wetland’s ecological significance not only to this species but also to other biodiversity. By assessing LULC variations in the Rushebeya–Kanyabaha watershed for the years 1986, 1998, 2010, and 2022 and investigating how these changes affected GCC distribution and feeding ecology, this study addresses this knowledge gap. This study advances our understanding of how the ecological processes of wetland-dependent species, such as GCCs, are influenced by changes in human-dominated landscapes. This study highlights the interactions between natural habitats and modified natural ecosystems by linking behavioral ecology with spatial patterns of land-use change. This information is crucial for implementing conservation and habitat management plans that maintain biodiversity in the face of progressive environmental change.

2. Materials and Methods

2.1. Study Area

Covering an area of 33 km², the Rushebeya–Kanyabaha wetland is situated in Rukiga District, southwestern Uganda (Figure 1). It is in the western Rift Valley of East Africa, with an average elevation of approximately 2000 m above sea level [20]. The wetland extends from Muhanga to Kanyabugunga, crossing four sub-counties. The undisturbed part of the wetland has a mixture of papyrus and wetland grasses, and it is heavily waterlogged. The Rushebeya–Kanyabaha wetland catchment has a humid subtropical climate, typical of southwestern Uganda. This climate features relatively steady temperatures all year round, with moderate to high rainfall [20].

The dominant natural vegetation in the wetland includes Cyperus papyrus, Cladium mariscus, and Cyperus dives, with patches of Miscanthidium violaceum and Typha spp. [21,22]. The wetland is extensively encroached upon and bordered by human settlements and subsistence farmland, with crops such as maize, sorghum, peas, potatoes, beans, cabbage, and scattered fallow fields [23].

2.2. Image Acquisition and Analysis

Two data types were acquired and utilized in this study: the Rushebeya–Kanyabaha wetland catchment administrative shapefiles and Landsat satellite imagery for the four years (1986, 1998, 2010, and 2022) over a 36-year period to illustrate land cover dynamics. The study site’s catchment was delineated from a Digital Elevation Model (DEM) using the ArcSWAT extension embedded in ArcGIS software. Primary data was obtained from Landsat satellite imagery. Landsat TM/ETM and OLI satellite image datasets from 1986, 1998, 2010, and 2022, which are freely accessible and have limited cloud coverage (0–10%) in the study area, were selected for use in this study.

Spectral bands 3, 2, and 1 for 1986, 1998, and 2010, and bands 4, 3, and 2 for the 2022 image set, were stacked in ArcGIS 10.8. The identified Landsat scenes from 1986, 1998, 2010, and 2022 were downloaded, processed, and analysed [24]. The satellite images were downloaded from the USGS-Earth Explorer website (https://earthexplorer.usgs.gov/) on 13 April 2023 for use in the spatial–temporal land/use cover classification and analysis.

All images were taken during the dry seasons of the respective years because their surface features displayed consistent reflectance properties [25]. The image acquisition and specifications are shown in

Table 1. Specifications of Landsat image used in this study.

Year	Satellite/Sensor	Date	Path	Row	Band No.	Resolution (m)
1986	Landsat TM	19 July 1984	173	061	3,2,1	30
1998	Landsat TM/ETM+	9 November 1998	173	061	3,2,1	30
2010	Landsat TM/ETM+	26 January 2010	173	061	3,2,1	30
2022	Landsat OLI/TIS	14 July 2022	173	061	4,3,2	30

.

2.3. Image Processing and Classification

Landsat TM/ETM images were resampled to 30 m spatial resolution before analysis. The resampled images (30 m) were then atmospherically corrected using the Dark Object Subtraction method to minimize the effects of atmospheric distortion on the sensor. This method identifies and removes dark pixel values. Linear contrast stretching was applied to the 1986, 1998, and 2010 Landsat images due to low contrast and cloud cover. This process enhanced the image contrast and improved visual quality. The 1998 Landsat image was further refined to remove clouds by applying raster functions to eliminate clouds and shadows from surface reflectance data within a single image using ArcGIS.

After processing the Landsat images, the identification of different land cover/use classes was carried out, using visual features such as texture, tone, and effect zones to distinguish classes, i.e., subsistence farmland, wetland, built-up areas, grassland, bushland, woodlots, and others [26]. Land use types were classified using the supervised maximum likelihood method because it is one of the most widely used techniques in scientific literature, as well as being the fastest, easiest to use, and providing a clear interpretation of the results [26,27,28].

Training area numbers and percentages were identified to classify several training and test areas. These results were compared with supporting ground-truth data. A spectral signature, a statistical file created by the image processing software for each class, was used. Each pixel was assigned to the most likely class using the maximum likelihood algorithm, which assigns each pixel to the spectral class with the highest probability density function for the multispectral values. Land cover types were later classified into the following six main classes: subsistence farmland, wetlands, built-up areas, grasslands, bushlands, and woodlots (

Table 2. Details of delineated classes of LULC.

Class Name	Description
Built-up areas	Community service areas (parks, playing grounds, lorry parks), residential areas, and commercial and industrial areas are classified as built-up areas. Lands that have been cleared in readiness for building construction are also classified as built-up areas.
Bushland	Remnants of disturbed vegetation that is not cultivated, but with trees, shrubs, and other vegetation
Grassland	Area in which the vegetation is dominated by a nearly continuous cover of grasses.
Subsistence farmland	Farming land is done on a small scale for home consumption, with a little surplus for sale.
Wetland	The area where the water table is near or above the land surface for prolonged periods of the year
Woodlots	A dense area of land with sparingly scattered small trees

).

2.4. Validation and Accuracy Assessment

Auxiliary data was collected, including ground-truth data and topographic maps. The ground-truth data comprised reference points obtained using a Geographical Positioning System for the 2022 image analysis. Three hundred fifty points were gathered via GPS and Google Earth from various land use and cover types and used as reference points to develop image error matrices. Overall accuracy, producers’ and users’ accuracy, and Fleiss’ Kappa coefficient (κ), were calculated from error matrices. An overall accuracy of over 83.8% was achieved for the images from 1986, 1998, 2010, and 2022, with Fleiss’ Kappa coefficient indicating highly dependable accuracy (i.e., almost perfect) values of 0.86, 0.8, 0.78, and 0.81 for the years, respectively.

2.5. Cranes Survey Methods

Fixed-route surveys, opportunistic sightings, breeding surveys, and flocking surveys were all employed in the study. Fixed route counts involved frequent travelling along predetermined transects along existing roads. Information on crane presence was gathered through opportunistic observations and random records collected during fieldwork outside established survey routes. Breeding performance was assessed through systematic observations of territorial pairs, nesting attempts, and chick survival in breeding surveys focused on known or potential nesting sites. Flocking surveys targeted aggregations at foraging or roosting sites, where group size and age distribution counts provided insights into social behavior and population dynamics.

The surveys were carried out during the main period of flocking for cranes, from mid-August to the beginning of October 2022 [12,15,29,30]. The surveys were conducted at 40 locations within the Rushebeya wetland catchment, encompassing various land-use types. They covered the daily flight distance from the nearest roost sites to the foraging and breeding sites, as identified through fixed routes and monthly sightings monitoring surveys, as well as breeding surveys conducted during the breeding season.

We counted the number of cranes on all fields within sight at each location using a pair of 8 × 10 binoculars. The survey sites were divided into four fixed routes of varying lengths, i.e., Ruhonwa–Kabimbiri–Rushebeya–Kantare–Kanyabugunga, 33 km; Kamusiza–Kitunga–Burime–Kabimbiri, 16 km; Rushebeya–Nyakarambi–Nyarurambi–Nyakakyera, 14 km; and Bukinda–Nyakasiru–Ryabirengye–Nyaruhanga, 12 km. These routes were surveyed three times a week, with one route assigned each day. Since the 33 km route was longer, it was conducted on one day, while the shorter 16 km, 14 km, and 12 km routes were completed on the remaining two days. Surveys ran from dawn to dusk, starting consistently at 8:00 a.m.; however, survey time ended depending on the length of each route.

2.6. Field Surveys for the Foraging Patterns of Cranes

To examine a specific selection of foraging sites and food availability within the newly prepared crop fields, we surveyed 50 locations used by the cranes for crop type and spilled grain availability during the seasons of intensive activity, i.e., flocking and breeding seasons. We analysed whether the choice of foraging sites is affected by disturbance. We visually identified food availability directly in the field. We considered the distance to human settlements and disturbance as indicators of foraging site quality, demonstrating the increased likelihood of higher net energy gain with more available food and less disturbance.

We investigated whether food availability, distance to roosting sites, and human disturbance affected foraging patterns. Since cranes consistently utilized the same roosting site, we assumed that they would select fields close to roost sites and exhibit stronger selectivity for high-quality sites associated with higher net energy intake [18]. For example, the high availability of grain results in less time spent searching for food within sites and a lower risk of human disturbance in the fields, particularly with increasing distance from the roost site. Central place foraging theory [18] explained the foraging patterns in habitats differing in quality and the distance to the central place.

3. Results

3.1. Land Use/Cover Changes in Rushebeya–Kanyabaha Wetland

Results from the LULC maps derived from the classification of Landsat images over twelve years (1986–1998, 1998–2010, 2010–2022) (Figure 2) show that built-up areas, bushland, grassland, subsistence farmland, wetland, and woodlots are the dominant land use/cover types in the Rushebeya wetland catchment, with a total area coverage of 16,984 hectares in 1986, 1998, 2010, and 2022 (

Table 3. Proportion (%) change in LULC in Rushebeya–Kanyabaha wetland in different years under study.

	1986		1998		2010		2022
Land Use/Cover	Area (Ha)	%	Area (Ha)	%	Area (Ha)	%	Area (Ha)	%
Built-up area	166	1.0	202	1.2	565	3.3	967	5.7
Bushland	520	3.1	449	2.7	368	2.2	183	1.1
Grassland	9256	54.5	1365	8.1	1085	6.4	319	1.9
Subsistence farmland	298	1.8	9822	58.2	10,312	61.1	9875	58.1
Wetland	1482	8.7	842	5.0	734	4.3	594	3.5
Woodlot	5263	31.0	4203	24.9	3819	22.6	5046	29.7

).

The study indicates that in 1986, grasslands (9256 ha, occupying 54.5% of the total land area) and woodlots (5263 ha, 31%) were the most predominant land uses, followed by wetlands (1482 ha, 8.7%) (Figure 1; Table 3). In 1998, 2010, and 2022, subsistence farmland was the most dominant land cover, at 9822 Ha (57.8%), 10,312 ha (61.1%), and 9875 ha (58.1%), respectively, followed by woodlots. However, results revealed a significant reduction in major land cover types, such as grassland, bushland, wetland, and woodland, from 1998 to 2022, replaced by increasing areas of farming and built-up areas. The pattern of woodland increasing while subsistence farmland decreased between 2010 and 2022 demonstrates a landscape undergoing various changes, shifting from small-scale farmland, as farming spatially declined at a rate of 2.9%, to an increase in woodlots at 7.1% (Table 3).

3.2. Distribution Patterns of Cranes in the Rushebeya Wetland Catchment

GCCs were found to prefer wetlands during the breeding season to other land use categories. They would be seen occupying territories in the selection of nesting sites, while in pairs, during nest building, during incubation, and after hatching, with either fledglings or juveniles.

The distribution of adult and chick GCCs shows that wetland habitats are tightly linked to both age groups. While chicks were concentrated in the same areas, adult cranes were seen throughout the study area, with higher densities near Kasambya, Rwanyacucu, and Muhanga Town Council. The number of crane pairs with two to three chicks represented ranged from 2 to 3, while those with none to 1 chick ranged from 0 to 1. These locations appear to be substantial nesting and breeding grounds based on their spatial overlap (Figure 3).

GCCs were observed roosting mainly in trees near wetlands and in trees close to homesteads adjacent to the wetland. It was noted that cranes left their roost between dawn and an hour afterwards and returned around nightfall. Most breeding observations of the cranes occurred in sedges with an average height of 2.6 m along the wetland edges, primarily as paired individuals, and in cultivated fields within the wetland catchment. The nests were built at an average distance of 67.9 m from the wetland edge and 139.8 m near the water source (

Table 4. Descriptive statistics of factors that influence the location and distribution of crane nests in Rushebeya wetland.

Parameters	Mean	Min.	Max.	SE	SD
Proximity of the nest to water	139.8	10	400	34.0	220.1
Height of the vegetation	2.6	1	4	0.1	0.8
Distance of nest from the edge of the swamp	67.9	10	200	6.3	40.8

).

Breeding GCCs typically build their nests in open areas with short vegetation. The distance of these nests from water sources and the edge of wetlands varies based on local environmental conditions. On average, nests are located approximately 140 m from the water and 68 m from the edge of the wetland, with surrounding vegetation averaging a height of about 2.6 m. The placement of nests varies.

There was a weak positive correlation between vegetation height and nest distance from the wetland edge (r = 0.439), while the relationship between nest proximity to water and vegetation height was weak (r = −0.043). In contrast, the correlation between nest distance from the wetland edge and proximity to water was positive but weak (r = 0.141) (

Table 5. Pearson’s correlation matrix of proximity of the nest to water, distance of nest from the wetland edge, and vegetation height.

		Proximity of Nest to Water	Distance of Nest from Wetland Edge	Vegetation Height
Proximity of the nest to water	Pearson Correlation	1
	Sig. (2-tailed)
Distance of nest from wetland edge	Pearson Correlation	0.141	1
	Sig. (2-tailed)	0.373
Vegetation height	Pearson Correlation	−0.043	0.439 **	1
	Sig. (2-tailed)	0.788	0.004

** Correlation is significant at the 0.01 level (2-tailed).

).

A summary of crane sightings (flocking/social behavior), the number of observations of each group type, and field activities is summarized (

Table 6. Observational patterns and composition of GCCs in Rushebeya–Kanyabaha wetland.

Sightings of crane groups in the Rushebeya–Kanyabaha wetland catchment		Frequency	Percent
	single (1 crane)	6	5.5
	pair (2 cranes)	71	64.5
	family (adults & young cranes)	20	18.2
	flock (>6 cranes)	13	11.8
	Total	110	100.0
Number of Observations	Frequency of Crane Sightings per Observation Period
	0–5	97	88.2
	6–10	3	2.7
	11–20	5	4.5
	21–50	3	2.7
	>50	2	1.8
	Total	110	100.0
Activities of cranes in the fields	Grey Crowned Crane recorded Patterns of Behavior
	foraging	92	83.6
	flying	4	3.6
	resting	10	9.1
	preening	4	3.6
	Total	110	100.0

).

Most of the GCC observations were made in bean fields, representing 35.5% of all sightings (n = 39). This was followed by sweet potato fields, where cranes were seen uprooting and feeding on leftover tubers after harvest (16.3%). Irish potato fields accounted for 12.7% of observations, while sorghum gardens, where cranes foraged on insects and weed seeds, made up 8.2%. Mixed crop fields of beans and maize accounted for 5.5% of the observations. Similar proportions (4.5% each) were recorded in cabbage gardens, maize fields, and mixed gardens of beans, peas, and sorghum. Additional sightings occurred in pea gardens (1.8%) and other mixed crops of sorghum and beans (2.7% and 3.6%, respectively) (

Table 7. Crop types where GCCs were primarily found.

Crop Type Where Cranes Were Found	Frequency	Percent
sorghum	9	8.2
maize	5	4.5
sweet potatoes	18	16.3
Irish potatoes	14	12.7
beans	39	35.5
peas	2	1.8
cabbages	5	4.5
beans and peas	3	2.7
maize and beans	6	5.5
sorghum and beans	4	3.6
beans, peas, and maize	5	4.5
Total	110	100.0

).

4. Discussion

4.1. Land Use/Cover Changes in Rushebeya–Kanyabaha Wetland

Over the years studied, the LULC changes in the Rushebeya–Kanyabaha wetland catchment were primarily driven by human population pressure, agricultural expansion, and infrastructure development. Built-up land and cultivated fields have grown consistently, a trend expected to continue as the dominant feature of land use change across East Africa [31,32,33]. Our results show a significant reduction in grassland, bushland, wetland, and woodland cover between 1998 and 2022, replaced by an increase in subsistence farmland and built-up areas. In the highland setting of Rukiga, the reported decline in grassland, shrubland, wetland, and woodland cover during this period poses a serious ecological and socioeconomic issue. One of the primary causes of wetland deterioration is the degradation of natural ecosystems by harmful human activities [34]. The population density of 310/Km², fragmented habitats, and steep slopes of the highlands in southwestern Uganda increase pressure on the country’s natural resources [35,36]. While agriculture is the primary economic activity in the Rushebeya–Kanyabaha landscape, the dense population poses a threat to the wetland as people seek to engage in farming, grazing, raw materials extraction for construction and handicrafts, water for household needs, hunting, and beekeeping [21,22]. The significant reduction in land cover is likely to have serious consequences for crane populations. Cranes need a varied landscape that includes wetlands and nearby grasslands for foraging, roosting, and breeding [15,37]. Conversion of these natural habitats into subsistence farmland and built-up areas reduces the availability of key food resources for cranes and other wildlife species, while increasing exposure to human disturbance [12,38,39,40].

Agroforestry and other greening programs, government-led environmental initiatives, or household fuelwood demand are likely the primary drivers of localized restoration efforts or tree-planting activities, as indicated by the increase in woodlots between 2010 and 2022, coinciding with a slight decline in farmland. While these measures boost overall vegetation cover in the landscape, they may include non-native or fast-growing species, such as Eucalyptus sp., a tree known to consume large quantities of water and alter the hydrology of the areas where they grow [31,41,42]. Therefore, the growth of such species may worsen wetland degradation and decrease the availability of suitable foraging and breeding habitats for cranes.

4.2. Distribution and Foraging Patterns of Grey Crowned Cranes Across Land-Use/Cover Categories

GCCs occupy a mosaic of habitats in this wetland but remain dependent on the wetland’s vegetation for breeding and roosting [30,43,44]. Most nests were found in clusters near the edges of wetlands, within 140 m of open water, and in vegetation of moderate height. These patterns match crane ecology elsewhere in East Africa [12]. The nests were probably placed this way due to adaptation, which means balancing the need to avoid predators with the need to access the nests easily by adults [12,13]. Despite this dependence on wetlands, foraging activity was concentrated in subsistence farmland, particularly in fields of sorghum, beans, and potatoes. This finding aligns with the other evidence that cranes increasingly exploit cropland as natural foraging habitats decline [44]. While agricultural landscapes provide critical food resources, reliance on farmland exposes cranes to disturbance, persecution, and potential conflict with farmers [30,44]. Results showed that cranes selected foraging sites near roosts but avoided fields with frequent human activity, supporting Central place foraging theory predictions, which suggests that animals optimise energetic gain while minimising risk [45,46].

4.3. Social Behaviour and Activities of the Grey Crowned Crane

The findings of this study clearly show that cranes use the study area (wetlands) to meet their breeding and foraging needs. This is supported by the observation of 65% and 5% of cranes being families and singletons, respectively. Further, 88% of all the observations were crane families. Nearly 2% of crane observations were flocks, which may be because of the rugged and hilly landscape dominated by human settlements and croplands, which limit the existence of open, flat areas that cranes often prefer for flocking, or because the croplands are in constant use. A study conducted in a hilly–flat landscape in the central highlands of Kenya noted that cranes avoided flying over the hillsides and would use sections along valleys to move between foraging sites [47].

5. Conclusions and Recommendations

The Rushebeya–Kanyabaha wetland’s ecosystem has changed due to alterations in land use and land cover, resulting in increased agricultural and built-up areas, as well as a decrease in the availability of natural habitats. The wetland composition has been fragmented, breeding habitats have decreased, and reliance on human-dominated landscapes for foraging has increased due to the conversion of grassland, bushland, and wetland to subsistence farms and urban areas between 1986 and 2022. Although they mainly rely on farmlands for food, GCCs still use wetlands for nesting and roosting. This study has also demonstrated the relationships between wildlife and human land use in altered ecosystems.

GCC conservation in this wetland catchment necessitates coordinated management that supports sustainable farming methods while preserving the integrity of the wetland. Preventing habitat degradation and disturbance would involve establishing protective buffer zones surrounding wetlands, as well as controlling land conversion near breeding grounds and engaging local communities in habitat conservation. To identify persistent spatial trends, future research should focus on three areas: first, long-term monitoring of land-use dynamics; second, ecological studies on food availability, breeding success, and movement ecology of cranes under changing landscapes; and third, socioeconomic assessments that analyse the human factors influencing wetland transformation. A more detailed understanding of how landscape change influences species persistence in multiple ecosystems would result from combining ecological, social, and geographical data.

Participatory wetland management plans that strike a balance between habitat preservation and agricultural productivity should be given top priority in policy initiatives. Food security would be maintained while habitat degradation would be reduced by providing incentives for agroecological practices, such as keeping areas fallow, reducing pesticide inputs near wetland boundaries, and implementing soil–water conservation strategies. By combining community-based monitoring and conservation education, local ownership may be promoted, ensuring long-term sustainability and compliance.