Bird assemblages in specialty coffee production landscapes in pre-montane humid subtropical forests

doi:10.21203/rs.3.rs-4825928/v1

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Bird assemblages in specialty coffee production landscapes in pre-montane humid subtropical forests

https://doi.org/10.21203/rs.3.rs-4825928/v1

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Coffee-related agricultural intensification affects bird species abundance, richness, and composition through habitat loss and degradation. Production of specialty coffee is expected to be more sustainable and environmentally friendly than conventional coffee. Nevertheless, not all specialty coffee is grown sustainably. To evaluate environmental sustainability, we evaluated the composition of bird assemblages in six specialty coffee-producing communities in Bolivia’s pre-montane subtropical humid forest region. To do this, we measured the diversity, richness, generalist and specialist species, and the effect of habitat on bird assemblages, comparing coffee plots and secondary forest plots as part of “Nature’s matrix”. We found significant differences in the abundance of generalist bird species. We did not find differences in the richness and diversity of specialist species. Plant strata, herbaceous leaf cover, and shrub leaf cover affected the assemblages of generalist species. Our results represent a first step toward understanding the intricate relationship between biodiversity and specialty coffee production, highlighting the importance of considering regional differences in landscape characteristics – conceived of as Nature’s matrix – when examining biodiversity in specialty coffee systems.

Bird assemblages

Nature’s matrix

specialty coffee

sustainability

Land use practices can have varying impacts on local plant and animal species (Gil-Tena et al. 2009; Newbold et al. 2013; Prokopová et al. 2019; Schürings et al. 2022). One possible impact is biotic homogenization, meaning a process of increasing species invasions and extinctions resulting in greater taxonomic, genetic, or functional similarity between two or more biotas over time (Olden and Rooney 2006). Agroforestry and its associated management practices have been identified as potential means of promoting biodiversity (Marconi and Armengot 2020). Of special relevance here is the theoretical framework proposed by Perfecto et al. (2019), in particular the concept of “Nature’s matrix” emphasizing that agriculture is part of a broader ecological matrix. This means that natural habitats, such as forests and rivers, interact with and impact agricultural areas. In this way, studying production systems using a Nature’s matrix-approach can improve our understanding of the contribution of biodiversity to sustainable agriculture (Chappell and LaValle 2011).

Determination of the diversity and abundance of bird species is often contingent upon the complexity and composition of assemblages (Chao and Jost 2015; Ramírez and Gutiérrez-Fonseca 2016). The term assemblage is used in community ecology to refer to a group of taxonomically related species that occur together in space and time (Weiss and Ray 2019). Numerous studies have been conducted in Brazil (Pirela de Faría and Prieto de Alizo 2006), Guatemala (Greenberg et al. 1997), Venezuela (Jones et al. 2002), Mexico (Alvarez-Alvarez et al. 2022), and Colombia (Jones et al. 2023) to investigate the association between bird assemblages and coffee-production systems. The results of these studies show that bird species richness tends to be higher in forested environments, diverse agroforestry systems, and those production systems located near forest remnants in comparison to monocultures or agroforestry systems situated near fragmented forests. In Bolivia, increased bird richness has been linked to the structural complexity of vegetation in cocoa agroforestry systems (Naoki et al. 2017). Further, it has been shown that birds can contribute to ecosystem services in coffee-production systems, for example as controllers of borer disease (Hypothenemus hampei) (Landivar Albis 2018). Nevertheless, the composition of bird assemblages associated with specialty coffee-production systems in Bolivia has not yet been evaluated, despite the market growth of specialty coffee in Bolivia and beyond (Torrez et al. 2023; Jacobi et al. 2024). Notably, customers of such expensive high-quality coffee typically expect it to be environmentally sustainable (Giovannucci and Koekoek 2007). Against this background, evaluation of bird communities in different coffee-producing areas can shed light on the relationship between biodiversity and the production of specialty coffees.

The present study aims to investigate the relationship between bird assemblages in what Perfecto et al. (2019) refer to as “Nature’s matrix”, highlighting that cropland is not isolated from its surrounding habitats. Indeed, the latter also belong to the wider agricultural landscape. The landscape configuration of the study region consists of coffee-production systems surrounded by secondary forests, thus providing suitable characteristics for evaluation of bird assemblages within the conceptual framework of Nature’s matrix. In the present study, we understand Nature’s matrix as a landscape that includes specialty coffee plots and secondary forests. Our working question is how bird assemblages vary between coffee plots and secondary forests in specialty coffee-production systems. We expect to find a lower diversity and richness of species in and around coffee plantations, as well as more generalist species and fewer specialists, due to the tendency of bird assemblages to homogenize in systems of lower plant complexity, such as coffee plantations, compared to secondary forests (Karp et al. 2012).

Study site

The study was carried out in the municipality of Caranavi, in the department of La Paz, Bolivia. The average size of coffee-producing farms here is 2.6 hectares (ha), often shaded by trees Inga edulis (Torrico et al. 2024) and surrounded by secondary forest. Sampling was carried out in June 2021. We evaluated 49 sites selected following the criteria of Toledo and Moguel (2012) and Perfecto et al. (2019), considering coffee plots as part of Nature’s matrix, whereby agriculture is viewed as an integral component of the biodiversity conservation agenda (Perfecto et al. 2019). In our study, Nature’s matrix comprises coffee plots and secondary forests. We chose this approach because coffee plots in Caranavi, Bolivia, are surrounded by continuous forest or slightly fragmented secondary forest, in contrast to the intensive coffee-production landscapes of Brazil, Colombia, and Peru (Perfecto et al. 2019), where secondary forests are generally absent around coffee plantations. Initially, our sampling sites encompassed three types of systems: monocultures, agroforestry systems, and secondary forests. However, the monocultures were close to the forests. Each monoculture plot had a smaller distance to forests ($\:\stackrel{-}{x}$: 108,3 a 194,5 m; $\:\sigma\::$ 101,6 a 158,7 m) than the standard to guarantee the independence of sampling in birds, which is 250 m radius. Therefore, it is likely that their ird diversity is similar to agroforestry systems. For this reason, monocultures and agroforestry systems were grouped together as coffee plots.

Our study site is located in Bolivia’s pre-montane humid subtropical forest region (Torrico-Albino et al. 2019). The average annual temperature here is 25.9 ºC, with mean annual precipitation of 1500–2600 mm and relative humidity varying between 70% and 80% (Stackhouse 2024). Humidity data were obtained from the Prediction of World Energy Resources (POWER) Project at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC), funded by the NASA Earth Sciences/Applied Sciences Program. Due to urbanization and expansion of the agricultural frontier, the local landscape is composed of different stages of secondary forests and crops (Navarro 2011). Coffee agroforestry systems are dominated by Inga spp., as shade trees, in combination with several species of Citrus spp., as well as other forest and fruit species such as Persea Americana and Musa paradisiaca L. (Torrico et al. 2024).

We installed 29 circular plots (hereafter, plots) in the coffee-producing farms (13 were monocrops and 16 were agroforestry systems) and 15 plots (5 m in radius) in secondary forests of similar size following Deppe and Rotenberry (2008). These plots were installed within five agricultural communities located between 700 and 1500 m a.s.l (Villa El Carmen, Montaña Verde association located in the San Lorenzo canton; the community of Villa Caturapi, the community of Quijarro; Villa Copacabana and Bolinda). The plots are shown in Fig. 1.

Data collection

The plots were established using the tallest tree in the designated area to configure the circular plot based on Celis-Murillo et al. (2012) and Suárez García (2017). Once the focal tree was selected, a 2 m radius was measured around it using a flexometer in the direction of the four cardinal points.

In each plot, we estimated the percentage of vegetation cover (shade percentage), considering plant strata (growth type) such as trees, shrubs, young trees, herbaceous vegetation, and mosses. We measured the percentage of herbaceous leaf cover, mosses leaf cover, shrubs and tree leaf cover, as well as plant species richness. Tree species identification was carried out in the field for the most common species during another study (Torrico et al. 2024). We identified tree, herbaceous, shrub, and moss samples in different ways, including photography and expert consultation. Around 50% of samples were classified as morphotypes because we could not identify the species. Topographic variables were also measured, such as percentage and direction of slope (with a clinometer and a compass) as well as the presence of water bodies (presence/absence) within a 250 m radius of the sampling plots. To ensure accuracy and consistency in habitat description, all of these criteria were applied according to the bird habitat description protocols of Ralph et al. (1993); Celis-Murillo et al. (2012); Suárez García (2017).

Bird richness and abundance were quantified using two methods: point count surveys (Ralph et al. 1993) and acoustic recordings (Celis-Murillo et al. 2012; Suárez García 2017), always carried out between 6:00 and 11:00 am (Greenberg et al. 1997) by the same observer (NA). Two types of microphones were used, a unidirectional microphone installed on a tripod in the direction of the largest source of song emission and a second (multidirectional) microphone, both positioned at the same height in all plots. There were no sound disturbances on the recordings, as one of the microphones was multidirectional and the unidirectional microphone was on a tripod. Both methods were applied at the same time by two persons. The first person was the first author and the second person a field assistant, who synchronized both unidirectional and multidirectional microphones immediately upon commencement of the point-count to enable acoustic recordings. Sampling at each plot lasted 10 minutes, to avoid pseudo-replication, as recommended by Celis-Murillo et al. (2012); Suárez García (2017).

Bird assemblage composition

We used rank-abundance curves to assess the dominance of species in both systems (secondary forests and coffee systems) and the relevant Nature’s matrix. The curves were created using R software version 4.2.2 (R Core Team 2024) and were used to visually represent the species composition of the different systems. Similarity in species composition between coffee plots and secondary forest plots was assessed using the proportional similarity (SP) index (Feinsinger 2004). The raw values of $\:{p}_{\text{i}}$ for each species in both systems were examined, and the lower of the two values of $\:{p}_{\text{i}}$ was reported $\:{p}_{\text{i}\text{C}\text{o}\text{f}\text{f}\text{e}\text{e}}\:\text{o}\text{r}\:\:{p}_{\text{i}\text{F}\text{o}\text{r}\text{e}\text{s}\text{t}}$. If a species was absent from one of the systems, the value given to this species was zero. Finally, all minimum values were summed, resulting in the species similarity index between the systems.

$$\:SP=\:\sum\:_{}^{S}\text{min}({p}_{\text{i}\text{C}\text{o}\text{f}\text{f}\text{e}\text{e}}{p}_{\text{i}\text{F}\text{o}\text{r}\text{e}\text{s}\text{t}})$$

Diversity, richness, and forest specialist and generalist species

Species diversity was assessed using the Shannon-Weaver index with the Jost correction (Shannon and Weaver 1949; Jost and González-Oreja 2012; Chao and Jost 2015). This index considers both species richness and evenness. If one group of species is twice as abundant as another group, the first group will have twice the diversity value as the second group (Jost and González-Oreja 2012). Additionally, we employed Pielou’s evenness index to measure the relationship between observed diversity and expected maximum diversity with R studio’s vegan package (R Core Team 2022). The value of this index ranges between zero and one, where one indicates that all species are equally abundant.

To evaluate the differences in species richness, total abundance of bird species, and abundance of generalist and specialists between the coffee plots and secondary forest plots, it was first necessary to rule out the possible system effect of these variables. System effects (for coffee and secondary forest plots) were evaluated using Generalized Linear Models (GLM). Residuals were adjusted to different distributions for each case (S6). For the analysis, the factor levels considered were coffee and secondary forest plots while the abundance of bird species in each plot was used as the response variable. Abundance was selected as the response variable due to correlations between species richness, abundance, and diversity. In all the cases, the model with the lowest Akaike Information Criteria corrected for small sample sizes (Delta AICc < 2) was selected as the best model (Anderson et al. 2008) and confirmed by means of a Likelihood Ratio test (LRT) comparing the best model against the null model. Analyses were carried out in R (version 4.2.2.; R Core Team 2022). We used the following packages: lme4 to build GLMs (Bates et al. 2014), MuMIn for model selection (Bartoń 2013), lmtest to verify model fit comparing best model vs. null model using the likelihood ratio test (LRT), and emeans for the post-hoc analysis, using pairwise Tukey tests (Okoye and Hosseini 2024). Due to a high level of overdispersion, the model was unable to be fitted. As a solution, we used system as a random effect to avoid overdispersion.

Generalist and specialist species in the assemblage

Our research analysed bird species in coffee plantations and secondary forests, focusing on their feeding groups to understand their habitat preferences according to generalist or forest specialist species. To do so, we followed the methodology of Aben et al. (2008), which requires information on the natural history of each species. To compile natural history information for each species, we consulted various free natural history sources, including Birds of the World (2024), eBird, Neotropical Ornithological Society (NOS), BirdLife, and Xeno-Canto. Following review of the natural history information, the species were classified as forest generalists or forest specialists according to their affinity with the habitat (Aben et al. 2008). Our study used generalized linear models and boxplots to evaluate whether there was a significant difference in the diversity, richness, or abundance of generalists and specialists between the two study systems. To graph this, we use box diagrams, where we include their comparison with the entire Nature's matrix.

Assemblage and habitat conditions

At each circular plot, we measured the elevation, waterbodies (presence/absence), direction of slope, percentage of slope, plant richness, plant strata (trees, shrubs, young trees, herbs, and mosses) of tree leaf cover, shrub leaf cover, herbaceous leaf cover, and mosses leaf cover, in line with the methodology of Ralph et al. (1993), Suárez-García et al. (2017), and Celis-Murillo et al. (2012). To evaluate the effects of habitat variables (independent variables) on species richness, as well as the abundance of generalists and specialists (response variables), we first explored the relation of each habitat variable and its interaction with each dependent variable. Then, we selected the independent variables and fitted different models. For the relationship of habitat variables, the numerical independent variables were scaled to standardize the data. As no differences were found for bird richness between systems, we put together the richness data from both systems (coffee and secondary forest plots). Next, we used a GLM to assess the relationship between bird richness and habitat variables, including a negative binomial distribution (Atkinson and Woods 2015). This was done to handle overdispersed count data. We considered system level as a random factor, because habitat variables were taken from different system levels (coffee plots and secondary forest) (Bolker 2015).

Bird assemblage composition

A total of 1,010 individuals of 124 bird species were recorded, eight of which were migratory while the rest were resident birds. We found 68 bird species in secondary forest plots and 94 species in the coffee plots (Fig. 2. a-c); 38 species were shared between coffee and secondary forest plots, of which 21 were specialists and 17 were generalists; 56 species were exclusive to the coffee plots, of which 24 were generalists and 32 were specialists; 30 species were exclusive to secondary forests, of which 15 were specialists and 15 were generalists.

The rank-abundance curves (Fig. 2. d) point to greater species richness in the coffee plots compared to the forest plots. Regarding the rank-abundance curves, each bird species is represented by a line on the graph that indicates its rank in terms of abundance and its relative abundance within the habitat. Judging by the differing width of the rank-abundance curves, species dominance between the coffee and secondary forest plots is not similar (Fig. 2. d). The curves also indicate that the five most-common species differ in their distribution across the coffee and secondary forest plots and the entirety of Nature’s matrix. The extension of the curve line for secondary forest plots suggests slightly more species stability compared to coffee plots, but greater stability is observed throughout Nature’s matrix. Steep slopes on the rank-abundance curves indicate unequal distribution of species abundance. A pronounced numerical dominance of Psarocolius decumanus (a generalist species) is indicated for coffee plots while a dominance of Myiodynastes maculatus (a specialist species) is seen for secondary forest plots (Fig. 2. d). For Nature’s matrix (sum of both systems), it can be seen how each system contributes different dominant species to the full assemblage. Psarocolius decumanus (a generalist species) displays the highest relative abundance and appears dominant in coffee plots; followed by Myiodynastes maculatus (a specialist species) that appears dominant in secondary forest plots (Fig. 2. d). The dominant generalist and specialist species can be seen in S1–S3.

Of the five most dominant species in coffee and secondary forest plots and throughout Nature’s matrix, the dominant frugivorous and omnivorous species observed were Psarocolius decumanus and Pionus menstruus and Ramphastos vitellinus, followed by a granivorous bird, Patagioenas plumbea, and an insectivore, Myiodynastes maculatus.

The results of analysis of the similarity index point to low species similarity between coffee and secondary forest plots. We found only 0.35 (35%) similarity of all species between the coffee and secondary forest plots, as well as 0.3 (30%) similarity of generalist species and 0.26 (26%) similarity of specialist species between both systems. These results suggest that the assemblages of coffee and secondary forest plots do not feature similar species composition.

Diversity, richness of assemblages

According to our analysis, coffee plots and secondary forest plots did not differ in terms of overall bird diversity (LRT: χ² = 0.048, d.f. = 1, P = 0.825, ΔAIC = 2.26; Fig. 3a-d, S4), abundance (LRT: χ² = 0.7497, d.f. = 1, P = 0.386, ΔAIC = 1.56; Fig. 3. a-d, S4), Pielou evenness (LRT: χ² = 0.17, d.f. = 1, P = 0.679, ΔAIC = 2.14; Fig. 3. a-d, S4), and richness (LRT: χ² = 0.002, d.f. = 1, P = 0.965, ΔAIC = 2.31; Fig. 3. a-d, S4). However, when we distinguish between generalist and specialist species, our analysis shows that secondary forest plots bore higher specialist species richness than coffee plots, whereas the assemblage of generalist species was richer in coffee plots (Fig. 3.)

Secondary forest plots featured lower variation in species richness and abundance for generalist species compared to coffee plots. However, secondary forest plots displayed significantly higher values for specialist species compared to coffee plots (Fig. 3 and S5 and Table 1).

Table 1

Best models’ parameter estimates for the variables that showed variation explained by the system level (see S5) for generalist and specialist species.
Group	Variable	Parameter	Estimate	SD	z value	p – value
Generalists	Richness	(Intercept: Coffee plot)	1.574	0.101	15.648	< 0.001
		Forest	-0.432	0.193	-2.422	< 0.05
	Abundance	(Intercept: Coffee plot)	1.9557	0.112	17.491	< 0.001
		Forest	-0.621	0.212	-2.933	< 0.01
Specialists	Richness	(Intercept: Coffee plot)	1.351	0.114	11.823	< 0.001
		Forest	0.372	0.182	2.047	< 0.05
	Diversity	(Intercept: Coffee plot)	1.116	0.107	10.44 (t-value)	< 0.001
		Forest	0.448	0.183	2.45 (t- value)	< 0.05

Assemblages and habitat conditions

Only herbaceous leaf cover displayed a significant negative effect on all bird species richness, representing the entire assemblage of birds in Nature’s matrix (LRT: χ² = 4.754, d.f. = 1, P < 0.05, ΔAIC = 2.45; Fig. 4., S7, and Table 2). We chose this variable because all the assemblage variables (richness, diversity, abundance) were correlated. We found that herbaceous leaf cover, leaf cover of shrubs, and plant strata explained the variation of abundance of generalists across Nature’s matrix (LRT: χ² = 66.05, d.f. = 1, P < 0.001, ΔAIC = 58.76; Fig. 4., S7 and Table 2). Shrub leaf cover displayed significant positive effects on the abundance of generalists. The number of herbaceous leaf cover and plant strata was negatively related to the abundance of generalists (Fig. 4., S7, and Table 2). None of the environmental variables explained variation in the abundance of specialists (LRT: χ² = 1.901, d.f. = 1, P = 167, ΔAIC = 0.52, S7 and Table 2).

Table 2

GLMM estimates averaged from the best models of abundance of generalists with delta AICc lower than two (see S8)
Group	Variable	Estimate	Std. Error	z value	Pr(>\|z\|)
Bird species richness	(Intercept)	2.111	0.125	16.940	< 0.001
	Herbaceous leaf cover	-0.205	0.100	2.044	< 0.05
	Waterbody	0.069	0.151	0.463	0.643
	Plant richness	0.013	0.049	0.257	0.797
	Plant strata	0.012	0.052	0.225	0.822
Abundance of generalists	(Intercept)	2.370	0.050	47.715	< 0.001
	Plant strata	-0.363	0.081	4.468	< 0.001
	Herbaceous leaf cover	0.165	0.078	2.114	< 0.05
	Shrub leaf cover	0.270	0.067	4.033	< 0.001
	Elevation	0.018	0.041	0.454	0.650
Abundance of specialists	(Intercept)	1.634	0.150	10.879	< 0.001
	Elevation	0.047	0.087	0.537	0.591
	Waterbody	0.025	0.101	0.252	0.801
	Herbaceous leaf cover	0.009	0.045	0.208	0.835
	Shrub leaf cover	0.022	0.065	0.331	0.74

Our research found that small-scale, forest-adjacent coffee plantations managed by traditional producers can host a diversity of bird assemblages comparable to or even greater than that of surrounding natural forests, refuting the hypothesis that coffee plantations always have lower diversity and richness of birds (Karp et al. 2012). Indeed, our analysis found even more bird species in coffee plantations than in secondary forests (Faminow and Ariza 2001; Jones et al. 2002; Leyequien et al. 2006; Ocampo-Ariza et al. 2024). Overall, landscape characteristics such as the amount of forest cover and vegetation heterogeneity positively influence the diversity and composition of bird communities (Alexandrino et al. 2017; Perfecto et al. 2019; Marconi and Armengot 2020). Specifically, we identified greater numbers of generalist and specialist species in the assemblages of coffee plantations compared to those of forests alone. This likely results from the special configuration of Nature’s matrix, featuring diverse habitats benefitting both bird groups, to which the small-scale coffee plantations contribute (Alexandrino et al. 2017; Ocampo-Ariza et al. 2024; Martínez-Penados et al. 2024).

Variation of bird assemblages between coffee plots and secondary forests in specialty coffee production systems

Our research refutes the hypothesis that coffee-plot bird assemblages necessarily feature less diversity and richness than secondary forest plots (Karp et al. 2012). No significant difference was found in the diversity, richness, or abundance of bird species between the two systems. Indeed, more bird species were quantified in the coffee-plot assemblages compared to those of the secondary forest plots. According to Faminow and Ariza (2001); Jones et al. (2002); Leyequien et al. (2006); Perfecto et al. (2019); Ocampo-Ariza et al. (2024), coffee systems such as those we researched – i.e. configured on a small-scale by traditional producers and surrounded by forest – display bird diversity comparable to or greater than natural forests.

In addition, according to Perfecto et al. (2019), landscapes like those in the present study feature greater biodiversity than landscapes dominated by large coffee plantations. We identified 94 bird species in our coffee plots and 68 in secondary forests. In a separate study, Ong’ondo et al. (2022) recorded 127 bird species in shade coffee plantations and 79 in forests. These findings support the hypothesis that coffee small-scale plantations can host more bird species than forests alone, even if they are monocultures or agroforestry systems like those in our study, in particular when they are configured near to forests.

Further, our analysis revealed a similarity index of 35% when comparing the species composition of coffee-plot and forest-plot bird assemblages, not unlike the findings (similarity index 42%) of Ong’ondo et al. (2022). This low similarity value indicates that coffee plantations cannot substitute for natural forests, but can rather serve as complementary sites on behalf bird conservation. In particular, our results confirm this in terms of dominant and exclusive species found in each system.

Specifically, we found that Myiodynastes maculatus and Ramphastos vitellinus (specialist birds) were dominant in secondary forest plots, whereas Psarocolius decumanus and Pionus menstruus (generalist birds) were dominant in coffee plots. We found 30 species exclusive to secondary forest plots and 56 exclusive to coffee plots. These findings are also in line with those of Ong'ondo et al. (2022), who identified 18 bird species exclusive to forests and 66 exclusive to coffee systems. According to de Souza Leite et al. (2022), landscape characteristics explain variations in the abundance and presence/absence of birds identified in such environments. According to Harvey and González Villalobos (2007), differences in habitat and vegetation generate filters that favour certain birds, giving rise to dissimilar communities between natural habitats and agroecosystems such as those observed in our study. The Nature’s matrix created by the complementary plots in our study enables maintenance of exclusive assemblages of species according to morphological attributes (Ong’ondo et al. 2022).

In terms of generalist and specialist species, our results refute the hypothesis that forest assemblages feature more specialist species while coffee plantations feature more generalist species. Indeed, we found higher numbers of both groups in coffee plots. According to Alexandrino et al. (2017); Ocampo-Ariza et al. (2024), landscape-scale forest cover positively influences bird communities, especially forest and fruit-eating bird species like those in our study. The small-scale coffee plantations we studied were surrounded by forest cover, regardless of whether they were monocultures or agroforestry systems. According to Velásquez-Trujillo et al. (2021), a configuration like gives rise to greater species diversity than secondary forests.

In terms of generalist species, we found significant differences in richness and abundance between the systems analysed. Specifically, we identified greater richness and abundance of generalists in the coffee plots compared to the secondary forest plots. However, no significant difference was found in terms of the diversity of generalists. These findings may be attributed to the landscape configuration. According to Muhamad et al. (2013) and Alexandrino et al. (2017), nearby forests and scattered trees in agricultural landscapes can act as corridors and “islands” that facilitate the movement and presence of bird species, including generalists and insectivores. In the agroforestry systems we studied, Torrico et al. (2024) found more than 85 different plant species as shade trees. This suggests that the landscape configuration of our systems could explain the differences observed with regard to generalist bird species.

In addition, we found significant differences regarding the richness and diversity of specialist bird species between coffee plots and secondary forest plots. In particular, we found greater richness and diversity of specialist bird species in the secondary forest plots. However, contrary to our hypothesis, we observed a higher number of pecialists in coffee plots compared to secondary forest plots. These patterns of diversity and richness of specialists could relate to the amount of forest cover present at the landscape scale, which was the same in the two systems under study (Alexandrino et al. 2017). In our Nature’s matrix and that found elsewhere, forest cover positively influences the diversity and composition of bird communities (Robertson et al. 2013). Further, the heterogeneity of the landscape in contexts such as ours provides good conditions for specialist species, in line with Alexandrino et al. (2017) and Tavares et al. (2019).

According to our results, the configuration of the wider landscape – particularly the amount of forest covers present and the heterogeneity of the vegetation structure belonging to Nature’s matrix – may modulate the effects of plot-level homogenization on the diversity and composition of bird communities. This is consistent with patterns identified by Alexandrino et al. (2017), Velásquez-Trujillo et al. (2021), Ocampo-Ariza et al. (2024) with regard to agricultural landscapes. According to Ocampo-Ariza et al. (2024), it is necessary to consider these dynamics at multiple scales in order to develop effective conservation strategies in the context of specialty coffee production systems.

Effect of habitat on assemblages

The richness of all bird species was significantly affected by the percentage of herbaceous leaf cover: the greater the herbaceous leaf cover, the lower the bird species richness. No significant effects were found with respect to percentages of shrub leaf cover, plant richness, plant strata, elevation, or waterbodies. This could be because the bird species in our study were common to a particular stratum, similar to the species in the study by Jones et al. (2002).

Our results support this because plant strata and the percentage of herbaceous leaf cover have a negative effect on the abundance of generalists. By contrast, the percentage of shrub leaf cover has a positive effect on the abundance of generalists. Buechley et al. (2015) indicate that different types of shade management in coffee plantations can generate more diverse vegetation cover, including trees, grasses, and shrubs – that benefit bird species. Overall, according to Jones et al. (2002), shrubs are more common in forests and understory birds use coffee plantations as a conduit to travel between shrubs in search of food. Therefore, the bird species in our study, especially the generalists, are likely understory birds for whom shrub cover is particularly relevant.

In line with Bhagwat et al. (2008), this indicates that the amount of forest cover present at the landscape scale is key to the diversity and composition of bird communities. Shrub cover in forests and coffee plantations benefits understory birds, as observed by Jones et al. (2002) and Buechley et al. (2015). These bird species can use it as runners in search of food (Jones et al. 2002; Alexandrino et al. 2017).

The variables of elevation, plant richness, and waterbodies had no significant effect on the abundance of generalists. While these elements belong to Nature’s matrix Perfecto et al. (2019), they do not appear to make a direct contribution to bird diversity in the context of specialty coffee landscapes. According to de Souza Leite et al. (2022), landscape-scale factors are more important than plot- or patch-level factors in determining bird diversity patterns.

No habitat variables affected specialist abundance in the present study. This indicates that our Nature’s matrix was high quality and featured less contrast between habitats, precluding edge effects like predation and parasitism that particularly impact specialists (Leyequien et al. 2006). According to de Souza Leite et al. (2022), natural corridors that enable birds to access forests are particularly important, as habitat specialist species depend on forest environments for their reproduction and survival.

Overall, our findings indicate that the landscape configuration of Nature’s matrix – including forest cover, habitat heterogeneity, and connecting elements – plays a key role in modulating habitat effects on biodiversity (Olson and Dinerstein 1998; Olden and Rooney 2006; García and Martínez 2012). In this way, maintaining a variety of habitats within Nature’s matrix – including coffee plantations featuring diverse cover (Buechley et al. 2015) and surrounded by forests (de Souza Leite et al. 2022) – can benefit both generalist and specialist bird species (Martínez-Penados et al. 2024) see Fig. 5.

In this research, no variation was found in bird communities between coffee plots and secondary forests in specialty coffee production systems.. Our study found that small-scale coffee plantations (monocultures and agroforestry systems) can host levels of bird diversity comparable to or even greater than that of natural forests, refuting the hypothesis that coffee plantations necessarily have lower bird diversity and richness. This appears related to special characteristics of the landscape. Contrary to our predictions, a higher number of generalist and specialist species were found in coffee plantations compared to forests. This may be attributed to the configuration of Nature’s matrix, in particular when it encompasses diverse habitats beneficial to both specialist and generalist bird species.

We conclude that specialty coffee plots can provide habitats that complement those of neighbouring forests in conserving bird species. As evidenced by our study, dispersed coffee plots surrounded by forest cover can host species compositions similar to those found in secondary forests.

Funding

This research received funding from the project “Exploring the interlinkages between specialty coffee farms biodiversity and farmer’s practices in Bolivia” Seed Money Grant 1912, with the financial support of the Leading House for the Latin American Region, Latin American Swiss Center (CLS-HSG), University of St. Gallen, and the State Secretariat for Education, Research and Innovation (SERI), Switzerland.

Author Contribution

All authors contributed to the main text of the manuscript, but NA had the greatest involvement, NA prepared Figures 1 to 5. All authors reviewed the manuscript. GT contributed with forums for the study area. VT contributed to the map of the study area.

Acknowledgement

We thank the coffee farmers of Caranavi for allowing us access to their farms. Special thanks also go to D. Mendez for lending us the recording equipment, to D. Camacho for helping us with some bird identifications, and to our field guides: S.R. López-Gutiérrez, S. Pastén, and S.V. Condori-Coarite. The research was funded by a seed grant from the Swiss Secretariat for Education, Research, and Innovation (SERI).

Data Availability

Los datos de diversidad riqueza y abundancia respaldan los hallazgos de este estudio junto con los datos de abundancia de especies generalistas y especialistas. Los datos se proporcionan dentro del manuscrito y en los archivos de información complementaria.

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Bird assemblages in specialty coffee production landscapes in pre-montane humid subtropical forests

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study site

Data collection

Bird assemblage composition

Diversity, richness, and forest specialist and generalist species

Generalist and specialist species in the assemblage

Assemblage and habitat conditions

Results

Bird assemblage composition

Diversity, richness of assemblages

Assemblages and habitat conditions

Discussion

Variation of bird assemblages between coffee plots and secondary forests in specialty coffee production systems

Effect of habitat on assemblages

Conclusion

Declarations

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1