Abstract
Context
Birds, as indicators of biodiversity, are experiencing habitat reduction and loss due to landscape changes. Evidence is mounting that the response of bird richness to landscape patterns remains controversial on a global scale. In this study, we conducted a quantitative global synthesis to gain a comprehensive understanding of this relationship. Our findings contribute to the development of bird conservation strategies that align with the objectives of SDG15.
Objective
Through a quantitative review, this study investigated the effects of landscape patterns on bird richness and analyzed the sources of heterogeneity in the results.
Methods
A random-effects model was utilized to merge the impacts of landscape metrics on bird richness, and a meta-regression analysis was performed to investigate the origins of heterogeneity.
Results
The review encompassed 101 articles from 51 countries worldwide. Field sampling emerged as the primary method for acquiring bird data, with multiple linear regression and generalized linear models as the main analytical approaches. The meta-analysis results highlighted landscape area as a crucial factor influencing bird richness. Regarding landscape composition, the proportions of forests, shrublands, and water bodies positively impacted bird richness, while agricultural land and urban land had negative effects. The relationship between landscape complexity and bird richness is influenced by factors, including net primary productivity (NPP) and precipitation. Landscape heterogeneity was identified as a contributing factor to increased species richness.
Conclusion
Compared to landscape complexity, indicators of landscape composition and heterogeneity are more suitable as reference tools for bird conservation. The results of landscape complexity exhibit variation. Moreover, our findings underscore the crucial role of preserving forested areas in supporting bird diversity, emphasizing the necessity to account for regional variations when establishing forest cover thresholds.
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Introduction
Biodiversity decline is a pressing global environmental issue (Fischer et al. 2023), where human-induced landscape changes play a pivotal role (Thomas et al. 2004; Newbold et al. 2015). Birds, as widely distributed and easily observable species, serve as vital indicators of biodiversity status (Strohbach et al. 2013). However, bird populations are dwindling due to extensive habitat fragmentation and loss (Haddad et al. 2015; Püttker et al. 2020). Since 1990, 57 bird species have become extinct (Ceballos et al. 2015), and 13% are currently at risk of extinction on a global scale (Tilman et al. 2017), impacting the provision of ecosystem services and human well-being (Cox et al. 2018; Jahani et al. 2021). Therefore, understanding the impact of landscape patterns on bird diversity is crucial for developing effective conservation strategies and achieving SDG15 (Life on Land).
Landscape pattern encompasses the spatial distribution, composition and configuration of various land use types (Krummel et al. 1987; Turner and Gardner 2015; Liu et al. 2020). It is influenced by multiple factors, with human activities playing a pivotal role in shaping land types that impact landscape patterns (Turner 2005). Anthropogenic and natural factors have collectively driven landscape fragmentation, leading to a shift from landscape homogeneity to complexity (Turner 1990; Ryser et al. 2021). Research findings indicate a significant 95.8% decrease in the average patch area of global land during the early twenty-first century (Jacobson et al. 2019), accompanied by a 15.5% increase in fragmented forest patches (Rocchini et al. 2010). The dynamics of landscape change involve modifications in patch area, edge length, shape, and isolation, which contribute to the emergence of diverse landscape pattern characteristics (Haddad et al. 2015). The aspects of composition, complexity, and heterogeneity provide valuable insights into the fundamental structure and dynamics of landscapes (Duarte et al. 2018; Jin et al. 2023). Composition refers to the proportional distribution of land use patches (McGarigal and Marks 1995). Complexity captures the morphological and structural features of patches, such as higher fractal dimension indicating more complex patch shapes (Feng and Liu 2015). Heterogeneity manifests as the diversity of patch types within the landscape (Rocchini et al. 2010).
Bird diversity is closely linked to landscape patterns, with species richness serving as a valuable indicator for quantifying biodiversity. Research findings indicate that species richness plays a pivotal role in driving ecosystem functionality, emphasizing its significance beyond species turnover (Albrecht et al. 2021). Given its clear and intuitive portrayal of species diversity, this metric is widely utilized on a global scale (Santini et al. 2017; Stevenson et al. 2024). Previous research has indicated a positive correlation between forest cover and bird richness (Bonfim et al. 2021). Additionally, studies have found that bird richness is positively associated with the proportion of native forest but negatively correlated with the proportion of mixed forest (Lisón et al. 2022). The habitat heterogeneity hypothesis suggests that greater habitat heterogeneity supports higher species richness (MacArthur et al. 1966; Stein et al. 2014; Albrecht et al. 2021). Furthermore, it has been demonstrated that heterogeneity in agricultural landscapes contributes to increased bird richness (Anderle et al. 2023). In urban environments, green space heterogeneity positively influences the richness of urban avoider species (Chiron et al. 2024). However, the impact of landscape complexity on birds is comparatively weaker than that of composition and heterogeneity (Bonfim et al. 2021; Semper-Pascual et al. 2021; Dong et al. 2023).
Despite numerous published studies, inconsistent findings persist regarding the relationship between landscape patterns and bird richness, especially concerning landscape complexity (Henden et al. 2013; Schindler et al. 2013; Bonfim et al. 2021; Molina-Marin et al. 2022), and the potential sources contributing to these variations remain unclear. This inconsistency presents challenges for providing cohesive guidance in landscape planning and design. Therefore, conducting a comprehensive and systematic literature review is essential to comprehend the diverse results and their sources. This will contribute to a better understanding of how landscape patterns influence bird diversity, allowing landscape practitioners to develop effective strategies for bird conservation.
Meta-analysis has been proven to be a scientific and quantitative statistical method (Muruga et al. 2024), providing an effective approach for extensively understanding the relationship between landscape patterns and bird richness on a broad scale. It has been applied to examine the impacts of anthropogenic disturbances on bird functional diversity (Matuoka et al. 2020), the effects of roads on bird richness (Kroeger et al. 2022), as well as the influence of urbanization on bird abundance (Lakatos et al. 2022). Additionally, meta-analysis has revealed a positive association between landscape complexity and the richness and abundance of arthropods (Marja et al. 2022). However, there is a notable dearth of quantitative synthesis regarding the correlation between bird richness and landscape patterns. Consequently, this study aims to bridge this gap by undertaking a systematic evaluation and meta-analysis. We addressed the following questions: (1) How does landscape composition, complexity, and heterogeneity influence bird richness? (2) What are the sources of variation in the outcomes? (3) What threshold values for crucial indicators can be identified to inform landscape planning and design?
Materials and methods
Literature search
We conducted a comprehensive systematic search in the Web of Science literature database using the search strategy (“landscape pattern OR complex* OR fragment* OR heterogeneity OR configuration OR composition OR landscape metric or landscape index OR landscape indices”) AND (“bird* or avian*”), AND (“diversity or species richness or biodiversity”). Initially, three sets of search terms were established to encompass landscape patterns, birds, and species richness. These sets were searched using the OR operator. Subsequently, the results from the three searches were combined using the AND operator to perform a comprehensive search (Table S1 in the Supplemental Material contains the detailed search expressions). No limitations were imposed on the publication dates of the retrieved articles to ensure a comprehensive coverage of relevant literature. The same search strategy was applied in the Scopus database using the TITLE-ABS-KEY field. The search results were refined to include articles written in English and categorized as “Article”. This process yielded a total of 18,300 articles.
After removing duplicates, 11,462 articles underwent screening. Non-original research papers were excluded, and further screening based on titles and abstracts resulted in 1155 articles. Full-text articles were then screened using the following exclusion criteria (Fig. 1): (1) Absence of landscape pattern metrics, as this would not provide the necessary data relevant to our research objectives; (2) Lack of bird richness data, impeding the quantitative analysis of the relationship with landscape patterns; (3) Studies that were unable to provide the necessary effect sizes were excluded from the meta-analysis, regardless of their relevance. (4) Lastly, studies not based on real research case sites were also excluded, as simulated results would not contribute to our research objectives. Ultimately, 101 articles were included in the review.
Data extraction
We extracted various information from the literature, including author names, publication year, geographic location, bird data sources, bird richness, sample area, sample size, landscape metrics, and correlation coefficients. Data were recorded in Excel spreadsheets for subsequent analysis. Table 1 presents the landscape metrics mentioned in the collected literature, along with their corresponding descriptions. The literature data extraction was independently performed by two researchers, who also assessed the quality of the papers. Heterogeneity among the studies will be evaluated and addressed through heterogeneity evaluation and sensitivity analysis.
In constructing our literature database, the calculation of landscape metrics involved two approaches: field data collection and the utilization of land use data in Fragstats software. Some metrics, calculated for individual land use types (e.g., “class” metrics in Fragstats), may vary in their effects on birds due to different land use attributes. For example, the effect of the number of forest patches on bird cannot be equated to the impact of the overall number of landscape patches. Thus, we classified the landscape metrics into two categories: hybrid landscape metrics and single landscape metrics. This categorization facilitates a nuanced analysis of the impact of each metric on bird richness.
To elucidate the heterogeneity of research results, we incorporated external explanatory variables and collected data from WorldClim (https://worldclim.org/) for the year 2020. This included global annual total precipitation, average maximum temperature, and average minimum temperature, all with a spatial resolution of 21 km2. We also acquired 2020 global net primary productivity (NPP) data from MODIS with 500 m resolution. The vector data for the Ecoregion map were obtained from Olson et al. (2001). All the aforementioned data were stored in ArcGIS 10.8.
Effect size calculation
Species richness is a readily comprehensible and uncontroversial indicator for describing biodiversity (Fjellstad et al. 2001). In this study, we employed the Pearson’s correlation coefficient (Pearson’s r) between bird richness and landscape metrics as the effect size for meta-analysis. Pearson’s r values were obtained through the following approaches: (1) Direct reporting of Pearson’s r values in the original studies (n = 17; 16.83%); (2) Conversion of Spearman’s correlation coefficients provided in the articles to Pearson’s r (n = 5; 4.95%) (Yang and Konrath 2023); (3) Calculation of Pearson’s r using raw data from the articles (n = 54; 53.47%); (4) Extraction of numerical values for bird richness and landscape metrics from graphical data using GetData Graph Digitizer 2.26 software for Pearson’s r computation (n = 25; 24.75%) (Cauvy-Fraunié and Dangles 2019; Liu et al. 2023). To ensure data normality prior to meta-analysis, Fisher’s z transformation was applied to Pearson’s r (Koricheva et al. 2013). The formula for Fisher’s z transformation is as follows (Borenstein et al. 2009):
where \(z\) represents the Pearson’s r that has been transformed using Fisher’s z. The variance of \(z\) can be calculated as follows:
where \({V}_{z}\) denotes the variance of \(z\), and \(n\) represents the sample size.
Statistical analysis
We conducted a meta-analysis on the Fisher’s z transformed Pearson’s r, including publications with at least 5 counts (Wu et al. 2023; Li et al. 2024). For publications with fewer counts, we provided narrative description. The random effects model assumes that the estimated effects in the studies originate from different population effects, and the heterogeneity among studies includes not only sampling errors but also genuine effect differences (Borenstein et al. 2009). The data collection for this study encompasses studies with varied landscape characteristics and experimental designs, rendering the random effects model more appropriate. Employing this model, we obtained a 95% confidence interval (CI) and visualized the results with a forest plot. A non-zero 95% CI indicated a statistically significant combined effect size of landscape pattern on bird richness, while a CI including zero suggested a significant difference in the results (Song et al. 2019). To facilitate interpretation, we converted Fisher’s z values back to Pearson’s r.
When heterogeneity exists in the meta-analysis results, meta-regression can be employed to explore the sources of heterogeneity (Hedges et al. 2010). By introducing covariates that may impact the variation in study outcomes and assessing their associations with the effect sizes, meta-regression provides valuable insights. Therefore, we performed meta-regression with seven moderators: sample area, latitude and longitude of the study area, publication year, NPP, annual total precipitation, average maximum temperature, and average minimum temperature. We also assessed publication bias, which can affect the credibility of the meta-analysis, by employing a funnel plot and conducting Egger test. Funnel plot asymmetry and a p-value less than 0.05 in the Egger test indicated the presence of publication bias (Egger et al. 1997). In the presence of publication bias, we employed the Trim-and-Fill method to assess its impact on the results. If the effect size estimate remains unchanged after accounting for potentially missing studies, it suggests that publication bias has minimal influence on the meta-analysis results.
The robustness of the meta-analysis results was assessed using sensitivity analysis. We used the “one study removed” approach, sequentially excluding one study at a time while combining the effect sizes of the remaining studies. Changes in the combined effect size and 95% CI were compared to determine consistency with the original meta-analysis. Consistent results would indicate the reliability of the meta-analysis. The aforementioned analyses utilized the “metafor” package in RStudio3.1 (Viechtbauer 2010).
To test thresholds for guiding landscape planning and design, we obtained the bioclimatic zones associated with each individual study and conducted both linear and nonlinear regression analyses. This comprehensive analysis encompassed a range of regression models, including linear, polynomial, exponential, power, and piecewise models. By exploring the fitting performance of different functional forms, we identified the most suitable model for characterizing the relationship between forest cover and bird richness. The model performance was evaluated using the Akaike Information Criterion (AIC) to determine the most suitable fit. These analyses were carried out in Rstudio 3.1, utilizing the “ggtrendline” package for linear, polynomial, exponential, and power regressions (Mei et al. 2022), the “segmented” package for segmented regression (Muggeo 2008), and the “cluster” package for k-means clustering (Mächler et al. 2012).
Results
Narrative synthesis of study characteristics
Our literature database comprises 101 publications sourced from 56 different journals. Research on the correlation between landscape patterns and birds has increased significantly over the past three decades (Fig. 2). The earliest publication in our database dates back to 1993, with 77.2% of the literature published after 2013. The years 2018 and 2022 emerge as pivotal, each hosting the highest number of included publications (n = 11). These publications span across 51 countries globally, with a predominant focus on temperate broadleaf and mixed forest biomes (Fig. 3). Geographically, the distribution of publications is primarily concentrated in Europe (n = 49), followed by Asia (n = 24), North America (n = 23), South America (n = 20), and a smaller number of studies conducted in Africa (n = 9) and Oceania (n = 9). China leads in the number of publications (n = 14), followed by the United States (n = 11), Australia (n = 9), and Brazil (n = 9), indicating a concentration of research efforts in these countries.
The geographical distribution of reviewed studies (where a single document may encompass multiple countries), with the Ecoregion map sourced from Olson et al. (2001)
A significant majority of articles (81.19%) utilized structured surveys to collect bird data (Fig. 4a). Some studies relied on data extracted from previous literature (n = 8) or utilized bird observation data from official institutions or organizations (n = 8). Within our literature dataset, two publications in 2018 made use of citizen science data. The use of semi-structured citizen science data, gathered by volunteers, has emerged as a valuable source for bird research. This data type offers wide observational coverage and substantial volume, effectively addressing data gaps (Planillo et al. 2021; Squires et al. 2021). Additionally, certain studies combined field surveys with supplementary bird observation records obtained from historical literature.
The association between bird richness and landscape patterns has been investigated using various models, with regression models being the most commonly utilized. This includes Multiple Linear Regression (n = 17), Generalized Linear Model (n = 16), and Generalized Linear Mixed Model (n = 14) (Fig. 4b). The Poisson distribution is frequently used to characterize count data distribution (Mohd-Azlan and Lawes 2011; Bain et al. 2020). Bird observation data is often collected in the form of counts, aligning with the assumptions of the Poisson-based Generalized Linear Model. Pearson correlation analysis was used in 13 instances to examine the relationship between landscape metrics and bird richness. In recent years, machine learning algorithms have gained prominence as a valuable tool. For example, Steel et al. (2017) employed an enhanced regression tree model, a self-learning binary algorithm that combines regression tree algorithms and boosting methods, to capture the nonlinear relationship between bird richness and predictive factors. Ferenc et al. (2016) utilized a random forest model to evaluate the ranking of landscape factors’ importance.
Effects of landscape composition, complexity, and heterogeneity on bird richness
Landscape composition
He meta-analysis included 31 studies examining the impact of Forest% on bird richness. Among these studies, 54.84% reported positive effects, 12.90% reported negative effects. And the remaining 32.26% of the studies reported neutral responses (Fig. 5a). The random effects model revealed that Forest% had the highest combined effect size (r = 0.32; 95% CI 0.18, 0.48) (Fig. 5a). Following Forest%, Shrubland% (r = 0.29; 95% CI 0.20, 0.39) and Water% (r = 0.21; 95% CI 0.08, 0.35) exhibited the second and third highest promoting effects on bird richness, respectively (Fig. 5b). 85.71% and 40.00% of the studies reported positive results for Shrubland% and Water%, respectively (Fig. 5a). In contrast, increasing Grassland% (r = − 0.17; 95% CI − 0.31, − 0.02), Agricultural% (r = − 0.35; 95% CI − 0.64, − 0.11), and Urban% (r = − 0.19; 95% CI − 0.34, − 0.04) would negatively impact species richness (Fig. 5b).
a The effect size and 95% CI for each study examining the impact of landscape composition on bird richness. Each square represents the Pearson’s r after Fisher z transformation, while the vertical line depicts the distribution of the 95% CI. An orange vertical line indicates a significant positive effect, green represents a significant negative effect, and gray signifies a neutral response. b Summary effect sizes for bird richness responses to landscape composition. Asterisk within the parentheses (*) indicates the conversion of effect sizes to Pearson r. Significance code: < 0.001 *** < 0.01 ** < 0.05*
Landscape complexity
The meta-analysis incorporated six landscape metrics to assess landscape complexity. When not distinguishing between landscape types, the 95% CI of all metrics’ effect sizes encompassed 0, indicating heterogeneous results. Specifically, ENN (r = 0.17; 95% CI − 0.23, 0.57), FRAC (r = 0.04; 95% CI − 0.31, 0.39), PD (r = 0.14; 95% CI − 0.24, 0.52), and SHAPE (r = 0.09; 95% CI − 0.15, 0.34) exhibited a weak positive trend, but these associations were not statistically significant. Similarly, ED (r = − 0.02; 95% CI − 0.18, 0.14) and NP (r = − 0.01; 95% CI −0.18, 0.17) showed non-significant negative relationships (Fig. 6b). However, when considering landscape types separately, the summary effect size of PD in natural landscapes was statistically significant (r = 0.42; 95% CI 0.17, 0.70), while the other metrics continued to display heterogeneity.
a The effect size and 95% CI for each study examining the impact of landscape complexity on bird richness. b Summary effect sizes for bird richness responses to landscape complexity. The annotations carry the same meaning as in Fig. 5
For landscape complexity metrics with limited sample sizes, we provide the distribution of Pearson’s r numerically (Fig. 7). Concerning DIVISION, three studies found a negative influence on species richness, while one study found a weak positive correlation. Regarding ECON, conflicting conclusions were presented in two articles. In terms of IJI, both studies indicated a weak correlation with bird richness. Only one source in the literature reported a positive correlation between the LPI and bird richness. For the PROX and TE, two studies each reported positive outcomes.
Figure 8 illustrates five instances of a positive correlation between AI and bird richness, along with two instances of a negative correlation, specifically observed in water bodies and urban land. Additionally, three results indicate a positive correlation between CONTIG and bird richness, while one study reveals a negative correlation. CIRCLE and COMPLEX generally exhibit a positive correlation with bird richness. Conversely, six studies assume a negative correlation between NP and bird richness, implying that the fragmentation of landscape context may have a detrimental impact on bird richness. ED, FRAC, LPI, and SHAPE present inconsistent outcomes, even within the same land cover category, indicating noteworthy disparities.
Landscape heterogeneity and area
Concerning landscape heterogeneity, 55.56% of the studies reported a positive influence of PR, while 22.22% reported negative or non-significant results (Fig. 9a). Similarly, SHDI (r = 0.31; 95% CI 0.20, 0.45) showed a positive correlation with bird richness, demonstrating its positive influence in both natural and agricultural landscapes. The analysis revealed a positive effect of landscape area (r = 0.53; 95% CI 0.47, 0.71), observed across natural, agricultural, and urban landscapes (Fig. 9b).
a The effect size and 95% CI for each study examining the impact of landscape heterogeneity on bird richness. b Summary effect sizes for bird richness responses to landscape heterogeneity. The annotations carry the same meaning as in Fig. 5
Evaluation of publication bias and sensitivity analysis
The assessment of publication bias revealed that the Egger test p-values for all indicators, except Agricultural%, were greater than 0.05 (Fig. S1–S3 in the Supplemental Material). Therefore, Agricultural% was identified as having publication bias. Applying the Trim-and-Fill method and including two additional studies, the meta-analysis produced a combined result of r = − 0.29; 95% CI − 0.54, − 0.05, indicating a slight change compared to the previous value (r = − 0.35; 95% CI − 0.64, − 0.11). The correlation trend did not reverse. Sensitivity analysis demonstrated the stability of the meta-analysis results for landscape composition, landscape heterogeneity, and area metrics. However, certain metrics of landscape complexity showed a change in the trend of the combined effect size, although the 95% CI did not reach statistical significance, consistent with the original results (Tables S4–S18 in the Supplemental Material). In summary, the influence of landscape composition, heterogeneity, and area on bird richness remains stable, while the results for landscape complexity exhibit notable variability.
Factors influencing bird richness in response to landscape complexity metrics
To examine the sources of result variability, a meta-regression analysis was performed on three landscape complexity metrics, leading to the exclusion of sample area, latitude, longitude, publication year, and temperature as influential factors (Fig. S4–S6 in the Supplemental Material). However, NPP and annual precipitation were found to partially account for the variation (Fig. 10). In regions characterized by higher NPP, increasing ED and NP tend to have more negative effects, while SHAPE shows a tendency towards more positive impacts. Moreover, in regions characterized by higher precipitation, there is an indication that increasing ED and NP may have a propensity to result in more negative impacts.
Test for forest cover thresholds
Forest cover plays a significant role in promoting biodiversity within landscape composition. Establishing thresholds for forest cover serves as a measure to restrict uncontrolled expansion of construction land. Figure 11 presents the regression results between global forest cover and species richness. Among the five models used, segmented regression demonstrates good fitting performance (the fitting results of each model can be found in Table S19 in the Supplemental Material). The results reveal variations in forest cover thresholds across different bioclimatic zones, with an average threshold value of 45.28%. Specifically, a study in the Deserts and Xeric Shrublands region indicates a threshold of 60.91%, while the Tropical Biome region includes seven studies with an average threshold of 51.25%. The Temperate Biome region incorporates four studies with an average threshold of 45.02%.
Discussion
Association between landscape patterns and bird richness
The research findings highlight the importance of forested habitats in preserving bird richness, as they offer intricate community structures, abundant food resources, and suitable nesting habitats, while also alleviating anthropogenic disturbances (MacArthur and MacArthur 1961 and Dong et al. 2023). However, the relationship between specific bird populations and forests can vary. Frugivorous birds, for instance, often rely heavily on forest coverage (Bonfim et al. 2021), while studies have found a negative correlation between forest coverage and the diversity of habitat generalist birds (Carrara et al. 2015). The meta-analysis results further demonstrate the positive contribution of shrublands to bird richness. Moreover, several studies have demonstrated synergistic effects on bird richness in mixed landscapes of forests and shrublands (Deschênes et al. 2003; Powell and Steidl 2015; Li et al. 2019). These effects can be attributed to the complex vegetation structure and diverse plant communities, providing additional ecological niches for birds (Deschênes et al. 2003). For instance, insectivorous and omnivorous birds show a stronger preference for such habitats (Ouin et al. 2015; ); Li et al. 2019 suggest that reducing understory vegetation or deadwood may negatively impact insectivorous birds. Moreover, water bodies and their surroundings provide suitable habitats for birds due to the presence of aquatic plants, which offer nesting sites and cover for waterfowl (Dong et al. 2023; Aubrechtová et al. 2024).
The habitat heterogeneity hypothesis posits that increasing heterogeneity in habitats contributes to promoting biodiversity (Pianka 1966). In this study, the combined effect size of the landscape Shannon’s diversity index showed a significant positive correlation in both natural and agricultural landscapes. Heterogeneous landscapes are recognized for providing more ecological niches compared to homogeneous landscapes (Allouche et al. 2012; Lorenzón et al. 2016). In natural landscapes, forest habitats provide diverse habitat choices and utilization for different species, thanks to their complex spatial structures with vertical layers of varying heights and habitat sizes (Laiolo 2002). In agricultural landscapes, a range of environmental conditions, including farmlands, water bodies, irrigation systems, and natural habitats, create diverse habitat environments (Belfrage et al. 2015). Furthermore, Li et al. (2020) highlighted the potential food sources for birds in diverse agricultural landscape types. Gardens and flowerbeds, for example, with their moist soil, attract a greater abundance of insects and vegetation, benefiting both insectivorous and frugivorous birds (Li et al. 2020). Due to the strong predictive performance of habitat heterogeneity in determining bird richness, Martinez-Núñez et al. (2023) employed land cover diversity data to predict global patterns of bird species populations and functional diversity.
The results underscore landscape area as the dominant factor, aligning with the species-area relationship theory (Boecklen 1986; Lomolino 2000). This theory suggests that larger habitat areas generally harbor a greater species richness. Nevertheless, landscape fragmentation diminishes patch size and intensifies patch isolation, hindering species dispersal and gene flow, ultimately compromising species diversity (Liu et al. 2022). Landscape area is the most crucial factor influencing species richness, and efforts should be made to increase the area of ecological land (e.g., forests, shrublands, wetlands) under limited conditions. Moreover, it is important to consider that increasing heterogeneity comes with the trade-off of reduced patch size. Urban avoider species exhibit a preference for large and highly heterogeneous green spaces, but the positive impact of increased heterogeneity on species diversity is less pronounced in small habitats (Chiron et al. 2024).
Variability in landscape complexity results may related to the differences in NPP and precipitation
The results of the meta-regression showed that NPP and precipitation significantly explained the variation in bird richness related to the ED, NP, and SHAPE metrics. Drawing on existing knowledge, we offer explanations for these findings. The “Species-energy theory” supports a positive association between NPP and bird richness (Wright 1983; Leveau 2019). In regions with high NPP, increased ED and NP manifest as landscape fragmentation, coupled with reduced habitat connectivity resulting from edge effects (Vieira-Alencar et al. 2023). These factors collectively have the potential to amplify the adverse effects on bird richness. To our knowledge, most studies have focused on examining the individual effects of vegetation productivity and landscape complexity on bird diversity. However, there has been comparatively limited research that integrates these factors to illuminate their underlying mechanisms. Leveau (2019) proposes a direct influence of vegetation productivity on bird richness, with habitat heterogeneity serving as an indirect factor. The results implies that the contributions of landscape patterns and vegetation productivity to bird richness might differ, potentially diminishing the explanatory power of indirect factors. Meanwhile, the meta-regression reveals that in regions with higher precipitation, the effects of ED, NP, and SHAPE on bird richness become more negative. Previous studies have demonstrated the adverse effects of precipitation on montane bird richness (Santillán et al. 2018, 2020), and increased precipitation reduces bird nesting success (Zuckerberg et al. 2018). Consequently, we propose that variations in vegetation productivity and precipitation across different regions worldwide could lead to diverse effects of landscape complexity on bird richness.
Setting thresholds for forest cover requires attention to regional variations
The concept of a threshold suggests a transition or change from one state to another within a specific regional context (Huggett 2005). This study aimed to identify the forest cover threshold as an indicator for managing urban expansion and preserving bird diversity in landscape planning. Specifically, we examined threshold variations in bird richness and forest cover across different bioclimatic regions. Our findings demonstrate a notable trend in tropical regions, where a higher threshold exists between bird richness and forest cover. This implies that birds in tropical areas depend on a greater extent of forest cover, supporting prior research (Betts et al. 2019; Weeks et al. 2023) that highlights the heightened sensitivity of bird species to forest fragmentation in tropical environments. For instance, certain tropical insectivorous bird species exhibit reluctance or inability to cross small gaps like roads, making them vulnerable to the adverse effects of habitat disturbance (Tobias et al. 2013).
We observed that within the same bioclimatic region, there could be variations in the threshold outcomes. To explain this finding, we considered potential reasons. Firstly, the impact of forest cover on bird richness could be influenced by other factors, such as the significant effects of water bodies and shrublands, as indicated by the findings of meta-analysis. Consequently, disparities in landscape composition among regions could affect the accuracy of the relationship between forest cover and bird richness. Secondly, the establishment of thresholds relied on regression analysis using independent research data, which might have been influenced by sample variations across studies.
The establishment of thresholds aims to mitigate the negative impacts of ongoing forest loss on biodiversity (Radford et al. 2005). However, it is crucial to acknowledge that ecosystem complexity imposes limitations on threshold determination (Fahrig 2003; Fischer and Lindenmayer 2007). Habitat fragmentation results in structural and qualitative changes that often yield reciprocal effects (Ewers and Didham 2005). Therefore, thresholds frequently fail to capture the intricate relationships within ecosystems (Ewers and Didham 2007). In addition to bioclimatic region variations, previous studies have also revealed inter-species differences in thresholds (Betts et al. 2007; Macchi et al. 2019). Guénette and Villard (2005) found lower tolerance to forest logging in Setophaga fusca, Empidonax flaviventris, and Certhia americana, while Vireo solitarius, Regulus satrapa, and Seiurus aurocapilla demonstrated higher tolerance to silvicultural activities. Overall, it is imperative to address the issue of thresholds diligently in biodiversity conservation. Adequate species sampling, consideration of inter-species differences, and local landscape characteristics should be taken into account prior to establishing thresholds.
Limitations and future directions
In the study, an extensive search was conducted across multiple literature databases, and a meticulous assessment of the selected publications was performed. However, it is worth noting that the availability of publications focusing on specific landscape metrics, such as ENN and PD, was limited. This limitation could potentially affect the robustness of meta-analysis results. Furthermore, the heterogeneity observed in the meta-analysis outcomes may stem from variations in the calculation methods employed for landscape pattern metrics. The utilization of field sampling data versus the application of Fragstats software for land use data calculations could introduce discrepancies in the meta-analysis findings. Moreover, our study focused solely on bird richness as an indicator, overlooking important metrics such as bird abundance and Shannon diversity at the alpha diversity level. Future research can also further explore beta diversity, capturing variations between communities. Furthermore, a more comprehensive understanding of the heterogeneity of results can be achieved by considering the distinct responses of species communities to landscape patterns through taxonomic classification.
We propose several questions for future research. Primarily, existing studies have primarily examined the relationship between landscape patterns and bird diversity at a single temporal scale. Yet, due to human activities, landscapes undergo dynamic changes. Understanding the implications of this variability on bird diversity necessitates analyzing landscape pattern changes over extended temporal scales, which should be a future research focus. In particular, it is important to examine variations in the impacts of landscape composition, complexity, and heterogeneity changes on bird diversity across different bioclimatic regions, as well as the role of these complex interactions in shaping species communities. In addition, exploring species-specific responses to landscape changes and their effects on colonization and extinction patterns can provide valuable insights.
The emergence of citizen science data and machine learning algorithms offers advantageous tools for exploring the relationship between landscape patterns and bird diversity at broader spatiotemporal scales. Considering the current methods of acquiring bird observation data, integrating diverse data sources, such as citizen science and historical literature, can supplement historical bird observations. Moreover, regarding data analysis models, the presence of multicollinearity among landscape pattern factors limits the effectiveness of traditional regression algorithms in addressing this issue. Therefore, the future application of machine learning algorithms holds greater potential. For instance, in elucidating biodiversity hotspots, large-scale citizen science data on birds and environmental variables can be collected, and key features can be extracted using machine learning algorithms to simulate spatial patterns of bird diversity.
Currently, there is a wealth of evidence on the impacts of landscape composition, heterogeneity, and area on bird richness. However, the effects of landscape complexity, especially morphology, on bird species are still debated and require further investigation. During the meta-regression process, we identified a divergence in the effects of climate and vegetation productivity, which contributed to variations in landscape complexity and species richness. This variability in outcomes can be attributed to the intricate relationship between landscape characteristics and climate change. Changes in climate can affect the timing, types, and density of vegetation growth, thereby impacting the availability of food resources (Lorel et al. 2021). Additionally, climate change can modify habitat structure and connectivity, ultimately influencing bird migration patterns, habitat selection, and population distribution (Guan et al. 2021). In the future, conducting comprehensive analyses of landscape complexity, vegetation productivity, and climate will aid in understanding their underlying mechanisms and revealing the responses of bird diversity to complex environments.
Conclusion
This study systematically reviewed the impact of landscape patterns on bird richness. The findings underscoring the significance of landscape composition, heterogeneity, and area metrics in assessing bird richness. However, the role of landscape complexity remains a subject of ongoing debate. The proportion of forests, shrublands, and water positively contribute to species richness, while grasslands, agricultural land, and urban land have adverse effects. Among these land types, forests exhibit the highest correlation with bird richness, and a recommend forest cover ranging from 15 to 40% to promote bird survival. Landscape heterogeneity and area demonstrate a positive role in bird richness. The impact of landscape complexity varies significantly. By controlling for moderators such as publication time, location, sample area, and temperature, studies propose that this variability may be attributed to precipitation and vegetation productivity in the study regions, emphasizing the combined influence of multiple environmental factors on bird communities. Furthermore, this study proposes further investigation into the long-term effects of dynamic landscape changes on birds, as well as the potential utilization of citizen science data and machine learning algorithms. The findings of our study offer compelling evidence to inform landscape planning and design decisions aimed at conserving bird communities.
Data availability
The datasets generated and/or analyzed during the current study are available in the Supplementary Information.
References
Albrecht J, Peters MK, Becker JN, Behler C, Classen A, Ensslin A, Ferger SW, Gebert F, Gerschlauer F, Helbig-Bonitz M, Kindeketa WJ, Kühnel A, Mayr AV, Njovu HK, Pabst H, Pommer U, Röder J, Rutten G, Schellenberger Costa D, Schleuning M (2021) Species richness is more important for ecosystem functioning than species turnover along an elevational gradient. Nat Ecol Evol 5(12):1582–1593
Allouche O, Kalyuzhny M, Moreno-Rueda G, Pizarro M, Kadmon R (2012) Area–heterogeneity tradeoff and the diversity of ecological communities. Proc Natl Acad Sci 109(43):17495–17500
Anderle M, Brambilla M, Hilpold A, Matabishi JG, Paniccia C, Rocchini D, Rossin J, Tasser E, Torresani M, Tappeiner U, Seeber J (2023) Habitat heterogeneity promotes bird diversity in agricultural landscapes: Insights from remote sensing data. Basic Appl Ecol 70:38–49
Aubrechtová E, Bydžovská T, Horák J (2024) Blue-green infrastructure and biodiversity: urbanization and forestation have an important influence on bird diversity in water habitats. Urban for Urban Green 91:128151
Bain GC, MacDonald MA, Hamer R, Gardiner R, Johnson CN, Jones ME (2020) Changing bird communities of an agricultural landscape: declines in arboreal foragers, increases in large species. R Soc Open Sci 7(3):200076
Belfrage K, Björklund J, Salomonsson L (2015) Effects of farm size and on-farm landscape heterogeneity on biodiversity—case study of twelve farms in a Swedish landscape. Agroecol Sustain Food Syst 39(2):170–188
Betts MG, Forbes GJ, Diamond AW (2007) Thresholds in songbird occurrence in relation to landscape structure. Conserv Biol 21(4):1046–1058
Betts MG, Wolf C, Pfeifer M, Banks-Leite C, Arroyo-Rodríguez V, Ribeiro DB, Barlow J, Eigenbrod F, Faria D, Fletcher RJ, Hadley AS, Hawes JE, Holt RD, Klingbeil B, Kormann U, Lens L, Levi T, Medina-Rangel GF, Melles SL, Ewers RM (2019) Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366(6470):1236–1239
Boecklen WJ (1986) Effects of habitat heterogeneity on the species-area relationships of forest birds. J Biogeogr 13(1):59
Bonfim FCG, Dodonov P, Cazetta E (2021) Landscape composition is the major driver of the taxonomic and functional diversity of tropical frugivorous birds. Landsc Ecol 36(9):2535–2547
Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (eds) (2009) Introduction to meta-analysis (Nachdr). Wiley, New York
Carrara E, Arroyo-Rodríguez V, Vega-Rivera JH, Schondube JE, De Freitas SM, Fahrig L (2015) Impact of landscape composition and configuration on forest specialist and generalist bird species in the fragmented Lacandona rainforest, Mexico. Biol Cons 184:117–126
Cauvy-Fraunié S, Dangles O (2019) A global synthesis of biodiversity responses to glacier retreat. Nat Ecol Evol 3(12):1675–1685
Ceballos G, Ehrlich PR, Barnosky AD, García A, Pringle RM, Palmer TM (2015) Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci Adv 1(5):e1400253
Chiron F, Lorrillière R, Bessa-Gomes C, Tryjanowski P, Casanelles-Abella J, Laanisto L, Leal A, Van Mensel A, Moretti M, Muyshondt B, Niinemets Ü, Ortí MA, Pinho P, Samson R, Deguines N (2024) How do urban green space designs shape avian communities? Testing the area–heterogeneity trade-off. Landsc Urban Plan 242:104954
Cox DTC, Hudson HL, Plummer KE, Siriwardena GM, Anderson K, Hancock S, Devine-Wright P, Gaston KJ (2018) Covariation in urban birds providing cultural services or disservices and people. J Appl Ecol 55(5):2308–2319
Deschênes M, Bélanger L, Giroux J-F (2003) Use of farmland riparian strips by declining and crop damaging birds. Agr Ecosyst Environ 95(2–3):567–577
Dong K, Li Y, Li D, Hu W, Xu G (2023) Effects of environmental factors on avian communities in urban parks in small- to medium-sized city: a case study of Fuyang City, Anhui. China Environ Monit Assess 195(11):1347
Duarte GT, Santos PM, Cornelissen TG, Ribeiro MC, Paglia AP (2018) The effects of landscape patterns on ecosystem services: meta-analyses of landscape services. Landsc Ecol 33(8):1247–1257
Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315(7109):629–634
Ewers RM, Didham RK (2005) Confounding factors in the detection of species responses to habitat fragmentation. Biol Rev 81(01):117
Ewers RM, Didham RK (2007) Habitat fragmentation: panchreston or paradigm? Trends Ecol Evol 22(10):511
Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol Syst 34(1):487–515
Feng Y, Liu Y (2015) Fractal dimension as an indicator for quantifying the effects of changing spatial scales on landscape metrics. Ecol Ind 53:18–27
Ferenc M, Sedláček O, Mourková J, Exnerová A, Škopek J, Formánek J, Fuchs R (2016) Disentangling the influences of habitat availability, heterogeneity and spatial position on the species richness and rarity of urban bird communities in a central European city. Urban Ecosyst 19(3):1265–1281
Fischer HW, Chhatre A, Duddu A, Pradhan N, Agrawal A (2023) Community forest governance and synergies among carbon, biodiversity and livelihoods. Nat Clim Chang 13(12):1340–1347
Fischer J, Lindenmayer DB (2007) Response to Ewers and Didham: untangling the complex ecology of modified landscapes. Trends Ecol Evol 20(10):512
Fjellstad WJ, Dramstad WE, Strand G-H, Fry GLA (2001) Heterogeneity as a measure of spatial pattern for monitoring agricultural landscapes. Nor Geogr Tidsskr 55(2):71–76
Guan B, Gao J, Chen W, Gong X, Ge G (2021) The effects of climate change on landscape connectivity and genetic clusters in a small subtropical and warm-temperate tree. Front Plant Sci 12:671336
Guénette J, Villard M (2005) Thresholds in forest bird response to habitat alteration as quantitative targets for conservation. Conserv Biol 19(4):1168–1180
Haddad NM, Brudvig LA, Clobert J, Davies KF, Gonzalez A, Holt RD, Lovejoy TE, Sexton JO, Austin MP, Collins CD, Cook WM, Damschen EI, Ewers RM, Foster BL, Jenkins CN, King AJ, Laurance WF, Levey DJ, Margules CR, Townshend JR (2015) Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci Adv 1(2):1500052
Hedges LV, Tipton E, Johnson MC (2010) Robust variance estimation in meta-regression with dependent effect size estimates. Res Synth Methods 1(1):39–65
Henden J-A, Yoccoz NG, Ims RA, Langeland K (2013) How spatial variation in areal extent and configuration of labile vegetation states affect the riparian bird community in arctic tundra. PLoS ONE 8(5):e63312
Huggett AJ (2005) The concept and utility of ‘ecological thresholds’ in biodiversity conservation. Biol Cons 124(3):301–310
Jacobson AP, Riggio J, Tait M, Baillie EM (2019) Global areas of low human impact (‘Low Impact Areas’) and fragmentation of the natural world. Sci Rep 9(1):14179
Jahani A, Kalantary S, Alitavoli A (2021) An application of artificial intelligence techniques in prediction of birds soundscape impact on tourists’ mental restoration in natural urban areas. Urban for Urban Green 61:127088
Jin H, Xu J, Peng Y, Xin J, Peng N, Li Y, Huang J, Zhang R, Li C, Wu Y, Gong B, Wang R (2023) Impacts of landscape patterns on plant species diversity at a global scale. Sci Total Environ 896:165193
Koricheva J, Gurevitch J, Mengersen KL (2013) Handbook of meta-analysis in ecology and evolution. Princeton University Press, Princeton
Kroeger SB, Hanslin HM, Lennartsson T, D’Amico M, Kollmann J, Fischer C, Albertsen E, Speed JDM (2022) Impacts of roads on bird species richness: a meta-analysis considering road types, habitats and feeding guilds. Sci Total Environ 812:151478
Krummel JR, Gardner RH, Sugihara G, O’Neill RV, Coleman PR (1987) Landscape patterns in a disturbed environment. Oikos 48(3):321
Laiolo P (2002) Effects of habitat structure, floral composition and diversity on a forest bird community in north-western Italy. Folia Zool 51(2):121–128
Lakatos T, Chamberlain DE, Garamszegi LZ, Batáry P (2022) No place for ground-dwellers in cities: a meta-analysis on bird functional traits. Global Ecol Conservat 38:e02217
Leveau LM (2019) Primary productivity and habitat diversity predict bird species richness and composition along urban-rural gradients of central Argentina. Urban for Urban Green 43:126349
Li D, Lee M, Xiao W, Tang J, Zhang Z (2020) Noncrop features and heterogeneity mediate overwintering bird diversity in agricultural landscapes of southwest China. Ecol Evol 10(12):5815–5828
Li L, Guo Y, Ma S, Wen H, Li Y, Qiao J (2024) Association between exposure to per- and perfluoroalkyl substances (PFAS) and reproductive hormones in human: a systematic review and meta-analysis. Environ Res 241:117553
Li N, Sun Y, Chu H, Qi Y, Zhu L, Ping X, Li C, Jiang Z (2019) Bird species diversity in Altai riparian landscapes: wood cover plays a key role for avian abundance. Ecol Evol 9(17):9634–9643
Lisón F, Matus-Olivares C, Troncoso E, Catalán G, Jiménez-Franco MV (2022) Effect of forest landscapes composition and configuration on bird community and its functional traits in a hotspot of biodiversity of Chile. J Nat Conserv 68:126227
Liu J, Liu X, Wang Y, Li Y, Jiang Y, Fu Y, Wu J (2020) Landscape composition or configuration: Which contributes more to catchment hydrological flows and variations? Landsc Ecol 35(7):1531–1551
Liu L, Wang Z, Ye Y, Qi K (2023) Effects of agricultural land types on microplastic abundance: a nationwide meta-analysis in China. Sci Total Environ 892:164400
Liu W, Yu D, Yuan S, Yi J, Cao Y, Li X, Xu H (2022) Effects of spatial fragmentation on the elevational distribution of bird diversity in a mountain adjacent to urban areas. Ecol Evol 12(7):e9051
Lomolino MV (2000) Ecology’s most general, yet protean pattern: the species-area relationship. J Biogeogr 27(1):17–26
Lorel C, Le Viol I, Plutzar C, Jiguet F, Mouchet M (2021) Linking the diversity and structure of French avian communities with landscape parameters, climate and NPP flows. Reg Environ Change 21(2):61
Lorenzón RE, Beltzer AH, Olguin PF, Ronchi-Virgolini AL (2016) Habitat heterogeneity drives bird species richness, nestedness and habitat selection by individual species in fluvial wetlands of the Paraná River, Argentina: Bird Species Richness and Habitat Heterogeneity. Austral Ecol 41(7):829–841
MacArthur RH, MacArthur JW (1961) On bird species diversity. Ecology 42(3):594–598
MacArthur R, Recher H, Cody M (1966) On the relation between habitat selection and species diversity. Am Nat 100(913):319–332
Macchi L, Baumann M, Bluhm H, Baker M, Levers C, Grau HR, Kuemmerle T (2019) Thresholds in forest bird communities along woody vegetation gradients in the South American Dry Chaco. J Appl Ecol 56(3):629–639
Mächler M, Rousseeuw P, Struyf A, Hubert M, Hornik K (2012) Cluster: cluster analysis basics and extensions. In R packages (vol. 1)
Marja R, Tscharntke T, Batáry P (2022) Increasing landscape complexity enhances species richness of farmland arthropods, agri-environment schemes also abundance: a meta-analysis. Agr Ecosyst Environ 326:107822
Martínez-Núñez C, Martínez-Prentice R, García-Navas V (2023) Land-use diversity predicts regional bird taxonomic and functional richness worldwide. Nat Commun 14(1):1320
Matuoka MA, Benchimol M, Almeida-Rocha JMD, Morante-Filho JC (2020) Effects of anthropogenic disturbances on bird functional diversity: a global meta-analysis. Ecol Ind 116:106471
McGarigal K, Marks BJ (1995). FRAGSTATS: Spatial pattern analysis program for quantifying landscape structure. (PNW-GTR-351; p. PNW-GTR-351). U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.
Mei W, Yu G, Greenwell B, Lai J, Mao Z, Umezawa Y, Zeng J, Wang J (2022). ggtrendline: an R package for adding trendline and confidence interval to ggplot.
Mohd-Azlan J, Lawes MJ (2011) The effect of the surrounding landscape matrix on mangrove bird community assembly in north Australia. Biol Cons 144(9):2134–2141
Molina-Marin DA, Rodas-Rua JC, Lara CE, Rivera-Páez FA, Fontúrbel FE, Castaño-Villa GJ (2022) Effects of landscape configuration on the body condition of migratory and resident tropical birds. Diversity 14(6):432
Muggeo VMR (2008) segmented: an R Package to fit regression models with broken-line relationships. 8.
Muruga P, Siqueira AC, Bellwood DR (2024) Meta-analysis reveals weak associations between reef fishes and corals. Nat Ecol Evolut. https://doi.org/10.1038/s41559-024-02334-7
Newbold T, Hudson LN, Hill SLL, Contu S, Lysenko I, Senior RA, Börger L, Bennett DJ, Choimes A, Collen B, Day J, De Palma A, Díaz S, Echeverria-Londoño S, Edgar MJ, Feldman A, Garon M, Harrison MLK, Alhusseini T, Purvis A (2015) Global effects of land use on local terrestrial biodiversity. Nature 520(7545):45–50
Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, D’amico JA, Itoua I, Strand HE, Morrison JC, Loucks CJ, Allnutt TF, Ricketts TH, Kura Y, Lamoreux JF, Wettengel WW, Hedao P, Kassem KR (2001) Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51(11):933
Ouin A, Cabanettes A, Andrieu E, Deconchat M, Roume A, Vigan M, Larrieu L (2015) Comparison of tree microhabitat abundance and diversity in the edges and interior of small temperate woodlands. For Ecol Manage 340:31–39
Pianka ER (1966) Latitudinal gradients in species diversity: a review of concepts. Am Nat 100(910):33–46
Planillo A, Fiechter L, Sturm U, Voigt-Heucke S, Kramer-Schadt S (2021) Citizen science data for urban planning: Comparing different sampling schemes for modelling urban bird distribution. Landsc Urban Plan 211:104098
Powell BF, Steidl RJ (2015) Influence of vegetation on montane riparian bird communities in the Sky Islands of Arizona, USA. Southwest Nat 60(1):65–71
Püttker T, Crouzeilles R, Almeida-Gomes M, Schmoeller M, Maurenza D, Alves-Pinto H, Pardini R, Vieira MV, Banks-Leite C, Fonseca CR, Metzger JP, Accacio GM, Alexandrino ER, Barros CS, Bogoni JA, Boscolo D, Brancalion PHS, Bueno AA, Cambui ECB, Prevedello JA (2020) Indirect effects of habitat loss via habitat fragmentation: a cross-taxa analysis of forest-dependent species. Biol Conserv 241:108368
Radford JQ, Bennett AF, Cheers GJ (2005) Landscape-level thresholds of habitat cover for woodland-dependent birds. Biol Cons 124(3):317–337
Rocchini D, Balkenhol N, Carter GA, Foody GM, Gillespie TW, He KS, Kark S, Levin N, Lucas K, Luoto M, Nagendra H, Oldeland J, Ricotta C, Southworth J, Neteler M (2010) Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Eco Inform 5(5):318–329
Ryser R, Hirt MR, Häussler J, Gravel D, Brose U (2021) Landscape heterogeneity buffers biodiversity of simulated meta-food-webs under global change through rescue and drainage effects. Nat Commun 12(1):4716
Santillán V, Quitián M, Tinoco BA, Zárate E, Schleuning M, Böhning-Gaese K, Neuschulz EL (2018) Spatio-temporal variation in bird assemblages is associated with fluctuations in temperature and precipitation along a tropical elevational gradient. PLoS ONE 13(5):e0196179
Santillán V, Quitián M, Tinoco BA, Zárate E, Schleuning M, Böhning-Gaese K, Neuschulz EL (2020) Direct and indirect effects of elevation, climate and vegetation structure on bird communities on a tropical mountain. Acta Oecologica 102:103500
Santini L, Belmaker J, Costello MJ, Pereira HM, Rossberg AG, Schipper AM, Ceaușu S, Dornelas M, Hilbers JP, Hortal J, Huijbregts MAJ, Navarro LM, Schiffers KH, Visconti P, Rondinini C (2017) Assessing the suitability of diversity metrics to detect biodiversity change. Biol Cons 213:341–350
Schindler S, Von Wehrden H, Poirazidis K, Wrbka T, Kati V (2013) Multiscale performance of landscape metrics as indicators of species richness of plants, insects and vertebrates. Ecol Ind 31:41–48
Semper-Pascual A, Burton C, Baumann M, Decarre J, Gavier-Pizarro G, Gómez-Valencia B, Macchi L, Mastrangelo ME, Pötzschner F, Zelaya PV, Kuemmerle T (2021) How do habitat amount and habitat fragmentation drive time-delayed responses of biodiversity to land-use change? Proc R Soc B 288(1942):20202466
Song J, Wan S, Piao S, Knapp AK, Classen AT, Vicca S, Ciais P, Hovenden MJ, Leuzinger S, Beier C, Kardol P, Xia J, Liu Q, Ru J, Zhou Z, Luo Y, Guo D, Adam Langley J, Zscheischler J, Zheng M (2019) A meta-analysis of 1119 manipulative experiments on terrestrial carbon-cycling responses to global change. Nat Ecol Evol 3(9):1309–1320
Squires TM, Yuda P, Akbar PG, Collar NJ, Devenish C, Taufiqurrahman I, Wibowo WK, Winarni NL, Yanuar A, Marsden SJ (2021) Citizen science rapidly delivers extensive distribution data for birds in a key tropical biodiversity area. Global Ecol Conserv 28:e01680
Steel ZL, Steel AE, Williams JN, Viers JH, Marquet PA, Barbosa O (2017) Patterns of bird diversity and habitat use in mixed vineyard-matorral landscapes of Central Chile. Ecol Ind 73:345–357
Stein A, Gerstner K, Kreft H (2014) Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol Lett 17(7):866–880
Stevenson SL, Watermeyer K, Ferrier S, Fulton EA, Xiao H, Nicholson E (2024) Corroboration and contradictions in global biodiversity indicators. Biol Cons 290:110451
Strohbach MW, Lerman SB, Warren PS (2013) Are small greening areas enhancing bird diversity? Insights from community-driven greening projects in Boston. Landsc Urban Plan 114:69–79
Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, de Siqueira MF, Grainger A, Hannah L, Hughes L, Huntley B, van Jaarsveld AS, Midgley GF, Miles L, Ortega-Huerta MA, Peterson AT, Phillips OL, Williams SE (2004) Extinction risk from climate change. Nature 427:5
Tilman D, Clark M, Williams DR, Kimmel K, Polasky S, Packer C (2017) Future threats to biodiversity and pathways to their prevention. Nature 546(7656):73–81
Tobias JA, Şekercioğlu ÇH, Vargas FH (2013) Bird conservation in tropical ecosystems: challenges and opportunities. In: Macdonald DW, Willis KJ (eds) Key topics in conservation biology. Wiley, New York, pp 258–276
Turner MG (1990) Spatial and temporal analysis of landscape patterns. Landsc Ecol 4(1):21–30
Turner MG (2005) Landscape ecology: what is the state of the science? Annu Rev Ecol Evol Syst 36(1):319–344
Turner MG, Gardner RH (2015) Landscape ecology in theory and practice: pattern and process. Springer, New York
Viechtbauer W (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36(3):78
Vieira-Alencar JPS, Bolochio BE, Carmignotto AP, Sawaya RJ, Silveira LF, Valdujo PH, Nogueira CDC, Nori J (2023) How habitat loss and fragmentation are reducing conservation opportunities for vertebrates in the most threatened savanna of the World. Perspect Ecol Conserv 21(2):121–127
Weeks TL, Betts MG, Pfeifer M, Wolf C, Banks-Leite C, Barbaro L, Barlow J, Cerezo A, Kennedy CM, Kormann UG, Marsh CJ, Olivier PI, Phalan BT, Possingham HP, Wood EM, Tobias JA (2023) Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds. Nat Ecol Evol 7(7):1079–1091
Wright DH (1983) Species-energy theory: an extension of species-area theory. Oikos 41(3):496
Wu Q, Li G, Zhao C-Y, Na X-L, Zhang Y-B (2023) Association between phthalate exposure and obesity risk: A meta-analysis of observational studies. Environ Toxicol Pharmacol 102:104240
Yang Y, Konrath S (2023) A systematic review and meta-analysis of the relationship between economic inequality and prosocial behaviour. Nat Hum Behav 7(11):1899–1916
Zuckerberg B, Ribic CA, McCauley LA (2018) Effects of temperature and precipitation on grassland bird nesting success as mediated by patch size. Conserv Biol 32(4):872–882
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Lu, X., Jia, Y. & Wang, Y. The effect of landscape composition, complexity, and heterogeneity on bird richness: a systematic review and meta-analysis on a global scale. Landsc Ecol 39, 132 (2024). https://doi.org/10.1007/s10980-024-01933-w
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DOI: https://doi.org/10.1007/s10980-024-01933-w