Conservation of Native Tree Species in The Agroforest of Rice-Based Agroecosystems Will Contribute to The Sustainable Agriculture

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The present study analyses the agroforests associated with the rice farming system of three different agricultural practices for biodiversity richness by comparing two parameters: plants and birds. Out of the 9 study sites, 3 sites were traditional farms maintained by Kurichiya tribal communities, 3 were natural farms, and the other 3 farms were modern. A total of 45 families, 104 genera, 128 species of plants, and 101 bird species belonged to 48 families, and 17 orders were identified from the study sites. The sample-size-based rarefaction and extrapolation (R/E) method was adopted to identify estimated biodiversity indices. Renyi profile was used to understand the native tree diversity profile of the selected sites. The result of this study indicates that bird diversity is positively correlated with native tree diversity and NDVI of May and October. Conserving more native trees in the farmland could be one of the reasons for the sustainable agriculture system of the Kurichiya tribal community as it attracts more bird species and contributes to the biological control of pests. Thus, the conservation of native tree species in the agroforest of rice-based agroecosystems will contribute to the sustainable agriculture system. Traditional agriculture Wayanad Kurichiya Native trees NDVI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Nature-based Solutions (NbS) have made tremendous progress in recent years, improving the environment's ecosystem functions (Miralles 2021 ). Traditional agriculture practice incorporates many NbS and is also identified as a climate-smart approach (Singh and Singh 2017 ). It relies on ecosystem services and mixed cropping patterns for sustainable food production and it can decrease the risk of crop failure, pests, and disease (Sauerborn, Sprich, and Mercer-Quarshie 2000). Traditionally managed rice-based agroecosystems are a typical example of a sustainable food production method as it involves diversified farmland (Lawler 2001 ) with the specific feature of avoiding chemical inputs and maintaining an average yield yet stable for thousands of years (Roger and Kurihara 1991 ). The importance of biological control in rice pest management has also gained attention, particularly in the tropics (Ooi and Waage 1994 ). Traditional agriculture is also known for its aesthetic, natural, cultural, historical, and socio-economic values (Baránková, Dobrovodská, and Štefunková 2011 ; Lieskovský et al. 2015). The regions where traditional farming landscapes occur had the same farming practices for a long period (Fischer, Hartel, and Kuemmerle 2012). Some of the finest examples of these regions include the Western Ghats of India, the Satoyama landscapes in Japan, the Milpa cultivation systems in Mexico, traditional village systems in Eastern Europe, and South-western China’s terrace landscapes(Fischer, Hartel, and Kuemmerle 2012; Hartel et al. 2010; Liu, Duan, and Yu 2012 ; McNeely and Schroth 2006; Ranganathan et al. 2008; Robson and Berkes 2011; Takeuchi 2010 ). Natural farming is another type of farming practice and is very similar to traditional farming methods as it is also relying on non-chemical inputs. In India, this farming practice utilizes eco-friendly bio inputs such as Jivamrit and Beejamrit in the farmlands instead of direct usage of cow dung and other natural products ( https://naturalfarming.niti.gov.in/ ). Whereas, conventional or modern farming relies on chemical inputs and extensive use of modern types of machinery. The farming practice has got significant implications for wild species of flora and fauna. Moreover, mechanical operations associated with agricultural activity can have negative impacts on particular groups of species such as birds (McLaughlin and Mineau 1995). There is evidence that organic farming and shaded agroforestry have positive impacts on the abundance and species richness of bat diversity (Park 2015). The present study intended to analyze the biodiversity associated with three farming practices, viz., Traditional Farming (TF), Natural Farming (NF), and Modern Farming (MF). All the selected study sites were rice-based agroecosystems. In a rice-based agroecosystem, biodiversity refers to the variability that exists at the habitat, species, and genetic levels. Habitat diversity refers to the patchwork of rice and non-rice habitats of the paddy landscape. The non-rice habitat includes the bunds, surrounding or adjacent grasslands swamps, or forests. Species diversity refers to the richness and relative abundance of rice pests and their natural enemies, weeds, and microorganisms in the rice ecosystem. Genetic diversity refers to the diversity of rice germplasm and commercial cultivars used by farmers (Borromeo 2001). For this study, we considered Habitat diversity . Biodiversity richness in these agriculture systems was compared by considering two parameters viz., plants and birds as both are indicators of environmental integrity (Bibby 1999 ; Fagan and Peart 2004 ; Galewski and Devictor 2016; Godet, Jaffré, and Devictor 2011; Gregory et al. 2005, 2019 ; LaPaix, Freedman, and Patriquin 2009; McGraw and Furedi 2005; McKinney 2006 ; Pykälä 2004 ). Birds can respond to environmental changes over many spatial scales (Temple and Wiens 1989) which makes them one of the good bioindicators. Moreover, they are comparatively diverse (~ 10,000 species globally) and tend to be at, or near the top of the food chain which makes them sensitive to changes at lower trophic levels (Gregory et al. 2005). Materials and methods Study area This study was conducted in the Wayanad district located in the North-Eastern region of Kerala, India. Wayanad district is the home of Kerala’s heritage rice farming system. This region resides on the crest of the Western Ghats, one of the 36 Biodiversity hotspots in the world and known for its rich abundance of flora and ethnic cultures. Agriculture is the backbone of the economy for this district. Many traditional rice varieties and traditional cultivating practices are still used in the district by the ethnic groups. Kurichiya is one among those ethnic groups and also conservers of the traditional rice farming system. They keep large land holdings, big herds of cattle, and they follow a family farming approach to ensure the cultural, culinary, and curative needs of the family and which is considered a sustainable agriculture system (Anil and Vedavalli 2016). Switching of farm practices from traditional to modern and rapid urban developmental activities is seen as a trend in the district. Moreover, as part of urbanization and other activities of land conversion, the changes in ecosystems are visible. About 40% of the total land area of Wayanad district is under a forest-protected zone (Government of Kerala 2016 ). From 2001 to 2021, Wayanad lost 2.65kha of tree cover, equivalent to a 1.6% decrease in tree cover since 2000. From 2002 to 2021, Wayanad lost 66ha of humid primary forest, making up 2.5% of its total tree cover loss in the same period. The total area of humid primary forest in Wayanad decreased by 0.24% in this period ( https://www.globalforestwatch.org/ ). Therefore, the study of biodiversity associated with agroecosystems such as rice fields is significant in this district as the maintenance of biological diversity is essential for ecologically sustainable agriculture (Pimentel et al. 1992). A total of 9 rice-based agroecosystems were selected for this study. Out of the 9 sites, 3 sites were traditional farms maintained by Kurichiya tribal community (T1, T2, T3), 3 were natural farms (N1, N2, N3) and another 3 farms were modern (M1, M2, M3). The geographic location of the study sites (Fig. 1 ) and other details of the location is depicted in Table 1 . Table 1 Location of study sites Study Sites Type of farming Latitude Longitude Average Rainfall (mm) Average Temperature (℃) T1 Traditional 11 0 38' 31.42" N 76 0 19' 02.25" E 2635.13 27.21 T2 Traditional 11 0 41' 10.59"N 76 0 05' 25.82"E 2402.59 28.93 T3 Traditional 11 0 51' 59.50" N 76 0 02' 52.24" E 2394.90 28.13 N1 Natural 11 0 41' 14.99"N 75 0 58' 42.67" E 2493.49 28.27 N2 Natural 11 0 42' 48.93"N 76 0 08' 22.65" E 2092.23 28.16 N3 Natural 11 0 35' 55.52"N 76 0 17' 38.76" E 2157.48 28.28 M1 Modern 11 0 47' 15.98" N 75 0 54' 23.35" E 2637.98 27.71 M2 Modern 11 0 43' 25.10" N 76 0 05' 42.89"E 2313.04 28.49 M3 Modern 11 0 35' 44.31"N 76 0 18' 03.10" E 1804.14 28.1 Data Collection Plant diversity The plant diversity survey was conducted between March to May 2022. The data was collected from agroforests of 9 sites by establishing a Cartesian grid of 100 × 100 m (10,000 m 2 ). Further, each minor grid was marked in 25 × 25 m and all corners were temporarily marked during the survey. All the trees and shrubs were identified up to the species level (Gamble 1915 ). The native trees were identified using the database maintained by Kerala Forest Research Institute ( http://keralaplants.in ). Renyi profile was determined to understand the native tree diversity in the sites (Fig. 3 ). The identified plants with their common name, scientific name, family, nativity, flowering, and fruiting period were demonstrated in Supplementary Material I. Bird diversity The bird survey was conducted from February to May 2022, a period when migrant birds are still present and most residents were active. Two habitats in each site were considered for the bird survey viz., paddy field and agroforest. Two-point counts in each habitat were conducted and a transect walk was taken between the area of the paddy field and the agroforest. A total of 36-point counts and 9 transect walks were conducted for this study. Every survey was carried out during the early morning hours (06.30 am – 10.30 am). Foggy and rainy days were avoided for the survey. Birds were detected visually or aurally by two detector teams and counted. Digital apps such as BirdNET(Kahl et al. 2021 ) and Merlin ( https://merlin.allaboutbirds.org/ ) also supported the bird survey. The primary dietary guild of the birds was identified from the website www.birds.cornell.edu . The identified birds with their common name, scientific name, family, IUCN status, and movement were demonstrated in Supplementary Material II. Alpha Diversity Alpha-diversity was calculated using the iNEXT package (Chao et al. 2014) by enumerating Hill numbers of order q: species richness (q = 0), Shannon diversity (q = 1, the exponential of Shannon entropy), and Simpson diversity (q = 2, the inverse of Simpson concentration). Of the three diversity indices, species richness gives equal weightage to common and rare species, Shannon diversity emphasizes the contribution of common species and Simpson diversity give weightage to the contributions of abundant species. Sample-size-based (or size-based) rarefaction and extrapolation (R/E) were adopted in this study as it computes diversity estimates for rarefied and extrapolated samples up to an appropriate size with 95% confidence intervals(Chao et al. 2014). We used rarefaction and extrapolation to avoid bias in estimates of species diversity in plots as the number of individuals may vary with the farming system. All the diversity indices are natural-log-transformed to meet model assumptions of normality. We also projected the species accumulation curves for the diversity indices using the sample-based rarefaction and extrapolation method (Fig. 2 ). To find the correlated variables to bird diversity we adopted the dimensionality reduction method using Principal Component Analysis (PCA) (Fig. 4 ). Remotely sensed data The Normalized Difference Vegetation Index (NDVI) time series data was derived for each site from the MODIS sensor (MODerate Resolution Imaging Spectroradiometer) on board the Terra satellite(Didan 2021 ). The NDVI is obtained from the reflectance of the vegetation in red and infrared portions of the spectrum and which is also a normalized spectral index (Tucker 1979 ). The data composition was done by one NDVI value for every 16 days from 2012 to 2022 at 250 m spatial resolution (MOD13Q1 product). NDVI is computed from the reflectance of the vegetation in red (R) and infrared (NIR) portions of the spectrum shown in equation (XYZ) (Parida, Kushwaha, and Ranjan 2022 ; Tucker 1979 ). $$NDVI=\frac{NIR-R}{NIR+R}$$ 1 Statistics Pearson correlation was performed for understanding the correlation among the categorical variables using R statistical and programming environment. Results Plant diversity A total of 45 families, 104 genera, and 128 species were identified from the study sites. This study recorded 7325 individuals (Supplementary Material I). Dominant species in all the sites were Coffea arabica and Areca catechu . The dominant native tree species was Artocarpus heterophyllus in 5 sites viz., T2, N1, N3, M1, and M3. Terminalia paniculate , Olea dioica , Aporosa lindleyana , and Mangifera indica were the dominant native tree species at the sites T1, T3, N2, and M2 respectively. Out of 128 species identified 92 were native species and 36 were exotic. The species richness of native tree species and abundance was comparatively high in all the traditional farms (T1, T2, and T3). The Renyi diversity profile shows that four sites had more diversified native tree species. Site number 1–3 represents traditional farms and site number 4 represents a natural farm (N1). The least diversified native tree sites were 8 and 9 which represent two modern farms. Artocarpus heterophyllus ranked first in native trees followed by Mangifera indica , Erythrina variegata , Olea dioica , and Artocarpus hirsutus . Bird diversity This study recorded 101 bird species belonging to 48 families and 17 orders. A total of 2051 individuals were identified. Out of the 101 bird species, 16 were migrant species. Some of the spotted birds at the study sites are depicted in Fig. 7 . Bird species richness and abundance were further categorized by the primary dietary guild. The percentage of the number of species belonging to each category at each site is as follows. Out of the all identified species, the majority of the species was belonging to the Insectivorous category. In TF and NF, it was 45%, and in MF, it was 40%. Similarly, TF had a comparatively large number of species belonging to the Omnivorous category (TF-16%, NF- 13%, and MF-15%). The percentage of the Frugivorous category was high in MF followed by TF (TF- 14%, NF-12%, and MF-16%). More number of the Carnivorous category was identified at NF (TF- 13%, NF- 17%, and MF- 14%). In the case of Granivorous species, which are known as pests in rice fields MF had more compared to other farming systems (TF- 7%, NF-8%, and MF-10%). All the farming systems had an equal percentage of species of Nectarivorous (4%) and Herbivorous (1%) (Table 3 ). Pearson correlation test was performed further to understand the relationship between the dietary guild categories and the Shannon index of native trees. It is observed that the Insectivorous, Omnivorous, and Carnivorous categories had a positive correlation with the native trees. All three categories had a correlation coefficient > 0.42. Similarly, Granivorous species had a negative correlation with the native trees (-0.37). Alpha Diversity Diversity estimates for the bird, native trees, and total trees were calculated using the iNEXT package. The estimated species richness ranged for birds from 35.5 to 120.18, and it was high in site T3 (120. 18) whereas, the highest Shannon and lowest Simpson indexes were found at sites M1 and N3 (41.26 and 4.46 respectively). Both the indexes ranged from 11.19 to 41.26, and 35.37 to 4.46 respectively. In the case of the native tree, the estimated species richness ranged from 14.23 to 55.06, the Shannon index ranged from 4.92 to 26.39, and the Simpson index ranged from 19.03 to 3.14. Moreover, species richness was high in site N1 (55.06), the Shannon index was high in T3 (26.39), and the Simpson index was least in M3 (3.14). Similarly, the estimated species richness for total trees ranged from 40.07 to 82.36, the Shannon index ranged from 5 to 17.53, and the Simpson index ranged from 17.7 to 2.1. The highest species richness and lowest Simpson index for total trees were observed in site N1, and the highest Shannon index was found in M1 (Table 2 ). All the indices were natural-log-transformed for principal component analysis. Table 2 Estimated diversity indices based on rarefaction and extrapolation method Sites Birds Total Trees Native Trees Species Richness Shannon Index Simpson Index Species Richness Shannon Index Simpson Index Species Richness Shannon Index Simpson Index T1 40.53 34.61 31.46 40.07 10.87 5.41 42.73 22.29 16.28 T2 45.04 19.68 10.49 50.05 24.7 17.7 50.05 25.76 19.03 T3 120.18 20.86 7.19 51.06 8.41 3.04 49.03 26.39 17.02 N1 61.21 28.37 15.62 82.36 5.41 2.1 55.06 21.96 13.24 N2 54.46 23.08 11.37 77.63 5.1 2.79 41.85 20.96 13.77 N3 35.99 11.19 4.46 40.99 7.16 3.46 28.43 6.57 4.1 M1 49.14 41.26 35.37 49.18 17.53 10.39 21.98 11.17 6.5 M2 65.83 27.54 19.45 40.11 9.68 4.41 24.65 16.23 12.95 M3 35.5 22.64 15.88 46.64 5 2.48 14.23 4.92 3.14 Table 3 Number of bird species identified in each primary dietary guild Site Insectivorous Omnivorous Frugivorous Carnivorous Granivorous Nectarivorous Herbivorous T1 19 7 5 6 4 2 0 T2 17 8 5 6 3 0 1 T3 27 7 9 6 3 3 1 N1 22 8 7 10 3 0 0 N2 22 7 5 7 3 3 1 N3 17 3 5 6 5 2 1 M1 18 9 8 5 3 1 1 M2 20 5 8 9 8 2 1 M3 17 6 6 5 3 2 0 In principal component analysis, the first axis explained 43.4%, and the second axis 28.9% of all data variation. Three categories of variables were considered for PCA viz., diversity indices of the bird at 9 sites, diversity indices of native trees in the corresponding site, and mean NDVI for each month from 2012 to 2022 time series. The angle between the vectors of variables explains their correlation in this space. Small angles represent a high positive correlation, right angles (90 0 ) represent a lack of correlation, and opposite angles represent a high negative correlation. As indices of Shannon and Species Richness (SR) for bird diversity fall under the same group, this group of variables was further considered for dimensionality reduction. It is observed that the indices for native tree diversity and mean NDVI for May, June, August, and October had a compositional relationship with the Shannon and SR indices of bird diversity. As part of dimensionality reduction Shannon index of bird diversity and the Shannon index of native tree diversity among other indices was considered for further analysis. Pearson correlation test was conducted to explore the relationship between the Shannon index of bird diversity and the Shannon index of native tree diversity. A significant correlation between them (r = 0.75, p < 0.05) was identified (Fig. 5 ). Moreover, a similar trend was followed by the Shannon index of birdy diversity and the Shannon index of Native Tree (NT) diversity when compared to the Shannon index of Total Tree (TT) diversity (Fig. 5 . d). NDVI and birds It was identified that species richness and mean NDVI values were following the same trend (Fig. 6 . C). Pearson correlation test was conducted for the Shannon index of birds and mean NDVI of May, June, August, and October. Correlation coefficients for mean NDVIs in May, October, August, and June were found to be 0.63, 0.62, 0.5, and 0.32 respectively. The NDVI value was equally distributed in each study site irrespective of the farming practice (Fig. 6 b). Discussion Kurichiya community and native tree conservation Though, a minor community when compared with the population of other tribal communities in the Wayanad district, Kurichiya is one of the largest joint families ever reported in anthropological literature (Kumaran 1996 ). The Kurichiya joint family consists of a single-house complex that consists of more than 100 members holding a considerably large area of land (Ravi Varma 2004 ) and which includes one or two hills and a large area of wetland between them. The abundance of native tree species in their farmland is due to their conservation of sacred groves and their religious beliefs constrained them to damage the vegetation in the specified region (Vaman D and Madhav 1981). The result of the Renyi profile was in agreement with the conservation practice followed by Kurichiya tribes. Site N1 had a similarity in tree diversity with the traditional farms except for farming practices. This was also reflected in the Renyi profile. Relativity of native trees, NDVI, and birds One of the major ecosystem services provided by biodiversity is the controlling of herbivorous insect populations by insectivorous birds (Cagan H 2006 ; Wenny et al. 2011 ). The rice farming system in Wayanad is associated with 16 species of birds rendering invaluable services to rice crops by controlling 10 species of pests in rice (Vishnudas 2007 ). The percentage of insectivorous species was high in both TF and NF sites. Out of the three natural farms, N1, and N2 sites followed an integrated farming system by conserving a moderate number of native trees among the cash crops. Site N3 had fewer native trees compared to other sites. Interestingly, the presence of some bird species was only identified in TFs and they were Asian fairy bluebird ( Irena puella ), Brown-cheeked fulvetta ( Alcippe poioicephala ), Large-billed leaf warbler ( Phylloscopus magnirostris ), Brown-breasted flycatcher ( Muscicapa muttui ), White-bellied blue flycatcher ( Cyornis caerulatus ), Blue-capped rock thrush ( Monticola cinclorhynchus ) and Green bee-eater ( Merops orientalis ). The abundance of the native tree species Syzygium cumini can attract many insectivorous and frugivorous birds (Sinu, Shivanna, and Kuriakose 2012). This was evident in traditional farms. The Green bee-eater was spotted from T2 and T3 sites. The abundance of Ficus tsjahela in these sites could be the reason for spotting it (Vanitharani et al. 2009 ). Moreover, this tree species was not present in other farming systems. The presence of such native trees could be the reason for identifying the above-mentioned birds only in traditional farms. The other native trees present only in traditional farms were Alstonia scholaris , Terminalia paniculata, Sterculia foetida, Ficus exasperata, Tabernaemontana heyneana, Schleichera oleosa, Terminalia elliptica, Vateria indica, Callicarpa tomentosa, Semicarpus anacardium, Vitex altissima, Macaranga indica, Bischofia javanica Blume, Elaeocarpus tuberculatus, Naringi crenulata, Trichilia connaroides, Arenga wightii Griffith, Ochlandra scriptoria, Canarium strictum, Diospyrous ovalifolia Wigh, and Gnetum ula. The increased diversity of bird species w.r.t. native trees are also reported in previous studies ((Dyson 2020 ). Moreover, Kurichiya tribes are very specific about eating pure and toxin-free food, therefore no external chemical inputs are applied in their rice farming system. This purity in cultivation would also influence the bird species richness (Anil Kumar et al. 2022 ). It is reported that there is a strong positive correlation between bird species richness and the maximum mean NDVI value (Boniface, Andrew, and Rolf 2000). The result of this study is also in agreement with this (Fig. 6 c). Among the selected data set, May had NDVI values between 0.6 to 0.7 and for October it was between 0.7 and 0.8. The other two months viz., June and August had NDVI values < 0.6. Another study has reported that bird species richness was noticeably high at NDVI values between 0.3 and 0.4 (Lucas M. and Federico I. 2021). Whereas, in this study, the highest NDVI value (0.8) and highest bird species richness (120. 18) was observed in site T3. This indicates that the highest values of NDVI are associated with the highest values of bird species richness. Conclusion The result of this study indicates that bird diversity is positively correlated with native tree diversity and mean NDVI of May, October, and August (r > 0.5). A significant correlation between the Shannon index of bird diversity and the Shannon index of native trees (r = 0.75, p 0.42. Similarly, Granivorous species had a negative correlation with the native trees (r= -0.37). This indicates that agroforests with diversified native trees can attract many favorable bird species and can reduce the Granivorous birds which are considered a pest in rice fields. Conserving more native trees in the farmland could be one of the reasons for the sustainable agriculture system of the Kurichiya tribal community. Considerable variation in NDVI was not observed in the three farming systems. It is because of the similar tree density in all the sites, except for variation in the number of native tree species. Thus, the conservation of native tree species in the agroforest of rice-based agroecosystems will contribute to the sustainable agriculture system. Declarations Acknowledgments We thank all the conservationists, farmers, and researchers who gave us advice during the selection of study sites or helped with the surveys with field assistance, especially Ajayan (Biodiversity Management Committee), Salim Pichan (MSSRF), Deepak (KFRI), Telna Sebastian (MSSRF), and Remel (MSSRF). Funding The work was supported by the Department of Science and Technology (DST), Government of India for the funding under Women Scientist- B program (DST/WOS-B/AFE-3/2021). Author Merlin Lopus has received research support from DST. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Merlin Lopus, Reshma D, Subaiba Shafi, Abdulla Habeeb, and Amit Kushwaha. The first draft of the manuscript was written by Merlin Lopus and critical revisions were made by Deepak Jaiswal and Shakeela V. All authors read and approved the final manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Conflict of Interest The authors have no conflicts of interest to declare. References Anil Kumar N, Merlin Lopus, Telapurath Raveendran, Vipin Das (2022) Making Rice-Farming System More Climate Resilient and Nutrition Sensitive: Heritage of Kurichiya Tribe Community of Western Ghats. In Innovation in Small-Farm Agriculture, 1 st edn. Boca Raton: CRC Press, pp 287-98. http://dx.doi.org/10.1201/9781003164968-29.1 Anil Kumar, Vedavalli (2016) Conservation of Family Farming Heritage. In Family Farming- Meeting the Zero Hunger Challenge, New Delhi: FAO and MSSRF, pp 245–63.https://www.fao.org/3/i5650e/i5650e.pdf. Baránková Z, Dobrovodská M, Štefunková D (2011) Participation of Local People on Identifying the Landscape Values and Future Development in Historical Agricultural Landscapes. Ekológia (Bratislava) 30(2): 216–28. Bibby, Colin J (1999) Making the Most of Birds as Environmental Indicators. Ostrich 70(1): 81–88. Boniface, Oindo K, Skidmore A, de By Rolf (2000) Interannual Variability of NDVI and Species Richness in Kenya. International Archives of Photogrammetry and Remote Sensing 33: 1402–9. Borromeo, Teresita H (2001) Biodiversity and the Quest for Sustainable Rice Production Systems. Philipp J Crop Sci 26 (1): 15–20. Cagan H, Sekercioglu (2006) Increasing Awareness of Avian Ecological Function. Trends in Ecology & Evolution 21(8): 464–71. Chao A, Nicholas J (2014) Rarefaction and Extrapolation with Hill Numbers: A Framework for Sampling and Estimation in Species Diversity Studies. Ecological Monographs 84 (1): 45–67. Didan K (2021) MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13Q1.061 (Accessed on January 18, 2023). Dyson K (2020) Conserving Native Trees Increases Native Bird Diversity and Community Composition on Commercial Office Developments. Journal of Urban Ecology 6(1): juaa033. Fagan ME, Peart DR (2004) Impact of the Invasive Shrub Glossy Buckthorn (Rhamnus Frangula L.) on Juvenile Recruitment by Canopy Trees. Forest Ecology and Management 194(1–3): 95–107. Fischer J, Tibor H, Tobias K (2012) Conservation Policy in Traditional Farming Landscapes: Conserving Traditional Farming Landscapes. Conservation Letters 5(3): 167–75. Galewski T, Vincent D (2016) When Common Birds Became Rare: Historical Records Shed Light on Long-Term Responses of Bird Communities to Global Change in the Largest Wetland of France ed. Andy J Green. PLOS ONE 11(11): e0165542. Gamble JS (1915) Flora of the Presidency of Madras. London: Adlard and Son Ltd. Godet L, Mikaël J, Vincent D (2011) Waders in Winter: Long-Term Changes of Migratory Bird Assemblages Facing Climate Change. Biology Letters 7(5): 714–17. Government of Kerala (2016) District Survey Report of Minor Minerals- Wayanad District. Thiruvananthapuram: Department of Mining and Geology. https://dmg.kerala.gov.in/wp-content/uploads/2020/01/dsr_way.pdf. Gregory, Richard D (2005) Developing Indicators for European Birds. Philosophical Transactions of the Royal Society B: Biological Sciences 360(1454): 269–88. Gregory, Richard D, Jana S, Petr V, Simon Butler (2019) An Analysis of Trends, Uncertainty and Species Selection Shows Contrasting Trends of Widespread Forest and Farmland Birds in Europe. Ecological Indicators 103: 676–87. Hartel T (2010) Amphibian Distribution in a Traditionally Managed Rural Landscape of Eastern Europe: Probing the Effect of Landscape Composition. Biological Conservation 143(5): 1118–24. Kahl S, Connor MW, Maximilian E, Holger K (2021) BirdNET: A Deep Learning Solution for Avian Diversity Monitoring. Ecological Informatics 61: 101236. Kumaran V (1996) Kurichyarude Jeevithavum Samskaaravum. Kottayam: Current Books. LaPaix R, Bill F, David P (2009) Ground Vegetation as an Indicator of Ecological Integrity. Environmental Reviews 17: 249–65. Lawler, Sharon P (2001) Rice Fields as Temporary Wetlands: A Review. Israel Journal of Zoology 47(4): 513–28. Lieskovský J (2015) The Abandonment of Traditional Agricultural Landscape in Slovakia – Analysis of Extent and Driving Forces. Journal of Rural Studies 37: 75–84. Liu Y, Duan M, Yu Z (2012) Agricultural Landscapes and Biodiversity in China. Agric Ecosyst Environ 166: 46-54. Lucas M, Leveau, Isla FI (2021) Predicting Bird Species Presence in Urban Areas with NDVI: An Assessment within and between Cities. Urban Forestry & Urban Greening 63. https://doi.org/10.1016/j.ufug.2021.127199. McGraw, James B, Mary AF (2005) Deer Browsing and Population Viability of a Forest Understory Plant. Science 307(5711): 920–22. McKinney, Michael L (2006) Urbanization as a Major Cause of Biotic Homogenization. Biological Conservation 127(3): 247–60. McLaughlin, Alison, Pierre M (1995) The Impact of Agricultural Practices on Biodiversity. Agriculture, Ecosystems & Environment 55(3): 201–12. McNeely, Jeffrey A, Götz S (2006) Agroforestry and Biodiversity Conservation – Traditional Practices, Present Dynamics, and Lessons for the Future. Biodiversity and Conservation 15(2): 549–54. Miralles, Wilhelm (2021) Nature-Based Solutions in Agriculture Sustainable Management And Conservation of Land, Water And Biodiversity. Virginia: Food and Agriculture Organization of the United Nations and The Nature Conservancy. https://doi.org/10.4060/cb3140en. Ooi, PAC, Waage JK (1994) Biological Control in Rice: Applications and Research Needs. In Rice Pest Science and Management. Selected Papers from the International Rice Research Conference, Manila: International Rice Research Institute. http://books.irri.org/9712200515_content.pdf. Parida BR, Kushwaha A, Ranjan AK (2022) Synergy of Sentinel-2A and Near-Proximal Sensor Data for Deriving Biochemical Parameters of Paddy at Different Growth Stages. Environ Dev Sustain 24: 1048–68. Park, Kirsty J (2015) Mitigating the Impacts of Agriculture on Biodiversity: Bats and Their Potential Role as Bioindicators. Mammalian Biology 80(3): 191–204. Pimentel D (1992) Conserving Biological Diversity in Agricultural/Forestry Systems. BioScience 42(5): 354–62. Pykälä, Juha (2004) Immediate Increase in Plant Species Richness after Clear‐cutting of Boreal Herb‐rich Forests. Applied Vegetation Science 7(1): 29–34. Ranganathan J (2008) Sustaining Biodiversity in Ancient Tropical Countryside. Proceedings of the National Academy of Sciences 105(46): 17852–54. Ravi Varma KT (2004) Matriliny: Tribal Matriliny and Northern Systems. Thiruvananthapuram: State Institute of Languages. Robson, James P, Fikret B (2011) Exploring Some of the Myths of Land Use Change: Can Rural to Urban Migration Drive Declines in Biodiversity? .Global Environmental Change 21(3): 844–54. Roger PA, Kurihara Y (1991) The Floodwater Biology of Tropical Wetland Rice Fields. IBSRAM Monograph 2: 21 1-233. Sauerborn J, Sprich H, Mercer QH (2000) Crop Rotation to Improve Agricultural Production in Sub-Saharan Africa. Journal of Agronomy and Crop Science 184(1): 67–72. Singh R, Singh GS (2017) Traditional Agriculture: A Climate-Smart Approach for Sustainable Food Production. Energy, Ecology and Environment 2(5): 296–316. Sinu P, Shivanna K, Giby K (2012) Frugivorous Bird Diversity and Their Post-Feeding Behavior in Fruiting Syzygium Cumini (Myrtaceae) in Fragmented Forests of Central Western Ghats, India. Current Science 103: 1146–48. Takeuchi K (2010) Rebuilding the Relationship between People and Nature: The Satoyama Initiative. Ecological Research 25(5): 891–97. Temple SA, John AW (1989) Bird Populations And environmental changes: 43(2). Tucker CJ (1979) Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment 8(2): 127–50. Vaman D, Vartak, Gadgil M (1981) Studies on Sacred Groves along the Western Ghats from Maharashtra and Goa: Role of Beliefs and Folklores. In Glimpses of Indian Ethnobotany, Bombay: Oxford & IBH, 272-78. Vanitharani J, Bharathi K, Viji I et al. (2009) Ficus Diversity in Southern Western Ghats: A Boon for Biodiversity Conservation. Theoretical and Experimental Biology 6:85-94. Vishnudas CR (2007) Silent Services of Winged Beauties in Agriculture. LEISA: 13–14. Wenny DG et al. (2011) The Need to Quantify Ecosystem Services Provided by Birds. The Auk 128(1): 1–14. Additional Declarations No competing interests reported. Supplementary Files supplimentary1.xlsx supplimentary2.xlsx Cite Share Download PDF Status: Published Journal Publication published 21 Nov, 2023 Read the published version in Biodiversity and Conservation → Version 1 posted Editorial decision: Major revision 07 Oct, 2023 Reviewers agreed at journal 13 Sep, 2023 Reviewers agreed at journal 23 Aug, 2023 Reviews received at journal 13 Jul, 2023 Reviewers agreed at journal 07 Jul, 2023 Reviewers agreed at journal 30 Jun, 2023 Reviewers invited by journal 26 Jun, 2023 Editor assigned by journal 13 Jun, 2023 Submission checks completed at journal 10 Jun, 2023 First submitted to journal 10 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3046439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":208595305,"identity":"363b5f59-41bb-4f95-b64f-623a32b85581","order_by":0,"name":"Merlin Lopus","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYHACxgMJDDY8bAwMzAw8ID4zEXqAWtJ42NhI0sLAcJiBAa6FENBt731w4OGO8zJ88r2HDd4w2MkzsPMewKvF7MxxgwOJZ24DHcaXnDiHIdmwgZkvAb+WG2kMBxLbQFp4jA/zMDAnMDDzGODXcv8ZSMs5mJZ6IrTcYANpOQDWkszDcJgILWfADksGaslLNpxjcNywjaCW48cYH/5ss7OXbz57WOJNRbU8P/8Z/FqQAChSDEDxQzwgKh5HwSgYBaNgJAIAqf05QDAQu/8AAAAASUVORK5CYII=","orcid":"","institution":"Indian Institute of Technology Palakkad","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Merlin","middleName":"","lastName":"Lopus","suffix":""},{"id":208595306,"identity":"e384b442-34dc-419a-b77a-4003df47f15d","order_by":1,"name":"Deepak Jaiswal","email":"","orcid":"","institution":"Indian Institute of Technology Palakkad","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Jaiswal","suffix":""},{"id":208595307,"identity":"c6fcc4d4-53da-4e26-bcae-7a58de155f73","order_by":2,"name":"V Shakeela","email":"","orcid":"","institution":"M S Swaminathan Research Foundation","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"V","middleName":"","lastName":"Shakeela","suffix":""},{"id":208595308,"identity":"92bd4993-735e-48a6-bd3e-dacef00d194e","order_by":3,"name":"D Reshma","email":"","orcid":"","institution":"M S Swaminathan Research Foundation","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"D","middleName":"","lastName":"Reshma","suffix":""},{"id":208595309,"identity":"9056c0f9-740e-4ff6-a6d7-a2a67bea76f7","order_by":4,"name":"Subaiba Shafi","email":"","orcid":"","institution":"M S Swaminathan Research Foundation","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Subaiba","middleName":"","lastName":"Shafi","suffix":""},{"id":208595310,"identity":"55e2cb29-c148-463e-8115-8b8ac139fd72","order_by":5,"name":"Abdulla Habeeb","email":"","orcid":"","institution":"M S Swaminathan Research Foundation","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abdulla","middleName":"","lastName":"Habeeb","suffix":""},{"id":208595311,"identity":"df5a145d-8ec4-4788-856a-b941db7d60d0","order_by":6,"name":"Amit Kushwaha","email":"","orcid":"","institution":"Indian Institute of Technology Palakkad","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Amit","middleName":"","lastName":"Kushwaha","suffix":""}],"badges":[],"createdAt":"2023-06-10 10:44:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3046439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3046439/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10531-023-02738-0","type":"published","date":"2023-11-21T15:01:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":38480323,"identity":"e7d43770-64be-4eb5-9ae7-82f2c7fd57d9","added_by":"auto","created_at":"2023-06-13 15:20:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":773256,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical locations of the study sites are marked in the map of Wayanad district and one of the sites is projected.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3046439/v1/dc59bce47fead72beacb6081.png"},{"id":38480322,"identity":"923d3d4a-9d7d-4e64-8a0b-2115c0087c74","added_by":"auto","created_at":"2023-06-13 15:20:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":764122,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies accumulation curves for the diversity indices using the sample-based rarefaction and extrapolation method\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3046439/v1/598c6572853b71814ef3dddb.png"},{"id":38479474,"identity":"806cf4fe-1690-44ff-ae37-61261d39bee1","added_by":"auto","created_at":"2023-06-13 15:12:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":229464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eRenyi diversity profile \u003cstrong\u003e(b) \u003c/strong\u003eRanking of species concerning its abundance\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3046439/v1/87cfd90bf46cf49f5a94b1bc.png"},{"id":38479476,"identity":"208eb444-22b9-47d7-b0f9-57e6c86289db","added_by":"auto","created_at":"2023-06-13 15:12:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":298809,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis of diversity indices.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3046439/v1/1918bf3aeff0fa04c7e7fed0.png"},{"id":38480321,"identity":"2e9c174c-dd8e-4377-98a2-f1b55d9ba755","added_by":"auto","created_at":"2023-06-13 15:20:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":163516,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of Shannon index of the bird with \u003cstrong\u003e(a)\u003c/strong\u003e Shannon index of Native Tree (NT), \u003cstrong\u003e(b)\u003c/strong\u003e NDVI-May, and \u003cstrong\u003e(c) \u003c/strong\u003eNDVI-Oct. \u003cstrong\u003e(d)\u003c/strong\u003e comparison of Shannon indices of birds, native trees, and total trees.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3046439/v1/0b34212907942f013b372809.png"},{"id":38479482,"identity":"f920b030-aa19-4064-b499-4aa0f02512a2","added_by":"auto","created_at":"2023-06-13 15:12:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":370969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eSmoothed NDVI values, \u003cstrong\u003e(b)\u003c/strong\u003e NDVI values at different sites, \u003cstrong\u003e(c) \u003c/strong\u003ecomparison of average NDVI with Species Richness (SR) of birds, and \u003cstrong\u003e(d) \u003c/strong\u003ecomparison of selected four NDVI values.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3046439/v1/f75c3ee8d817539522f02774.png"},{"id":38479481,"identity":"a2775a17-247c-4430-99b8-52be20f28d31","added_by":"auto","created_at":"2023-06-13 15:12:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1725847,"visible":true,"origin":"","legend":"\u003cp\u003eSighted birds at the study sites. \u003cstrong\u003e(a) \u003c/strong\u003eLittle egret, \u003cstrong\u003e(b)\u003c/strong\u003e Orange minivet female, \u003cstrong\u003e(c)\u003c/strong\u003e Orange minivet male, \u003cstrong\u003e(d)\u003c/strong\u003e Black- napped monarch flycatcher, \u003cstrong\u003e(e)\u003c/strong\u003e Bronzed drongo, \u003cstrong\u003e(f) \u003c/strong\u003eCommon kingfisher, \u003cstrong\u003e(g)\u003c/strong\u003e Oriental magpie-robin, \u003cstrong\u003e(h)\u003c/strong\u003e Brown shrike.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3046439/v1/8f855e8598eb5492bdc868ca.png"},{"id":47146317,"identity":"c965c5e4-39db-4738-975a-2fefc6bb6f10","added_by":"auto","created_at":"2023-11-27 15:07:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4298648,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3046439/v1/4277115d-125e-4f2d-9c82-1470288cdf99.pdf"},{"id":38479480,"identity":"97297e8d-f713-49b3-ad16-c388bf68e969","added_by":"auto","created_at":"2023-06-13 15:12:03","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22727,"visible":true,"origin":"","legend":"","description":"","filename":"supplimentary1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3046439/v1/1dfcee7414bf47ed940f86e4.xlsx"},{"id":38479478,"identity":"8c0d517d-ec3d-4d2b-b4bf-1e75f51f0df1","added_by":"auto","created_at":"2023-06-13 15:12:03","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24075,"visible":true,"origin":"","legend":"","description":"","filename":"supplimentary2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3046439/v1/75bf3bbe6cfca65658b982c8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Conservation of Native Tree Species in The Agroforest of Rice-Based Agroecosystems Will Contribute to The Sustainable Agriculture","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNature-based Solutions (NbS) have made tremendous progress in recent years, improving the environment's ecosystem functions (Miralles \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Traditional agriculture practice incorporates many NbS and is also identified as a climate-smart approach (Singh and Singh \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It relies on ecosystem services and mixed cropping patterns for sustainable food production and it can decrease the risk of crop failure, pests, and disease (Sauerborn, Sprich, and Mercer-Quarshie 2000). Traditionally managed rice-based agroecosystems are a typical example of a sustainable food production method as it involves diversified farmland (Lawler \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) with the specific feature of avoiding chemical inputs and maintaining an average yield yet stable for thousands of years (Roger and Kurihara \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The importance of biological control in rice pest management has also gained attention, particularly in the tropics (Ooi and Waage \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Traditional agriculture is also known for its aesthetic, natural, cultural, historical, and socio-economic values (Bar\u0026aacute;nkov\u0026aacute;, Dobrovodsk\u0026aacute;, and Štefunkov\u0026aacute; \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lieskovsk\u0026yacute; et al. 2015). The regions where traditional farming landscapes occur had the same farming practices for a long period (Fischer, Hartel, and Kuemmerle 2012). Some of the finest examples of these regions include the Western Ghats of India, the Satoyama landscapes in Japan, the Milpa cultivation systems in Mexico, traditional village systems in Eastern Europe, and South-western China\u0026rsquo;s terrace landscapes(Fischer, Hartel, and Kuemmerle 2012; Hartel et al. 2010; Liu, Duan, and Yu \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; McNeely and Schroth 2006; Ranganathan et al. 2008; Robson and Berkes 2011; Takeuchi \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNatural farming is another type of farming practice and is very similar to traditional farming methods as it is also relying on non-chemical inputs. In India, this farming practice utilizes eco-friendly bio inputs such as \u003cem\u003eJivamrit\u003c/em\u003e and \u003cem\u003eBeejamrit\u003c/em\u003e in the farmlands instead of direct usage of cow dung and other natural products (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://naturalfarming.niti.gov.in/\u003c/span\u003e\u003cspan address=\"https://naturalfarming.niti.gov.in/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Whereas, conventional or modern farming relies on chemical inputs and extensive use of modern types of machinery. The farming practice has got significant implications for wild species of flora and fauna. Moreover, mechanical operations associated with agricultural activity can have negative impacts on particular groups of species such as birds (McLaughlin and Mineau 1995). There is evidence that organic farming and shaded agroforestry have positive impacts on the abundance and species richness of bat diversity (Park 2015).\u003c/p\u003e \u003cp\u003eThe present study intended to analyze the biodiversity associated with three farming practices, viz., Traditional Farming (TF), Natural Farming (NF), and Modern Farming (MF). All the selected study sites were rice-based agroecosystems. In a rice-based agroecosystem, biodiversity refers to the variability that exists at the habitat, species, and genetic levels. \u003cem\u003eHabitat diversity\u003c/em\u003e refers to the patchwork of rice and non-rice habitats of the paddy landscape. The non-rice habitat includes the bunds, surrounding or adjacent grasslands swamps, or forests. \u003cem\u003eSpecies diversity\u003c/em\u003e refers to the richness and relative abundance of rice pests and their natural enemies, weeds, and microorganisms in the rice ecosystem. \u003cem\u003eGenetic diversity\u003c/em\u003e refers to the diversity of rice germplasm and commercial cultivars used by farmers (Borromeo 2001). For this study, we considered \u003cem\u003eHabitat diversity\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eBiodiversity richness in these agriculture systems was compared by considering two parameters viz., plants and birds as both are indicators of environmental integrity (Bibby \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Fagan and Peart \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Galewski and Devictor 2016; Godet, Jaffr\u0026eacute;, and Devictor 2011; Gregory et al. 2005, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; LaPaix, Freedman, and Patriquin 2009; McGraw and Furedi 2005; McKinney \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pyk\u0026auml;l\u0026auml; \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Birds can respond to environmental changes over many spatial scales (Temple and Wiens 1989) which makes them one of the good bioindicators. Moreover, they are comparatively diverse (~\u0026thinsp;10,000 species globally) and tend to be at, or near the top of the food chain which makes them sensitive to changes at lower trophic levels (Gregory et al. 2005).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThis study was conducted in the Wayanad district located in the North-Eastern region of Kerala, India. Wayanad district is the home of Kerala\u0026rsquo;s heritage rice farming system. This region resides on the crest of the Western Ghats, one of the 36 Biodiversity hotspots in the world and known for its rich abundance of flora and ethnic cultures. Agriculture is the backbone of the economy for this district. Many traditional rice varieties and traditional cultivating practices are still used in the district by the ethnic groups. \u003cem\u003eKurichiya\u003c/em\u003e is one among those ethnic groups and also conservers of the traditional rice farming system. They keep large land holdings, big herds of cattle, and they follow a family farming approach to ensure the cultural, culinary, and curative needs of the family and which is considered a sustainable agriculture system (Anil and Vedavalli 2016).\u003c/p\u003e \u003cp\u003eSwitching of farm practices from traditional to modern and rapid urban developmental activities is seen as a trend in the district. Moreover, as part of urbanization and other activities of land conversion, the changes in ecosystems are visible. About 40% of the total land area of Wayanad district is under a forest-protected zone (Government of Kerala \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). From 2001 to 2021, Wayanad lost 2.65kha of tree cover, equivalent to a 1.6% decrease in tree cover since 2000. From 2002 to 2021, Wayanad lost 66ha of humid primary forest, making up 2.5% of its total tree cover loss in the same period. The total area of humid primary forest in Wayanad decreased by 0.24% in this period (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.globalforestwatch.org/\u003c/span\u003e\u003cspan address=\"https://www.globalforestwatch.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Therefore, the study of biodiversity associated with agroecosystems such as rice fields is significant in this district as the maintenance of biological diversity is essential for ecologically sustainable agriculture (Pimentel et al. 1992).\u003c/p\u003e \u003cp\u003eA total of 9 rice-based agroecosystems were selected for this study. Out of the 9 sites, 3 sites were traditional farms maintained by \u003cem\u003eKurichiya\u003c/em\u003e tribal community (T1, T2, T3), 3 were natural farms (N1, N2, N3) and another 3 farms were modern (M1, M2, M3). The geographic location of the study sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and other details of the location is depicted in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLocation of study sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Sites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of farming\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage Rainfall (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage Temperature (℃)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraditional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003csup\u003e0\u003c/sup\u003e 38' 31.42\" N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003csup\u003e0\u003c/sup\u003e 19' 02.25\" E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2635.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraditional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003csup\u003e0\u003c/sup\u003e 41' 10.59\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003csup\u003e0\u003c/sup\u003e 05' 25.82\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2402.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraditional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003csup\u003e0\u003c/sup\u003e 51' 59.50\" N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003csup\u003e0\u003c/sup\u003e 02' 52.24\" E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2394.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003csup\u003e0\u003c/sup\u003e 41' 14.99\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003csup\u003e0\u003c/sup\u003e 58' 42.67\" E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2493.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003csup\u003e0\u003c/sup\u003e 42' 48.93\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003csup\u003e0\u003c/sup\u003e 08' 22.65\" E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2092.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003csup\u003e0\u003c/sup\u003e 35' 55.52\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003csup\u003e0\u003c/sup\u003e 17' 38.76\" E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2157.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003csup\u003e0\u003c/sup\u003e 47' 15.98\" N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003csup\u003e0\u003c/sup\u003e 54' 23.35\" E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2637.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003csup\u003e0\u003c/sup\u003e 43' 25.10\" N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003csup\u003e0\u003c/sup\u003e 05' 42.89\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2313.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003csup\u003e0\u003c/sup\u003e 35' 44.31\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003csup\u003e0\u003c/sup\u003e 18' 03.10\" E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1804.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003ePlant diversity\u003c/h2\u003e \u003cp\u003eThe plant diversity survey was conducted between March to May 2022. The data was collected from agroforests of 9 sites by establishing a Cartesian grid of 100 \u0026times; 100 m (10,000 m\u003csup\u003e2\u003c/sup\u003e). Further, each minor grid was marked in 25 \u0026times; 25 m and all corners were temporarily marked during the survey. All the trees and shrubs were identified up to the species level (Gamble \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1915\u003c/span\u003e). The native trees were identified using the database maintained by Kerala Forest Research Institute (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://keralaplants.in\u003c/span\u003e\u003cspan address=\"http://keralaplants.in\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Renyi profile was determined to understand the native tree diversity in the sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The identified plants with their common name, scientific name, family, nativity, flowering, and fruiting period were demonstrated in Supplementary Material I.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eBird diversity\u003c/h2\u003e \u003cp\u003eThe bird survey was conducted from February to May 2022, a period when migrant birds are still present and most residents were active. Two habitats in each site were considered for the bird survey viz., paddy field and agroforest. Two-point counts in each habitat were conducted and a transect walk was taken between the area of the paddy field and the agroforest. A total of 36-point counts and 9 transect walks were conducted for this study. Every survey was carried out during the early morning hours (06.30 am \u0026ndash; 10.30 am). Foggy and rainy days were avoided for the survey. Birds were detected visually or aurally by two detector teams and counted. Digital apps such as BirdNET(Kahl et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Merlin (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://merlin.allaboutbirds.org/\u003c/span\u003e\u003cspan address=\"https://merlin.allaboutbirds.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) also supported the bird survey. The primary dietary guild of the birds was identified from the website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://naturalfarming.niti.gov.in/\" target=\"_blank\"\u003ewww.birds.cornell.edu\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.birds.cornell.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The identified birds with their common name, scientific name, family, IUCN status, and movement were demonstrated in Supplementary Material II.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eAlpha Diversity\u003c/h2\u003e \u003cp\u003eAlpha-diversity was calculated using the iNEXT package (Chao et al. 2014) by enumerating Hill numbers of order q: species richness (q\u0026thinsp;=\u0026thinsp;0), Shannon diversity (q\u0026thinsp;=\u0026thinsp;1, the exponential of Shannon entropy), and Simpson diversity (q\u0026thinsp;=\u0026thinsp;2, the inverse of Simpson concentration). Of the three diversity indices, species richness gives equal weightage to common and rare species, Shannon diversity emphasizes the contribution of common species and Simpson diversity give weightage to the contributions of abundant species. Sample-size-based (or size-based) rarefaction and extrapolation (R/E) were adopted in this study as it computes diversity estimates for rarefied and extrapolated samples up to an appropriate size with 95% confidence intervals(Chao et al. 2014). We used rarefaction and extrapolation to avoid bias in estimates of species diversity in plots as the number of individuals may vary with the farming system. All the diversity indices are natural-log-transformed to meet model assumptions of normality. We also projected the species accumulation curves for the diversity indices using the sample-based rarefaction and extrapolation method (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To find the correlated variables to bird diversity we adopted the dimensionality reduction method using Principal Component Analysis (PCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eRemotely sensed data\u003c/h2\u003e \u003cp\u003eThe Normalized Difference Vegetation Index (NDVI) time series data was derived for each site from the MODIS sensor (MODerate Resolution Imaging Spectroradiometer) on board the Terra satellite(Didan \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The NDVI is obtained from the reflectance of the vegetation in red and infrared portions of the spectrum and which is also a normalized spectral index (Tucker \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). The data composition was done by one NDVI value for every 16 days from 2012 to 2022 at 250 m spatial resolution (MOD13Q1 product). NDVI is computed from the reflectance of the vegetation in red (R) and infrared (NIR) portions of the spectrum shown in equation (XYZ) (Parida, Kushwaha, and Ranjan \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tucker \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1979\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$NDVI=\\frac{NIR-R}{NIR+R}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003ePearson correlation was performed for understanding the correlation among the categorical variables using R statistical and programming environment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePlant diversity\u003c/h2\u003e \u003cp\u003eA total of 45 families, 104 genera, and 128 species were identified from the study sites. This study recorded 7325 individuals (Supplementary Material I). Dominant species in all the sites were \u003cem\u003eCoffea arabica\u003c/em\u003e and \u003cem\u003eAreca catechu\u003c/em\u003e. The dominant native tree species was \u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e in 5 sites viz., T2, N1, N3, M1, and M3. \u003cem\u003eTerminalia paniculate\u003c/em\u003e, \u003cem\u003eOlea dioica\u003c/em\u003e, \u003cem\u003eAporosa lindleyana\u003c/em\u003e, and \u003cem\u003eMangifera indica\u003c/em\u003e were the dominant native tree species at the sites T1, T3, N2, and M2 respectively. Out of 128 species identified 92 were native species and 36 were exotic. The species richness of native tree species and abundance was comparatively high in all the traditional farms (T1, T2, and T3).\u003c/p\u003e \u003cp\u003eThe Renyi diversity profile shows that four sites had more diversified native tree species. Site number 1\u0026ndash;3 represents traditional farms and site number 4 represents a natural farm (N1). The least diversified native tree sites were 8 and 9 which represent two modern farms. \u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e ranked first in native trees followed by \u003cem\u003eMangifera indica\u003c/em\u003e, \u003cem\u003eErythrina variegata\u003c/em\u003e, \u003cem\u003eOlea dioica\u003c/em\u003e, and \u003cem\u003eArtocarpus hirsutus\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBird diversity\u003c/h2\u003e \u003cp\u003eThis study recorded 101 bird species belonging to 48 families and 17 orders. A total of 2051 individuals were identified. Out of the 101 bird species, 16 were migrant species. Some of the spotted birds at the study sites are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Bird species richness and abundance were further categorized by the primary dietary guild. The percentage of the number of species belonging to each category at each site is as follows. Out of the all identified species, the majority of the species was belonging to the Insectivorous category. In TF and NF, it was 45%, and in MF, it was 40%. Similarly, TF had a comparatively large number of species belonging to the Omnivorous category (TF-16%, NF- 13%, and MF-15%). The percentage of the Frugivorous category was high in MF followed by TF (TF- 14%, NF-12%, and MF-16%). More number of the Carnivorous category was identified at NF (TF- 13%, NF- 17%, and MF- 14%). In the case of Granivorous species, which are known as pests in rice fields MF had more compared to other farming systems (TF- 7%, NF-8%, and MF-10%). All the farming systems had an equal percentage of species of Nectarivorous (4%) and Herbivorous (1%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePearson correlation test was performed further to understand the relationship between the dietary guild categories and the Shannon index of native trees. It is observed that the Insectivorous, Omnivorous, and Carnivorous categories had a positive correlation with the native trees. All three categories had a correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.42. Similarly, Granivorous species had a negative correlation with the native trees (-0.37).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAlpha Diversity\u003c/h2\u003e \u003cp\u003eDiversity estimates for the bird, native trees, and total trees were calculated using the iNEXT package. The estimated species richness ranged for birds from 35.5 to 120.18, and it was high in site T3 (120. 18) whereas, the highest Shannon and lowest Simpson indexes were found at sites M1 and N3 (41.26 and 4.46 respectively). Both the indexes ranged from 11.19 to 41.26, and 35.37 to 4.46 respectively. In the case of the native tree, the estimated species richness ranged from 14.23 to 55.06, the Shannon index ranged from 4.92 to 26.39, and the Simpson index ranged from 19.03 to 3.14. Moreover, species richness was high in site N1 (55.06), the Shannon index was high in T3 (26.39), and the Simpson index was least in M3 (3.14). Similarly, the estimated species richness for total trees ranged from 40.07 to 82.36, the Shannon index ranged from 5 to 17.53, and the Simpson index ranged from 17.7 to 2.1. The highest species richness and lowest Simpson index for total trees were observed in site N1, and the highest Shannon index was found in M1 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All the indices were natural-log-transformed for principal component analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated diversity indices based on rarefaction and extrapolation method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBirds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eTotal Trees\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eNative Trees\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies Richness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShannon Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimpson Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecies Richness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eShannon Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSimpson Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSpecies Richness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eShannon Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSimpson Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e42.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e22.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e16.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e19.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e49.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e26.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e55.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e21.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of bird species identified in each primary dietary guild\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsectivorous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOmnivorous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrugivorous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCarnivorous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGranivorous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNectarivorous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHerbivorous\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003cp\u003eIn principal component analysis, the first axis explained 43.4%, and the second axis 28.9% of all data variation. Three categories of variables were considered for PCA viz., diversity indices of the bird at 9 sites, diversity indices of native trees in the corresponding site, and mean NDVI for each month from 2012 to 2022 time series. The angle between the vectors of variables explains their correlation in this space. Small angles represent a high positive correlation, right angles (90\u003csup\u003e0\u003c/sup\u003e) represent a lack of correlation, and opposite angles represent a high negative correlation. As indices of Shannon and Species Richness (SR) for bird diversity fall under the same group, this group of variables was further considered for dimensionality reduction. It is observed that the indices for native tree diversity and mean NDVI for May, June, August, and October had a compositional relationship with the Shannon and SR indices of bird diversity.\u003c/p\u003e \u003cp\u003eAs part of dimensionality reduction Shannon index of bird diversity and the Shannon index of native tree diversity among other indices was considered for further analysis. Pearson correlation test was conducted to explore the relationship between the Shannon index of bird diversity and the Shannon index of native tree diversity. A significant correlation between them (r\u0026thinsp;=\u0026thinsp;0.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Moreover, a similar trend was followed by the Shannon index of birdy diversity and the Shannon index of Native Tree (NT) diversity when compared to the Shannon index of Total Tree (TT) diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e. d).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNDVI and birds\u003c/h2\u003e \u003cp\u003eIt was identified that species richness and mean NDVI values were following the same trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e. C). Pearson correlation test was conducted for the Shannon index of birds and mean NDVI of May, June, August, and October. Correlation coefficients for mean NDVIs in May, October, August, and June were found to be 0.63, 0.62, 0.5, and 0.32 respectively. The NDVI value was equally distributed in each study site irrespective of the farming practice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eKurichiya\u003c/b\u003e \u003cb\u003ecommunity and native tree conservation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThough, a minor community when compared with the population of other tribal communities in the Wayanad district, \u003cem\u003eKurichiya\u003c/em\u003e is one of the largest joint families ever reported in anthropological literature (Kumaran \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The \u003cem\u003eKurichiya\u003c/em\u003e joint family consists of a single-house complex that consists of more than 100 members holding a considerably large area of land (Ravi Varma \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and which includes one or two hills and a large area of wetland between them. The abundance of native tree species in their farmland is due to their conservation of sacred groves and their religious beliefs constrained them to damage the vegetation in the specified region (Vaman D and Madhav 1981). The result of the Renyi profile was in agreement with the conservation practice followed by \u003cem\u003eKurichiya\u003c/em\u003e tribes. Site N1 had a similarity in tree diversity with the traditional farms except for farming practices. This was also reflected in the Renyi profile.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRelativity of native trees, NDVI, and birds\u003c/h2\u003e \u003cp\u003eOne of the major ecosystem services provided by biodiversity is the controlling of herbivorous insect populations by insectivorous birds (Cagan H \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wenny et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The rice farming system in Wayanad is associated with 16 species of birds rendering invaluable services to rice crops by controlling 10 species of pests in rice (Vishnudas \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The percentage of insectivorous species was high in both TF and NF sites. Out of the three natural farms, N1, and N2 sites followed an integrated farming system by conserving a moderate number of native trees among the cash crops. Site N3 had fewer native trees compared to other sites.\u003c/p\u003e \u003cp\u003eInterestingly, the presence of some bird species was only identified in TFs and they were Asian fairy bluebird (\u003cem\u003eIrena puella\u003c/em\u003e), Brown-cheeked fulvetta (\u003cem\u003eAlcippe poioicephala\u003c/em\u003e), Large-billed leaf warbler (\u003cem\u003ePhylloscopus magnirostris\u003c/em\u003e), Brown-breasted flycatcher (\u003cem\u003eMuscicapa muttui\u003c/em\u003e), White-bellied blue flycatcher (\u003cem\u003eCyornis caerulatus\u003c/em\u003e), Blue-capped rock thrush (\u003cem\u003eMonticola cinclorhynchus\u003c/em\u003e) and Green bee-eater (\u003cem\u003eMerops orientalis\u003c/em\u003e). The abundance of the native tree species \u003cem\u003eSyzygium cumini\u003c/em\u003e can attract many insectivorous and frugivorous birds (Sinu, Shivanna, and Kuriakose 2012). This was evident in traditional farms. The Green bee-eater was spotted from T2 and T3 sites. The abundance of \u003cem\u003eFicus tsjahela\u003c/em\u003e in these sites could be the reason for spotting it (Vanitharani et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Moreover, this tree species was not present in other farming systems. The presence of such native trees could be the reason for identifying the above-mentioned birds only in traditional farms. The other native trees present only in traditional farms were \u003cem\u003eAlstonia scholaris\u003c/em\u003e, \u003cem\u003eTerminalia paniculata, Sterculia foetida, Ficus exasperata, Tabernaemontana heyneana, Schleichera oleosa, Terminalia elliptica, Vateria indica, Callicarpa tomentosa, Semicarpus anacardium, Vitex altissima, Macaranga indica, Bischofia javanica Blume, Elaeocarpus tuberculatus, Naringi crenulata, Trichilia connaroides, Arenga wightii Griffith, Ochlandra scriptoria, Canarium strictum, Diospyrous ovalifolia Wigh, and Gnetum ula.\u003c/em\u003e The increased diversity of bird species w.r.t. native trees are also reported in previous studies ((Dyson \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, \u003cem\u003eKurichiya\u003c/em\u003e tribes are very specific about eating pure and toxin-free food, therefore no external chemical inputs are applied in their rice farming system. This purity in cultivation would also influence the bird species richness (Anil Kumar et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is reported that there is a strong positive correlation between bird species richness and the maximum mean NDVI value (Boniface, Andrew, and Rolf 2000). The result of this study is also in agreement with this (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Among the selected data set, May had NDVI values between 0.6 to 0.7 and for October it was between 0.7 and 0.8. The other two months viz., June and August had NDVI values\u0026thinsp;\u0026lt;\u0026thinsp;0.6. Another study has reported that bird species richness was noticeably high at NDVI values between 0.3 and 0.4 (Lucas M. and Federico I. 2021). Whereas, in this study, the highest NDVI value (0.8) and highest bird species richness (120. 18) was observed in site T3. This indicates that the highest values of NDVI are associated with the highest values of bird species richness.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe result of this study indicates that bird diversity is positively correlated with native tree diversity and mean NDVI of May, October, and August (r\u0026thinsp;\u0026gt;\u0026thinsp;0.5). A significant correlation between the Shannon index of bird diversity and the Shannon index of native trees (r\u0026thinsp;=\u0026thinsp;0.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was identified. Moreover, the Insectivorous, Omnivorous, and Carnivorous categories had a positive correlation with the native trees. All three categories had a correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.42. Similarly, Granivorous species had a negative correlation with the native trees (r= -0.37). This indicates that agroforests with diversified native trees can attract many favorable bird species and can reduce the Granivorous birds which are considered a pest in rice fields. Conserving more native trees in the farmland could be one of the reasons for the sustainable agriculture system of the \u003cem\u003eKurichiya\u003c/em\u003e tribal community. Considerable variation in NDVI was not observed in the three farming systems. It is because of the similar tree density in all the sites, except for variation in the number of native tree species. Thus, the conservation of native tree species in the agroforest of rice-based agroecosystems will contribute to the sustainable agriculture system.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the conservationists, farmers, and researchers who gave us advice during the selection of study sites or helped with the surveys with field assistance, especially Ajayan (Biodiversity Management Committee), Salim Pichan (MSSRF), Deepak (KFRI), Telna Sebastian (MSSRF), and Remel (MSSRF).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eThe work was supported by the Department of Science and Technology (DST), Government of India for the funding under Women Scientist- B program (DST/WOS-B/AFE-3/2021). Author Merlin Lopus has received research support from DST.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Merlin Lopus, Reshma D, Subaiba Shafi, Abdulla Habeeb, and Amit Kushwaha. The first draft of the manuscript was written by Merlin Lopus and critical revisions were made by Deepak Jaiswal and Shakeela V. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e The authors have no conflicts of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnil Kumar N, Merlin Lopus, Telapurath Raveendran, Vipin Das (2022) Making Rice-Farming System More Climate Resilient and Nutrition Sensitive: Heritage of Kurichiya Tribe Community of Western Ghats. In Innovation in Small-Farm Agriculture, 1\u003csup\u003est\u003c/sup\u003e edn. Boca Raton: CRC Press, pp 287-98. http://dx.doi.org/10.1201/9781003164968-29.1\u003c/li\u003e\n \u003cli\u003eAnil Kumar, Vedavalli (2016) Conservation of Family Farming Heritage. In Family Farming- Meeting the Zero Hunger Challenge, New Delhi: FAO and MSSRF, pp 245\u0026ndash;63.https://www.fao.org/3/i5650e/i5650e.pdf.\u003c/li\u003e\n \u003cli\u003eBar\u0026aacute;nkov\u0026aacute; Z, Dobrovodsk\u0026aacute; M, \u0026Scaron;tefunkov\u0026aacute; D (2011) Participation of Local People on Identifying the Landscape Values and Future Development in Historical Agricultural Landscapes. Ekol\u0026oacute;gia (Bratislava) 30(2): 216\u0026ndash;28.\u003c/li\u003e\n \u003cli\u003eBibby, Colin J (1999) Making the Most of Birds as Environmental Indicators. Ostrich 70(1): 81\u0026ndash;88.\u003c/li\u003e\n \u003cli\u003eBoniface, Oindo K, Skidmore A, de By Rolf (2000) Interannual Variability of NDVI and Species Richness in Kenya. International Archives of Photogrammetry and Remote Sensing 33: 1402\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eBorromeo, Teresita H (2001) Biodiversity and the Quest for Sustainable Rice Production Systems. Philipp J Crop Sci 26 (1): 15\u0026ndash;20.\u003c/li\u003e\n \u003cli\u003eCagan H, Sekercioglu (2006) Increasing Awareness of Avian Ecological Function. Trends in Ecology \u0026amp; Evolution 21(8): 464\u0026ndash;71.\u003c/li\u003e\n \u003cli\u003eChao A, Nicholas J (2014) Rarefaction and Extrapolation with Hill Numbers: A Framework for Sampling and Estimation in Species Diversity Studies. Ecological Monographs 84 (1): 45\u0026ndash;67.\u003c/li\u003e\n \u003cli\u003eDidan K (2021) MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13Q1.061 (Accessed on January 18, 2023).\u003c/li\u003e\n \u003cli\u003eDyson K (2020) Conserving Native Trees Increases Native Bird Diversity and Community Composition on Commercial Office Developments. Journal of Urban Ecology 6(1): juaa033.\u003c/li\u003e\n \u003cli\u003eFagan ME, Peart DR (2004) Impact of the Invasive Shrub Glossy Buckthorn (Rhamnus Frangula L.) on Juvenile Recruitment by Canopy Trees. Forest Ecology and Management 194(1\u0026ndash;3): 95\u0026ndash;107.\u003c/li\u003e\n \u003cli\u003eFischer J, Tibor H, Tobias K (2012) Conservation Policy in Traditional Farming Landscapes: Conserving Traditional Farming Landscapes. Conservation Letters 5(3): 167\u0026ndash;75.\u003c/li\u003e\n \u003cli\u003eGalewski T, Vincent D (2016) When Common Birds Became Rare: Historical Records Shed Light on Long-Term Responses of Bird Communities to Global Change in the Largest Wetland of France ed. Andy J Green. PLOS ONE 11(11): e0165542.\u003c/li\u003e\n \u003cli\u003eGamble JS (1915) Flora of the Presidency of Madras. London: Adlard and Son Ltd.\u003c/li\u003e\n \u003cli\u003eGodet L, Mika\u0026euml;l J, Vincent D (2011) Waders in Winter: Long-Term Changes of Migratory Bird Assemblages Facing Climate Change. Biology Letters 7(5): 714\u0026ndash;17.\u003c/li\u003e\n \u003cli\u003eGovernment of Kerala (2016) District Survey Report of Minor Minerals- Wayanad District. Thiruvananthapuram: Department of Mining and Geology. https://dmg.kerala.gov.in/wp-content/uploads/2020/01/dsr_way.pdf.\u003c/li\u003e\n \u003cli\u003eGregory, Richard D (2005) Developing Indicators for European Birds. Philosophical Transactions of the Royal Society B: Biological Sciences 360(1454): 269\u0026ndash;88.\u003c/li\u003e\n \u003cli\u003eGregory, Richard D, Jana S, Petr V, Simon Butler (2019) An Analysis of Trends, Uncertainty and Species Selection Shows Contrasting Trends of Widespread Forest and Farmland Birds in Europe. Ecological Indicators 103: 676\u0026ndash;87.\u003c/li\u003e\n \u003cli\u003eHartel T (2010) Amphibian Distribution in a Traditionally Managed Rural Landscape of Eastern Europe: Probing the Effect of Landscape Composition. Biological Conservation 143(5): 1118\u0026ndash;24.\u003c/li\u003e\n \u003cli\u003eKahl S, Connor MW, Maximilian E, Holger K (2021) BirdNET: A Deep Learning Solution for Avian Diversity Monitoring. Ecological Informatics 61: 101236.\u003c/li\u003e\n \u003cli\u003eKumaran V (1996) Kurichyarude Jeevithavum Samskaaravum. Kottayam: Current Books.\u003c/li\u003e\n \u003cli\u003eLaPaix R, Bill F, David P (2009) Ground Vegetation as an Indicator of Ecological Integrity. Environmental Reviews 17: 249\u0026ndash;65.\u003c/li\u003e\n \u003cli\u003eLawler, Sharon P (2001) Rice Fields as Temporary Wetlands: A Review. Israel Journal of Zoology 47(4): 513\u0026ndash;28.\u003c/li\u003e\n \u003cli\u003eLieskovsk\u0026yacute; J (2015) The Abandonment of Traditional Agricultural Landscape in Slovakia \u0026ndash; Analysis of Extent and Driving Forces. Journal of Rural Studies 37: 75\u0026ndash;84.\u003c/li\u003e\n \u003cli\u003eLiu Y, Duan M, Yu Z (2012) Agricultural Landscapes and Biodiversity in China. \u003cem\u003eAgric Ecosyst Environ\u003c/em\u003e 166: 46-54.\u003c/li\u003e\n \u003cli\u003eLucas M, Leveau, Isla FI (2021) Predicting Bird Species Presence in Urban Areas with NDVI: An Assessment within and between Cities. Urban Forestry \u0026amp; Urban Greening 63. https://doi.org/10.1016/j.ufug.2021.127199.\u003c/li\u003e\n \u003cli\u003eMcGraw, James B, Mary AF (2005) Deer Browsing and Population Viability of a Forest Understory Plant. Science 307(5711): 920\u0026ndash;22.\u003c/li\u003e\n \u003cli\u003eMcKinney, Michael L (2006) Urbanization as a Major Cause of Biotic Homogenization. Biological Conservation 127(3): 247\u0026ndash;60.\u003c/li\u003e\n \u003cli\u003eMcLaughlin, Alison, Pierre M (1995) The Impact of Agricultural Practices on Biodiversity. Agriculture, Ecosystems \u0026amp; Environment 55(3): 201\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003eMcNeely, Jeffrey A, G\u0026ouml;tz S (2006) Agroforestry and Biodiversity Conservation \u0026ndash; Traditional Practices, Present Dynamics, and Lessons for the Future. Biodiversity and Conservation 15(2): 549\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003eMiralles, Wilhelm (2021) Nature-Based Solutions in Agriculture Sustainable Management And Conservation of Land, Water And Biodiversity. Virginia: Food and Agriculture Organization of the United Nations and The Nature Conservancy. https://doi.org/10.4060/cb3140en.\u003c/li\u003e\n \u003cli\u003eOoi, PAC, Waage JK (1994) Biological Control in Rice: Applications and Research Needs. In Rice Pest Science and Management. Selected Papers from the International Rice Research Conference, Manila: International Rice Research Institute. http://books.irri.org/9712200515_content.pdf.\u003c/li\u003e\n \u003cli\u003eParida BR, Kushwaha A, Ranjan AK (2022) Synergy of Sentinel-2A and Near-Proximal Sensor Data for Deriving Biochemical Parameters of Paddy at Different Growth Stages. Environ Dev Sustain 24: 1048\u0026ndash;68.\u003c/li\u003e\n \u003cli\u003ePark, Kirsty J (2015) Mitigating the Impacts of Agriculture on Biodiversity: Bats and Their Potential Role as Bioindicators. Mammalian Biology 80(3): 191\u0026ndash;204.\u003c/li\u003e\n \u003cli\u003ePimentel D (1992) Conserving Biological Diversity in Agricultural/Forestry Systems. BioScience 42(5): 354\u0026ndash;62.\u003c/li\u003e\n \u003cli\u003ePyk\u0026auml;l\u0026auml;, Juha (2004) Immediate Increase in Plant Species Richness after Clear‐cutting of Boreal Herb‐rich Forests. Applied Vegetation Science 7(1): 29\u0026ndash;34.\u003c/li\u003e\n \u003cli\u003eRanganathan J (2008) Sustaining Biodiversity in Ancient Tropical Countryside. Proceedings of the National Academy of Sciences 105(46): 17852\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003eRavi Varma KT (2004) Matriliny: Tribal Matriliny and Northern Systems. Thiruvananthapuram: State Institute of Languages.\u003c/li\u003e\n \u003cli\u003eRobson, James P, Fikret B (2011) Exploring Some of the Myths of Land Use Change: Can Rural to Urban Migration Drive Declines in Biodiversity? .Global Environmental Change 21(3): 844\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003eRoger PA, Kurihara Y (1991) The Floodwater Biology of Tropical Wetland Rice Fields. IBSRAM Monograph 2: 21 1-233.\u003c/li\u003e\n \u003cli\u003eSauerborn J, Sprich H, Mercer QH (2000) Crop Rotation to Improve Agricultural Production in Sub-Saharan Africa. Journal of Agronomy and Crop Science 184(1): 67\u0026ndash;72.\u003c/li\u003e\n \u003cli\u003eSingh R, Singh GS (2017) Traditional Agriculture: A Climate-Smart Approach for Sustainable Food Production. Energy, Ecology and Environment 2(5): 296\u0026ndash;316.\u003c/li\u003e\n \u003cli\u003eSinu P, Shivanna K, Giby K (2012) Frugivorous Bird Diversity and Their Post-Feeding Behavior in Fruiting Syzygium Cumini (Myrtaceae) in Fragmented Forests of Central Western Ghats, India. Current Science 103: 1146\u0026ndash;48.\u003c/li\u003e\n \u003cli\u003eTakeuchi K (2010) Rebuilding the Relationship between People and Nature: The Satoyama Initiative. Ecological Research 25(5): 891\u0026ndash;97.\u003c/li\u003e\n \u003cli\u003eTemple SA, John AW (1989) Bird Populations And environmental changes: 43(2).\u003c/li\u003e\n \u003cli\u003eTucker CJ (1979) Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment 8(2): 127\u0026ndash;50.\u003c/li\u003e\n \u003cli\u003eVaman D, Vartak, Gadgil M (1981) Studies on Sacred Groves along the Western Ghats from Maharashtra and Goa: Role of Beliefs and Folklores. In Glimpses of Indian Ethnobotany, Bombay: Oxford \u0026amp; IBH, 272-78.\u003c/li\u003e\n \u003cli\u003eVanitharani J, Bharathi K, Viji I et al. (2009) Ficus Diversity in Southern Western Ghats: A Boon for Biodiversity Conservation. Theoretical and Experimental Biology 6:85-94.\u003c/li\u003e\n \u003cli\u003eVishnudas CR (2007) Silent Services of Winged Beauties in Agriculture. LEISA: 13\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eWenny DG et al. (2011) The Need to Quantify Ecosystem Services Provided by Birds. The Auk 128(1): 1\u0026ndash;14.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Traditional agriculture, Wayanad, Kurichiya, Native trees, NDVI","lastPublishedDoi":"10.21203/rs.3.rs-3046439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3046439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional agriculture relies on ecosystem services for sustainable food production and is also identified as a climate-smart approach. The present study analyses the agroforests associated with the rice farming system of three different agricultural practices for biodiversity richness by comparing two parameters: plants and birds. Out of the 9 study sites, 3 sites were traditional farms maintained by \u003cem\u003eKurichiya\u003c/em\u003e tribal communities, 3 were natural farms, and the other 3 farms were modern. A total of 45 families, 104 genera, 128 species of plants, and 101 bird species belonged to 48 families, and 17 orders were identified from the study sites. The sample-size-based rarefaction and extrapolation (R/E) method was adopted to identify estimated biodiversity indices. Renyi profile was used to understand the native tree diversity profile of the selected sites. The result of this study indicates that bird diversity is positively correlated with native tree diversity and NDVI of May and October. Conserving more native trees in the farmland could be one of the reasons for the sustainable agriculture system of the \u003cem\u003eKurichiya\u003c/em\u003e tribal community as it attracts more bird species and contributes to the biological control of pests. Thus, the conservation of native tree species in the agroforest of rice-based agroecosystems will contribute to the sustainable agriculture system.\u003c/p\u003e","manuscriptTitle":"Conservation of Native Tree Species in The Agroforest of Rice-Based Agroecosystems Will Contribute to The Sustainable Agriculture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-13 15:11:58","doi":"10.21203/rs.3.rs-3046439/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-10-08T01:01:27+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"2462d28e-1223-4a24-82d5-7548296d6fb7","date":"2023-09-14T03:50:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b8b1fbb0-fdc3-45a4-af07-1626a5a83c7b","date":"2023-08-23T07:02:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-13T04:49:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"f1380d25-3573-4409-9ae9-38a305cb6505","date":"2023-07-07T07:08:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a9d1baf3-2805-4001-9451-2b6fd034042a","date":"2023-06-30T06:34:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-06-26T07:04:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-14T00:15:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-10T10:51:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2023-06-10T10:43:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"96420e61-182e-43cb-8b56-c1bea6812711","owner":[],"postedDate":"June 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-11-27T15:04:13+00:00","versionOfRecord":{"articleIdentity":"rs-3046439","link":"https://doi.org/10.1007/s10531-023-02738-0","journal":{"identity":"biodiversity-and-conservation","isVorOnly":false,"title":"Biodiversity and Conservation"},"publishedOn":"2023-11-21 15:01:27","publishedOnDateReadable":"November 21st, 2023"},"versionCreatedAt":"2023-06-13 15:11:58","video":"","vorDoi":"10.1007/s10531-023-02738-0","vorDoiUrl":"https://doi.org/10.1007/s10531-023-02738-0","workflowStages":[]},"version":"v1","identity":"rs-3046439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3046439","identity":"rs-3046439","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}