Introduction

Climate change and its impact on biodiversity have emerged as a growing global concern1,2,3. The rising temperatures, shifting precipitation patterns, and alterations in habitat suitability directly influence the biological responses of species4, as well as their behavioral patterns5, circadian and circannual activity rhythms6, reproduction7, and both intraspecific and interspecific interactions8,9. These climate-driven shifts can also alter resource availability and potentially intensify competition among species that share the same ecological niche10,11. Moreover, anthropogenic land use and land cover (LULC) changes, combined with the effects of climate change, have significant implications for the diversity and structure of local ecological assemblages12,13, species interactions11, and local and regional distributions14. At the local scale, land fragmentation can also negatively impact the richness and composition of waterbird species15.

Furthermore, climate-LULC change can lead to shifts in the geographic distribution of species' ecological niches, creating opportunities for new interactions, including competition, among previously coexisting or non-coexisting similar species. For example, the interplay of climate change and anthropogenic activities has significantly impacted the distribution patterns of the Andean Condor (Vultur gryphus) and the Black Vulture (Coragyps atratus) throughout South America, leading to habitat overlapping that facilitate cohabitation16. Stenseth et al. investigated the competitive interactions between the Blue Tits (Cyanistes caeruleus) and the Great Tits (Parus major) in Western Europe and found that climate change can generate equilibrium conditions with local differences in species composition and spatial structures17. In the central Indian region, characterized by a mosaic of human-made and natural landscapes, three partially sympatric owl species exhibit a positive correlation with the presence of agriculture. The conversion of agricultural land may result in spatial segregation among these owl species18. Climate change can also impact the spatial overlaps between carnivores, intensifying competition due to water scarcity in arid and semi-arid regions and decreasing rainfall19. Studies have demonstrated that future climate change will significantly influence the patterns of spatial overlap between the Giant Pandas (Ailuropoda melanoleuca) and coexisting mammalian species in Qinling Mountains. Under moderate and worst-case climate change scenarios, competition between species will intensify11.

The Crested Ibis (Nipponia nippon), once thought to be nationally extirpated20, has experienced a remarkable recovery in its wild population over the last four decades. According to unpublished data from the Shaanxi Hanzhong Crested Ibis National Nature Reserve, the current wild population stands at approximately 6000 individuals. Despite the dedicated efforts to protect its habitat, the population still faces the inherent risk of extinction during the recovery phase21. Currently, the Hanjiang River Basin in southern Shaanxi is the sole remaining habitat for the Crested Ibis22. Among the coexisting species that partially overlap in diet with the Crested Ibis include the Little Egret (Egretta garzetta), Grey Heron (Ardea cinerea), Chinese Pond Heron (Aedeola bacchus), Cattle Egret (Bubulcus ibis), and Black-crowned Night Heron (Nycticorax nycticorax)23,24. Researchers have used multi-temporal niche models to predict potential shifts in the ecological niche and distribution of the Crested Ibis under future climate and land use scenarios25. Furthermore, studies have found that the Crested Ibis, as a tactile forager, can form mixed-species groups with the Little Egrets to reduce predation risk and can tolerate some intraspecific competition through resource depletion, aligning with the resource exploitative competition hypothesis23. However, the impact of future climate and land use changes on the habitat distribution of the current wild population, particularly in the context of competition with closely related sympatric species, remains unknown.

This study utilized ensemble Ecological Niche Models (ENMs) to simulate the overlapping distribution patterns of the Crested Ibis and Egret and Heron species (EHs), and analyzed their responses to climate and LULC changes. We propose the following hypotheses: Firstly, visually foraging EHs have a broader ecological niche breadth compared to the Crested Ibis, resulting in spatial overlap in their suitable habitats. Secondly, in comparison to climate change, alterations in LULC patterns within the densely populated habitats of the Crested Ibis will primarily influence its future distribution. Ultimately, our findings aim to provide new insights into niche similarities that contribute to the coexistence and competitive interactions among EHs and the Crested Ibis in a protected landscape (Fig. 1).

Fig. 1
figure 1

Study area and occurrence points of the Crested Ibis and the EHs. The map was created using ArcGIS Pro (https://www.esri.com/en-us/arcgis/products/arcgis-pro/).

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Results

Predicted LULC changes

The evaluation of the model performance yielded a Kappa coefficient of 0.83 and an OA of 0.88, demonstrating the effectiveness of the model in capturing actual variations in LULC within the study area. These findings strongly support our decision to select the most appropriate parameter sets for predicting LULC changes up to the year 2050. Based on the simulation, assuming the transition rate remains consistent with that observed in the previous decade (2010–2020), significant LULC changes are anticipated in the study area by 2050, particularly along the Hanjiang River basin. Comparing the projected LULC for 2050 with the actual distribution in 2020 (Fig. 2), the following alterations are anticipated in different land use categories: (1) Cropland: a negligible decrease of − 0.88%; (2) Forest: a slight increase of 0.67%; (3) Grassland: a substantial decline of − 10.43%; (4) Shrubland: a moderate decrease of − 3.68%; (5) Wetland: a significant reduction of − 27.27%; (6) Water: a minor decrease of − 4.00%; (7) Urban: a significant increase of 65.11%. Analyzing the percentage of each land use type within the total area, in 2020, Cropland, Forest, Grassland, Shrubland, Wetland, Water, and Urban accounted for 17.4674%, 76.7046%, 4.1552%, 0.2323%, 0.0094%, 0.5457%, and 0.7222% of the total area, respectively. By 2050, these proportions are projected to shift to 17.31422%, 77.2211%, 3.7217%, 0.2237%, 0.0068%, 0.5239%, and 1.1925%, respectively.

Fig. 2
figure 2

LULC maps of the study area, illustrating the observed LULC in 2020 and the simulated LULC in 2050. The map was created using ArcGIS Pro (https://www.esri.com/en-us/arcgis/products/arcgis-pro/).

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ENM evaluation and key environmental factors

Figure 3a showcases the average evaluation metrics obtained from 10 test runs, which were conducted to assess the predictive performance of ecological niche modeling. The average overall accuracy, calculated from six metrics, is 0.80 for the Crested Ibis, 0.60 for the Little Egret, 0.61 for the Grey Heron, 0.69 for the Chinese Pond Heron, 0.76 for the Eastern Cattle Egret, and 0.73 for the Black-crowned Night Heron. These results indicate that the selected predictors accurately simulated the distribution of these species, suggesting their suitability for future projections.

Fig. 3
figure 3

(a) Evaluation metrics of each individual model and (b) Importance of environmental variables for each species’ ecological niche model (CI, Crested Ibis; LE, Little Egret; GH, Grey Heron; CPH, Chinese Pond Heron; ECE, Eastern Cattle Egret; BNH, Black-crowned Night Heron).

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Habitat selection for Crested Ibis and EHs is influenced by various factors, each with distinct and significant impacts. In general, elevation, slope, and human disturbance exert a more pronounced influence on all of these species. Specifically for the Crested Ibis, several factors are found to be crucial in shaping its environmental niche. These factors include precipitation of the coldest quarter (Bio19), elevation, nightlight intensity, slope, and LULC patterns (Fig. 3b).

Quantification of niche similarities

There was a substantial geographic overlap of niches among the species, as indicated by the equivalence values (Schoener's D and Hellinger's I), which generally exceeded 0.5 (Fig. 4). Among the species pairs, the Grey Heron and the Chinese Pond Heron exhibited the highest niche overlap values, with D = 0.71 and I = 0.92. Notably, the Little Egret (D = 0.62 and I = 0.93) and the Eastern Cattle Egret (D = 0.57 and I = 0.94), two species closely resembling the Crested Ibis (the focal species for conservation efforts), demonstrated a significant degree of niche similarity with the Crested Ibis. Figure 5 presents kernel density plots that illustrate the density distribution of each species in relation to the key environmental variables. The Crested Ibis exhibits a limited distribution range and a low occurrence density in terms of important factors such as Bio19, elevation, slope, and human disturbance. Moreover, it appears to be overshadowed by the EHs, which indicates a narrow niche breadth for the Crested Ibis (Table 1).

Fig. 4
figure 4

Ecological niche similarity (D: Schoener's D; I: Hellinger's I) between species (CI, Crested Ibis; LE, Little Egret; GH, Grey Heron; CPH, Chinese Pond Heron; ECE, Eastern Cattle Egret; BNH, Black-crowned Night Heron).

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Fig. 5
figure 5

Kernel density distribution of the Crested Ibis and EHs with respect to key environmental factors: (a) Elevation; (b) Slope; (c) Nightlight; (d) Precipitation of coldest quarter (Bio19).

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Table 1 The maximum training sensitivity plus specificity (MTSS) threshold and niche breadths of the Crested Ibis and EHs.
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Changes in suitable habitats under current and future conditions

The predicted potentially suitable habitats for the Crested Ibis are primarily located on the southern slope of the Qinling Mountains. These suitable habitats, defined with a MTSS threshold of ≥ 0.29, covers a total area of 12,083.50 km2, mainly concentrated in the counties of Yangxian, Chenggu, Hantai, Mianxian, Xixiang, Shiquan, Foping, and Ningshan (Fig. 6a). The five species of Egrets and Herons exhibit varying geographical distributions (Figs. 6 and 7). Among them, the Little Egret has the widest distribution range, covering an area of 15,814.40 km2, while the Eastern Cattle Egret has the narrowest distribution range, occupying only 1,943.82 km2 (Fig. 8b). With the exception of the Eastern Cattle Egret, the other EHs partially overlap in their distribution areas with the Crested Ibis. However, the Eastern Cattle Egret exhibits nearly complete overlap in its distribution area with the Crested Ibis. Notably, the suitability maps reveal that areas with high suitability for EHs (indicated by warm-colored zones) coincide with regions where wild Crested Ibis are densely distributed (Fig. 6).

Fig. 6
figure 6

Habitat suitability of (a) Crested Ibis, (b) Little Egret, (c) Grey Heron, (d) Chinese Pond Heron, (e) Eastern Cattle Egret, and (f) Black-crowned Night Heron. The lower limit of the color band is the Maximum Training Sensitivity plus Specificity (MTSS) threshold. The map was created using ArcGIS Pro (https://www.esri.com/en-us/arcgis/products/arcgis-pro/).

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Fig. 7
figure 7

Current and future potential habitat distribution of (a) Crested Ibis, (b) Little Egret, (c) Grey Heron, (d) Chinese Pond Heron, (e) Eastern Cattle Egret, and (f) Black-crowned Night Heron. C, current; CC, climate change scenario; CLC, climate and LULC change combined scenario. The map was created using ArcGIS Pro (https://www.esri.com/en-us/arcgis/products/arcgis-pro/).

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Fig. 8
figure 8

Species richness distribution map (a), suitable habitat areas for individual species (b) and multi-species overlap (c) under current condition and different environmental change scenarios by 2050. The numbers displayed under the color bands and histogram represent the overlap of the corresponding numbers of species (CI, Crested Ibis; LE, Little Egret; GH, Grey Heron; CPH, Chinese Pond Heron; ECE, Eastern Cattle Egret; BNH, Black-crowned Night Heron). The map was created using ArcGIS Pro (https://www.esri.com/en-us/arcgis/products/arcgis-pro/).

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Future projections indicate that the suitable habitat range of the Crested Ibis is expected to potentially expand by 16.22% under the scenario of climate change alone. However, when considering both climate and LULC changes together, the suitable habitat range is projected to contract by 27.07%. The EHs are anticipated to be more significantly affected by both climate change and LULC changes. Specifically, under the climate change scenario, the habitat areas of the Little Egret, Grey Heron, Chinese Pond Heron, Eastern Cattle Egret, and Black-crowned Night Heron are projected to decrease by 32.35%, 12.43%, 9.38%, 16.42%, and increase by 10.41%, respectively. In the combined scenario of climate change and LULC changes, the projected habitat decreases for the Little Egret, Grey Heron, Chinese Pond Heron, and Black-crowned Night Heron are 62.16%, 25.83%, 55.98%, and 30.88% respectively, while the habitat of the Eastern Cattle Egret is projected to increase by 149.20% (Figs. 7, 8b).

Changes in overlaps (richness) under current and future conditions

Under the current climate and LULC conditions, the region with the highest species richness, encompassing both the Crested Ibis and five EHs (richness = 6), is located in an area of 785.70 km2, predominantly within the Hanjiang River basin (Fig. 8a). Additionally, areas with higher species richness, containing the Crested Ibis and four EHs (richness = 5), as well as the Crested Ibis and three EHs (richness = 4), cover 966.33 km2 and 1,715.22 km2, respectively (Fig. 8a, c).

Under the scenario of climate change alone, the overlap of suitable areas between the Crested Ibis and three, four, and five EHs is projected to decrease by 12.76%, increase by 6.11%, and decrease by 10.54%, respectively. However, when considering both climate change and LULC changes together, the overlap of suitable areas between the Crested Ibis and three, four, and five EHs is expected to decrease by 20.55%, decrease by 1.83%, and increase by 40.16%, respectively (Fig. 8a, c).

Discussion

Niche Competition Theory believes that when different species inhabit similar niches, they compete for limited resources such as space and nutrients26,27,28,29. The competitive exclusion principle, a fundamental concept in ecology, suggests that complete competitors are unable to coexist30. This exclusion can occur through exploitative competition for limited resources or apparent competition facilitated by shared natural enemies31. This subtlety can be leveraged to explore the relevance of niche competition theory to our study subjects, offering insights into the interactions and potential coexistence of species with partially overlapping niches. Drawing upon niche theory, the coexistence of sympatric Crested Ibis and EHs can be attributed to the spatial and temporal distribution of their diet composition and the segregation of their dietary niches32. For instance, the Crested Ibis employs tactile foraging, whereas EHs rely on visual foraging. The Crested Ibis derives benefits from associating mixed-species flocks that include visual foragers like Little Egrets, enhancing antipredator advantages through collective detection and early warning mechanisms23. In contrast, tactile foragers generally have lower night vision capabilities compared to visual nocturnal feeders33. Furthermore, tactile foragers tend to exhibit more limited habitat use and rely on short-term environmental factors that concentrate prey during periods of reduced food availability34. These overlaps and distinctions in their foraging behaviors mitigate competitive pressures, facilitating their coexistence. However, increasing spatial overlap in their distribution after environmental alterations may lead to heightened competitive interactions, as they share the consumption of aquatic creatures within the same space35,36,37,38.

Our results indicated that precipitation in the coldest quarter (Bio19), altitude, nightlight intensity, slope and LULC are important factors in the habitat choice of the Crested Ibis. This finding aligns with previous studies that have highlighted the significance of precipitation and anthropogenic activities in influencing the habitat distribution of the Crested Ibis25. The kernel density distributions of the first four important factors for the Crested Ibis displayed variations compared to those observed in EHs in terms of the ranges and densities of these factors. However, the distribution range and density of all EHs on these factors exceeded that of the Crested Ibis, with the Ibis’s curve almost encompassed by them. Furthermore, our research revealed that the niche breadth of the Crested Ibis is narrower compared to that of the EHs. This finding supports the assertion by Boulangeat et al. that species with a restricted niche breadth are at a greater risk of rarity and extinction39. This is attributed to a combination of factors, including habitat loss and environmental change, which can disproportionately affect specialist species40.

The Crested Ibis currently has a limited population confined to the counties along the banks of the Hanjiang River, sharing its habitat with the EHs. Our modeling predictions indicate that the geographical distribution range of the Crested Ibis is expected to expand in the future due to climate change, which aligns with a previous study by Yiwen Sun et al. that also predicted an increase in habitat range and abundance22. However, when considering the combined effects of climate change and LULC changes, the suitable habitat range for the Crested Ibis actually shows a decrease. This suggests that the species remains at significant risk due to human activities, highlighting its vulnerability. Previous studies have identified excessive hunting and agrochemical overuse as the primary threats to the wild Crested Ibis41,42. These studies have also reported a continued decline in the effective population size of the species over time. Our findings align with these previous studies and emphasize the importance of controlling human activities that contribute to LULC changes, such as the extensive expansion of agricultural land and impervious areas. Although the Crested Ibis does benefit from human intervention to some extent35,41, it is crucial to regulate the intensity of human pressure and the rates of LULC changes to ensure the species' population recovery. It is worth mentioning that changes in climate and LULC will affect sympatric EHs differently. While climate change is expected to have a minor negative effect on the coexisting EHs, the impact of LULC changes is expected to be significant. Based on the variable importance test, their habitat distributions are predominantly influenced by human activities. In our model, we considered a moderated Shared Socioeconomic Pathways scenario, in which these species may experience different levels of habitat loss as human activities intensify.

Our findings provide novel evidence supporting the notion that the Crested Ibis and EHs exhibit overlapping but slightly distinct environmental niches, indicating a potential competition for resources, particularly food and space. The EHs, known for their visual foraging behavior, possess a broader ecological niche compared to the Crested Ibis, enabling them to inhabit a wide range of habitats and exploit a variety of food sources. This adaptability and flexibility in their survival strategies make them more resilient to environmental changes43,44. In contrast, the Crested Ibis, as a tactile forager, has a narrower ecological niche and specialized requirements. This specialization makes the Crested Ibis more reliant on specific resources, such as larger prey like loaches, eels, crabs, crayfish, and aquatic insects. This differs from EHs, which predominantly focus on small fish. Moreover, the Crested Ibis necessitates specific environmental conditions, such as winter-flooded rice paddies and riverbanks, in contrast to EHs, which can thrive in diverse wetland habitats23,45,46. Consequently, it is less competitive and more vulnerable to environmental changes, potentially facing increased competition from other species with similar but broader niche requirements22,25,35.

The findings of our study indicate that alterations in climate and LULC have a substantial influence on species richness. Climate change is anticipated to lead to a reduction in areas with high species richness, while areas with low species richness are expected to increase. In central Iran, mirroring our findings, the habitats shared by carnivores are projected to decline due to climate change19. This implies that climate change may potentially reduce competition for resources and space within certain species' habitats. However, the combined effects of projected LULC changes and climate change are predicted to decrease the extent of regions characterized by low species richness. Conversely, regions with high species richness, including the Crested Ibis, are expected to expand in response to these changes. Specifically, the areas where the five EHs and the Crested Ibis coexist, considered diversity hotspots, are projected to increase due to the combined impacts of climate and LULC changes. The decrease in suitable habitats for individual species and the increased overlaps with the habitat of Crested Ibis indicate that EHs are more inclined to inhabit the protected Crested Ibis' habitat, if their habitats are threatened in the future, intensifying competition between them.

Our LULC simulation results suggest that urban land areas are expected to expand, while wetland and water areas will likely decrease in the study area. In accordance with the present results, Yu et al. (2022) demonstrated that the loss and deterioration of wetlands and forests, as well as the reduction of foraging and nesting habitats in historical Crested Ibis distribution areas, were consequences of agricultural intensification, expansion of impervious surfaces, and the clearing of natural vegetations25. It is quite possible that the ranges of the EHs now overlap with the historical habitat range of the Crested Ibis47. As a result of human activities leading to the destruction of their own habitats, the EHs are compelled to seek refuge in the remaining wild Ibis habitats around Yangxian County, where the natural environment is more effectively protected. This can lead to an increase in species richness and competition intensity in these areas. It's worth noting that some EHs have a global distribution, unlike the wild Crested Ibis, which are exclusively found on the southern slope of the Qinling Mountains. We acknowledge that the EHs within our study area might be constrained by altitude from crossing the northern slopes of the Qinling, yet they could potentially migrate southward in the future, which may alleviate the competitive pressures arising from habitat overlap. However, this aspect falls beyond the scope of our current research, necessitating further studies focusing on population dispersal and migration dynamics. The findings of this study suggest that potential habitat overlaps, along with certain ecological niche similarities, indicate that a significant level of habitat overlap could pose challenges to the sustained growth of the Crested Ibis population.

To comprehensively understand interspecific competition, it is essential to carefully examine various elements, including the impact of both biotic and abiotic factors, the scale of analysis, diurnal activity patterns, food abundance, and population abundance and dynamics48,49. Unfortunately, the scarcity of comprehensive data prevented us from fully considering the impact of these factors in our study. Instead, we relied solely on quantifying the spatial distribution of habitats, which inevitably introduced some inherent bias. However, despite this limitation, our understanding of distributional overlap can still provide valuable insights for restoration efforts by considering the competitive interactions among target species. Conservationists can leverage this knowledge to implement measures that prevent the dominance of a particular species and promote the coexistence of multiple species. This may involve habitat restoration, careful selection of suitable sites for reintroduction, controlled harvesting or translocation of dominant species, or promoting the colonization of less competitive species.

Methods

Target species and presence records

In our study, we selected five EHs, along with the Crested Ibis, based on their popularity, coexistence, and the availability of occurrence data. These EHs include the Little Egret, Grey Heron, Chinese Pond Heron, Eastern Cattle Egret, and Black-crowned Night Heron. Presence records were gathered through a combination of extensive field surveys and the utilization of additional resources. Initially, we obtained the annual reports from the Shaanxi Hanzhong Crested Ibis National Nature Reserve, which formed the basis of our dataset. To enhance this dataset with information on the distribution of the Crested Ibis beyond the protected area, we conducted extensive field observations, documenting both the Crested Ibis and EHs in Yangxian County and the surrounding 18 counties (32°02′–34°23′N, 106°11′–109°53′E) in Hanzhong and Ankang City, Shaanxi Province, China, spanning from 2017 to 2020 (Fig. 1). By compiling a comprehensive dataset, we amassed a total of 238 occurrence points for wild Crested Ibis and 93 points for EHs. Furthermore, we enriched the EHs records by incorporating observations recorded in the China Bird Report database (http://www.birdreport.cn/). In total, we gathered 263 georeferenced occurrence records, including 68 records for the Little Egret, 36 for the Grey Heron, 42 for the Chinese Pond Heron, 62 for the Eastern Cattle Egret, and 55 for the Black-crowned Night Heron (Fig. 1). To address spatial autocorrelation, we applied a filtering process where we randomly selected one occurrence point within each 300 \(\times\) 300 m grid, corresponding to the maximum cell size of the environmental variables.

Environmental variables

To comprehensively examine the habitat preferences, environmental niches, and distribution dynamics of the Crested Ibis and the EHs coexisting in the study area, our model incorporated a wide range of environmental factors. These factors included climate conditions, topographical attributes, human impacts, and the availability of water and food resources. To incorporate plausible variations in future climate, we utilized climate projections for the years 2041–2060 (referred to as 2050) from the BCC-CSM2-MR, MIROC6, and CMCC-ESM2 Global Circulation Models (GCMs) featured in the sixth assessment report of the Intergovernmental Panel on Climate Change (IPCC6). We considered the Shared Socioeconomic Pathways 2–45 (SSP2-45) scenario for the year 2050, averaging three GCMs. The SSP2-45 scenario is characterized by a “middle of the road” development pathway, with moderate economic growth, a growing global population, and a moderate level of environmental protection. In this scenario, there is a gradual transition towards a more sustainable environmental condition, but it is not as rapid or comprehensive as in some other scenarios50.

To obtain the bioclimatic variables, we sourced data from WorldClim, which has a spatial resolution of 30 arc-seconds (http://www.worldclim.org). The LULC variables were obtained from GlobeLand30 (http://www.globeland30.org/), while the topographical variables were sourced from Geospatial Data Cloud (http://www.gscloud.cn). Lastly, the human influence variables were obtained from the Resource and Environmental Science and Data Center (https://www.resdc.cn/). We calculated the distance to variables using the Euclidean Distance tool in the Spatial Analyst in ArcGIS 10.8.

To reduce multicollinearity between the variables, we performed a subset selection of the variable set using Pearson's correlation coefficient. We selected the variables with a coefficient of |r|< 0.75, reducing dependence among selected variables51. Ultimately, we retained the ten most independent and ecologically significant environmental factors to build the niche models. These factors included Aspect, Precipitation of the coldest quarter (Bio19), Isothermality (Bio3), Temperature annual range (Bio7), Elevation, Farmland, LULC, Nightlight, Slope, and Distance to water (Table 2).

Table 2 Environmental variables for Ecological Niche Models (ENMs) and the Patch-generating Land Use Simulation (PLUS) model: sources and applications.
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LULC simulation

To understand the dynamics of land in the study area and forecast its future trends, we conducted a LULC simulation using an advanced model known as the Patch-generating Land Use Simulation (PLUS) model52. The PLUS model extends the well-recognized cellular automata (CA) model to capture intricate spatio-temporal interactions and the underlying rules governing land use changes53,54. It incorporates a novel rule-mining strategy and a patch-generating mechanism, significantly enhancing its capability to represent the non-linear nature of LULC changes. This model is particularly suitable for simulating complex geographical evolution processes and has been widely employed in studies related to land use simulation55,56,57. To apply the PLUS model in this study, we utilized two historical LULC maps derived from GlobeLand30 as the fundamental input for future simulations. Recognizing the significant impact of climatic conditions on vegetation growth, soil moisture levels, and water availability crucial for biological processes; the influence of socio-economic factors on land use for residential, commercial, or industrial purposes; and the effects of natural environmental factors on agricultural productivity and land suitability, we identified environmental drivers that could potentially influence LULC changes. Following the research methodology outlined by Liang et al.52, we integrated a total of 15 driving factors, consisting of ten socioeconomic, two climatic, and three natural environmental factors, believed to play a role in the evolution of land type transitions (Table 2). The PLUS model consists of two main modules: the Land Expansion Analysis Strategy (LEAS) and the CA Model based on Multi-type Random Patch Seeds (CARS). We established specific parameters for the PLUS model as follows: (1) LEAS module: consisting of 20 regression trees, a sampling rate of 0.05, and mTry set to 15; (2) CARS module: with a neighborhood size of 3, patch generation rate of 0.7, expansion coefficient of 0.5, seed percentage of 0.0001, and neighborhood weights allocated as follows: 0.103 (Cropland), 0.495 (Forest), 0.151 (Grassland), 0.056 (Shrubland), 0.012 (Wetland), 0.039 (Water), and 0.376 (Urban). The neighborhood weights are established by calculating the ratio of the expansion areas specific to particular land use category in relation to the overall land expansion. Subsequently, these weights are fine-tuned based on expert insights and knowledge.

To evaluate the accuracy of the model, we initially simulated the LULC for the year 2020 based on the development probability derived from the land type transition that occurred between 2000 and 2010. Subsequently, we employed the validation function within the PLUS model to compare the simulated LULC data for 2020 with the actual contemporaneous data obtained from GlobeLand30-202058. For this comparison, we utilized the Confusion Matrix calculation method, with a selected sampling rate of 0.05. To evaluate the model’s performance, we computed two key metrics: the overall accuracy (OA)52 and the Kappa coefficient59. Higher values of OA and the Kappa coefficient indicate greater accuracy, with a score above 0.8 generally considered to indicate statistically satisfactory model performance60,61. Finally, we projected the LULC for the year 2050 to conduct future ecological niche analysis.

Ecological niche modeling

In our study, we utilized an ensemble modeling approach to enhance the calibration of ENMs for both the Crested Ibis and sympatric EHs. To create robust ENMs, we combined three widely used algorithms: Maximum Entropy (MaxEnt), Generalized Linear Model (GLM), and Random Forest (RDF). This ensemble approach allows us to capitalize on the strengths of each algorithm and improve the overall predictive performance.

To train the ENMs, we randomly divided the available data into two sets using the bootstrap random partition method. We allocated 80% of the data for model generation, and the remaining 20% was reserved for assessing the predictive accuracy of each model. The modeling process was implemented using the R package ENMTML62, and the significance of environmental variables was assessed using "imp_var" function. To allocate pseudo-absence points within the background area, we used the "GEO_ENV_KM_CONST" method. This method combines environmental and geographical approaches with a k-means non-agglomerative clustering procedure to evenly disperse points across environmental space. By doing so, it can mitigate the effects of data bias by providing a corrective measure, highlighting regions where the species is less likely to occur62,63,64.

To evaluate the performance of each algorithm, we considered six different evaluation metrics: True Skill Statistic (TSS)65, Boyce66, Kappa59, Sorensen67, Jaccard67, and Area Under the Curve (AUC)68. Higher values for these metrics indicate greater accuracy and reliability of the individual ENMs. To investigate the potential distribution of the selected species, we constructed a final model by combining the results of all the algorithms using a weighted mean approach69. This approach calculates suitability values by considering the performance of the algorithms. The weight of each algorithm was determined using the TSS values, as illustrated in the following equation:

$${{\varvec{W}}}_{{\varvec{j}}}=\frac{{{\varvec{r}}}_{{\varvec{j}}}}{{\sum }_{{\varvec{j}}=1}^{{\varvec{h}}}{{\varvec{r}}}_{{\varvec{j}}}}$$
(1)

where \({{\varvec{W}}}_{{\varvec{j}}}\) represents the weight assigned to the model result \({\varvec{j}}\), \({{\varvec{r}}}_{{\varvec{j}}}\) represents the TSS value associated with the model result \({\varvec{j}}\), and \({\varvec{h}}\) represents the number of model results.

To examine the potential impacts of future climate and LULC changes on the habitat distribution pattern of the species, we projected ENMs under two different scenarios: “climate change only” and “combination of climate and LULC changes”. The post-modelling analysis was performed in three steps using ArcGIS10.8. In the initial step, we categorized habitats into a binary map by applying the Maximum Training Sensitivity plus Specificity (MTSS) logistic threshold70. Subsequently, we performed an overlay analysis to compare the distribution of suitable habitats in the present timeframe and the projected future scenarios for the 2050s. Finally, we used SDM toolbox 2.071 to estimate species richness, which represents the total number of species in a grid cell. We also calculated the areas encompassing various classes of richness that include the habitat of the Crested Ibis.

Quantification of niche similarities

To assess and compare the environmental niches of the Crested Ibis and EHs, we employed ENMTools 1.4.372, a valuable tool for automating ENM creation, calculating similarity measures, and conducting statistical comparisons of habitat distributions. To quantify the niche overlap between the species, we calculated two similarity metrics: Schoener’s metric D and Hellinger’s index I, derived from the ecological environmental niche space73,74. Schoener’s D considers the probability distributions of species occurrence across different regions or cells, measuring the overlap of niches based on species abundance in those locations. Hellinger's I, on the other hand, relies solely on probability distributions without assuming species abundance patterns. Both metrics range from 0 to 1, where 0 indicates no overlap or complete niche divergence, and 1 indicates complete overlap or identical niches. To assess the niche breadth for each species, we calculated the Levins’ inverse concentration metric72,75. Niche breadth quantifies the range of habitats occupied by a species. A value of 0 indicates that the species is restricted to a single grid cell, while a value of 1 suggests an equally suitable environmental distribution across all grid cells. Therefore, species with a wider environmental distribution have higher niche breadth values. Finally, to explore the critical environmental variables associated with the niche similarity between the Crested Ibis and EHs, we employed the Kernel density estimation method. If the Kernel density plots for each factor show significant overlap, it implies that the environmental preferences of the species share similar values or exhibit similar patterns in their distribution. This indicates a potential correlation or dependency between the factors for each species. Conversely, if the plots do not overlap much and are distinct, it suggests that the factors are independent of each other76.