Keywords
avian community, bioacoustics, coffee agroecosystem, full annual cycle, habitat quality, LiDAR
Correspondence
Marius Somveille, Department of Genetics, Evolution and Environment, Centre for Biodiversity and Environment Research, University College London, London WC1E 6BT, UK. Tel: +44 (0) 1603 456161. E-mail: marius@somveille.com
Funding Information
This work was made possible by support from the Wolfson Foundation and UCL Grand Challenges to M.S.
Editor: Dr. Vincent Lecours
Associate Editor: Dr. Emma Higgins
Received: 30 December 2024; Revised: 16 May 2025; Accepted: 21 May 2025
doi: 10.1002/rse2.70015
Remote Sensing in Ecology and Conservation 2025;11 (6):686–700

RESEARCH ARTICLE
Consistent and scalable monitoring of birds and habitats along a coffee production intensity gradient
Marius Somveille1	, Joe Grainger-Hull1, Nicole Ferguson1, Sarab S. Sethi2,
Fernando Gonza´ lez-Garc´ıa3, Valentine Chassagnon4, Cansu Oktem1, Mathias Disney5,6	, Gustavo Lo´ pez Bautista7, John Vandermeer8 & Ivette Perfecto9
1Department of Genetics, Evolution and Environment, Centre for Biodiversity and Environment Research, University College London, London
WC1E 6BT, UK
2Department of Life Sciences, Imperial College London, London W12 0BZ, UK
3Red Biolog´ıa y Conservacio´ n de Vertebrados, Instituto de Ecolog´ıa, A.C. (INECOL), Carretera Antigua a Coatepec 351, El Haya, Xalapa, 91070,
Veracruz, Mexico
4AgroParisTech, Palaiseau 91123, France
5Department of Geography, University College London, London WC1E 6BT, UK
6NERC National Centre for Earth Observation, University College London, Gower Street, London WC1E 6BT, UK
7Finca Irlanda, Tapachula, Chiapas, Mexico
8Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor Michigan, 48109, USA
9School for Environment and Sustainability, University of Michigan, Ann Arbor Michigan, 48109, USA
Abstract
Land use change associated with agricultural intensification is a leading driver of biodiversity loss in the tropics. To evaluate the habitat–biodiversity relationship in production systems of tropical agricultural commodities, birds are commonly used as indicators. However, a consistent and reliable methodological approach for monitoring tropical avian communities and habitat quality in a way that is scalable is largely lacking. In this study, we examined whether the automated analysis of audio data collected by passive acoustic monitoring, together with the analysis of remote sensing data, can be used to efficiently monitor avian biodiversity along the gradient of habitat degradation associated with the intensification of coffee production. Coffee is an important crop produced in tropical forested regions, whose production is expanding and intensifying, and coffee production systems form a gradient of ecological complexity ranging from forest-like shaded polyculture to dense sun-exposed monoculture. We used LiDAR technology to survey the habitat, together with autonomous recording units and a vocalization classifier to assess bird community composition in a coffee landscape comprising a shade-grown coffee farm, a sun coffee farm and a forest remnant, located in southern Mexico. We found that LiDAR can capture relevant variation in vegetation across the habitat gradient in coffee systems, specifically matching the generally observed pattern that the intensification of coffee production is associated with a decrease in vegetation density and complexity. We also found that bioacoustics can capture known functional signatures of avian communities across this habitat degradation gradient. Thus, we show that these technologies can be used in a robust way to monitor how biodiversity responds to land use intensification in the tropics. A major advantage of this approach is that it has the potential to be deployed cost-effectively at large scales to help design and certify biodiversity-friendly productive landscapes.
ª 2025 The Author(s). Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Introduction
Land use change is a leading driver of biodiversity loss worldwide (IPBES, 2019), particularly in the tropics (Etard et al., 2022). Tropical forests, which are the most biodiverse terrestrial ecosystems on Earth, are threatened by an increasing demand for agricultural commodities (Laurance et al., 2014; Lewis et al., 2015), leading to an urgent need for policies that sustain both agricultural production and biodiversity in tropical regions. To stem the decline in tropical biodiversity, stakeholders engaged in supply chains must commit to sustainable agricultural practices (Curtis et al., 2018; Rueda et al., 2017), which rely on being able to quantify and monitor the response of biodiversity to habitat degradation.
An important crop produced in tropical forested regions is coffee. It is one of the world’s most traded commodities, being grown primarily in highly biodiverse tropical mountains. Traditionally, coffee is grown under diverse and dense shade canopies, and it can have a vegetation structure and a level of biodiversity that is comparable to native forests (Alvarez- Alvarez et al., 2020; Perfecto et al., 1996). Therefore, traditional shade-grown coffee offers both a refuge for biodiversity in areas that have been hard-hit by deforestation (Perfecto et al., 1996) and a high-quality matrix connecting patches of protected forest (Estrada-Carmona et al., 2022; Perfecto & Van- dermeer, 2010). However, a variety of socio-economic factors is leading to the expansion and intensification of coffee production across the world (Jha et al., 2014). Some of the main ways in which coffee production systems are intensified to increase yield include increasing sun exposure by a reduction in tree cover and applying chemical pesticides (Perfecto et al., 1996). Thus, coffee production systems now form a continuum ranging from forest-like shaded polyculture to dense sun-exposed monoculture, with an associated gradient of ecological complexity that provides a model system for investigating the response of biodiversity to habitat degradation (Moguel & Toledo, 1999; Perfecto et al., 2014).
To evaluate the habitat–biodiversity relationship in coffee and other tropical agricultural commodities, birds are commonly used as indicators as they are the best-known animal taxon globally, they occupy a wide range of ecological niches, and they are variably susceptible to disturbance (Sekercioglu et al., 2019). Thus, consistently and reliably monitoring tropical degraded forest habitat and their avian communities in a way that can be scalable is critical, but it remains challenging using traditional survey methods. Field surveys in coffee farms have been conducted using point counts and mist-netting methods to evaluate avian diversity and using visual methods to estimate foliage structure (Greenberg et al., 1997; Ong’ondo
Consistent and Scalable Monitoring of Birds and Habitats et al., 2022; Sekercioglu et al., 2019; Tejeda-Cruz & Sutherland, 2004). These approaches, however, are labour intensive and with limited reproducibility, which affects their scalability for widespread use.
In this study, we examined whether the automated analysis of audio data collected by passive acoustic monitoring (PAM), together with remote sensing, can help monitor avian biodiversity along the gradient of habitat degradation associated with the intensification of coffee production. PAM is increasingly being used to monitor vertebrate diversity, particularly birds (Hoefer et al., 2023), including in relation to specific agricultural management practices in coffee farms (Molina-Mora et al., 2024). PAM is also increasingly being combined with remotely sensed environmental data, notably in temperate forest ecosystems (e.g. Brunk et al., 2023; Wood et al., 2024). Here, we used autonomous recording units (ARUs) and a vocalization classifier to assess bird community composition, together with light detection and ranging (LiDAR) technology to quantify habitat quality, in a coffee landscape comprising a shade-grown coffee farm, a sun coffee farm and a forest remnant. Automated acoustic data processing was used here to monitor avian community composition continuously throughout a full annual cycle, which enabled the examination of community change between seasons with different climate (e.g. dry versus rainy). This full annual cycle perspective is crucial for understanding the factors that are structuring bird communities (Herrera, 1978) and how they respond to habitat change.
To evaluate how well bird diversity and habitat can be monitored in a consistent and scalable way across coffee landscapes, we investigated whether bioacoustics and LiDAR can replicate general patterns in avian communities and vegetation that are regularly observed along tropical land use intensity gradients. We tested the hypothesis (H1) that LiDAR can capture relevant variation in vegetation across the habitat gradient in coffee systems, specifically examining the prediction (P1) that the intensification of coffee production is associated with a decrease in vegetation density and complexity (Moguel & Toledo, 1999; Perfecto et al., 1996). We also tested the hypothesis (H2) that bioacoustics can capture functional signatures of avian communities across the habitat gradient in coffee systems. In particular, we examined three predictions: (P2.1) the proportion of forest specialists is higher in the forest while the proportion of generalist species increases with habitat disturbance (Buechley et al., 2015; Greenberg et al., 1997; Jarrett et al., 2020; Newbold et al., 2013; Ong’ondo et al., 2022; Sekercioglu et al., 2019; Tejeda-Cruz & Sutherland, 2004); (P2.2) the proportion of migrant species, which tend to be generalist and opportunistic, increases with habitat degradation
(Buechley et al., 2015; Greenberg et al., 1997; Newbold et al., 2013; Tejeda-Cruz & Sutherland, 2004); and (P2.3) the proportion of invertivores and understory specialists decreases with habitat degradation (Buechley et al., 2015; Jarrett et al., 2020; Newbold et al., 2013; Sekercioglu et al., 2019).
Materials and Methods
Study site
Data were collected between February 2022 and February 2023 in a coffee-growing agricultural matrix located between 900 and 1300 m a.s.l. in the Soconusco region of Chiapas in southern Mexico (Fig. 1B). The coffee landscape is composed of (i) a 278-ha shade-grown coffee farm (Finca Irlanda, Fig. 1D) classified as ‘diverse shade’ (Perfecto et al., 2003); (ii) a 300-ha sun coffee farm (Finca Hamburgo, Fig. 1E) classified as a ‘low diversity shaded non-organic coffee farm’ (Perfecto et al., 2003) and where canopy cover and vegetation diversity are significantly lower than in Irlanda and (iii) a forest fragment composed of primary and secondary forest and located between the two farms (Fig. 1C).
Habitat structure
LiDAR technology has been widely used to measure the three-dimensional structure of forests (Asner et al., 2014; Yao et al., 2012). We used two types of LiDAR data: (i) scans collected from the ground using a handheld device and (ii) scans collected from space from a device orbiting the Earth. These two LiDAR datasets were then compared to examine their capacity to capture the difference in vegetation between shade coffee, sun coffee and forest. Specifically, we derived vegetation metrics from LiDAR data to examine the prediction that vegetation density and complexity increase from sun coffee to shade coffee to forest.
Handheld LiDAR data collection
Vegetation data were collected at several locations within each zone of the coffee landscape using a GeoSLAM ZEB Revo RT handheld LiDAR scanner (hereafter referred to as handheld LiDAR). Scans were centred on a given point, from which the operator walked multiple overlapping figures-of-eight, following the procedure set out by Bauwens et al. (2016), and overall bounded at a radius of 25 m for a sampling duration of 5–7 minutes, depending on the accessibility of the terrain. Multiple scan locations were sampled randomly across each of the different zones, each separated by a minimum distance of 200 m, resulting in 8 scans in forest, 15 scans in sun coffee and 22 scans in shade-grown coffee (Fig. 1C). The procedure to process the handheld LiDAR data is described in Supplementary Material. At every scan location, we also visually recorded an estimate of canopy cover as the proportion of sky obstructed by vegetation as seen from the ground.
Handheld LiDAR vegetation metrics
We projected a three-dimensional voxel grid onto the LiDAR point clouds to divide them into 1 m3 voxels and obtain leaf area density (LAD) values per voxel. The voxel area of 1 m3 was chosen as a compromise of maximising resolution while avoiding high computational time. A vertical LAD profile (in m2/m3) was calculated for each handheld LiDAR scan, which gives the vertical distribution of one-sided leaf surface area per layer of a given height, across a given horizontal area. Based on the LAD profile, the leaf area index (LAI; in m2/m2), which measures one-sided leaf surface area per unit of ground surface (Watson, 1947), was calculated for understory (05 m), mid-height vegetation (5–10 m), canopy (10-20 m) and high canopy ( [ 20 m). We also calculated the foliage height diversity (FHD), which is a measure of the complexity of vegetation structure that has been shown to be a good indicator of habitat quality for birds and to correlate with bird diversity (MacArthur & MacArthur, 1961), as well as the maximum canopy height as the maximum height for which LAD [ 0.001 across the LAD profile. All vegetation metrics for the handheld LiDAR scans were calculated using the R package leafR (methodology presented in de Almeida et al., 2019).
Spaceborne LiDAR data

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Spaceborne LiDAR data were obtained from NASA’s Global Ecosystem Dynamic Investigation (GEDI) system, which provides high-resolution laser ranging using an instrument mounted on the International Space Station that samples footprints of 25 m diameter every 60 m, with a pointing uncertainty of 10 m (Dubayah et al., 2020). We used GEDI Level 2B data products (Dubayah et al., 2021) between February 2019 and February 2024. GEDI footprints for each zone (i.e. shade coffee, sun coffee and forest) were extracted within polygons representing convex hulls encompassing handheld LiDAR scans (Fig. 1C). A total of 36 footprints were obtained in sun coffee, 39 footprints were obtained in shade coffee, and 13 footprints were obtained in the forest. For each GEDI footprint, plant area index (PAI, in m2/m2) was calculated for understory (0-5 m), mid-height vegetation (5–10 m), canopy (10-20 m) and high canopy ( [ 20 m). PAI is

Figure 1. Study site. (A) Map showing the pattern of seasonal difference in avian richness across the Americas, calculated as the number of species present only during their breeding season minus the number of species present only during their non-breeding season (taken from Somveille et al., 2013). The red rectangle indicates the location of the study region on the Pacific slope of Chiapas, Mexico, whose topography is shown in (B). (C) Canopy height map obtained from Lang et al. (2023), covering the study area which includes a shade coffee farm (Finca Irlanda, in bottom-left quadrant), a sun coffee farm (Finca Hamburgo, in top-right quadrant) and forest patches. Open circles indicate the location of LiDAR scans obtained using the handheld device; polygons indicate minimum convex hulls around the handheld LiDAR scans for each zone; closed points indicate the location of GEDI footprints falling within each polygon; and black diamonds indicate the location of the autonomous acoustic monitoring units that were deployed in sun coffee (15°10018.69600 N, 92°19049.58400 W), shade coffee (15°10002.700 N, 92°200 37.400 W) and forest (15°1006.38400 N, 92°2008.80800 W). Each location where an autonomous acoustic monitoring unit was deployed was also scanned using the handheld LiDAR device. Photographs show (D) the shade coffee farm, (E) the sun coffee farm and (F) an autonomous acoustic monitoring unit composed of a solar panel, a battery (in yellow circle) and a BUGG acoustic recording device (in red circle).

highly comparable to LAI for describing vegetation structure across our landscape, as both measures have been shown to be closely related (Ziegler et al., 2023). Moreover, as point clouds from the handheld LiDAR scans do not differentiate between green and non-green components of the vegetation, LAI (calculated for handheld LiDAR) is directly comparable to PAI (calculated for spaceborne LiDAR). We also obtained the FHD as well as the maximum canopy height calculated as the height above ground of the received waveform signal start. All
GEDI data processing was conducted using the R package rGEDI.
Avian community composition and dynamics
Data collection
To monitor the composition of avian communities across the coffee landscape, audio data were collected via PAM using recording devices deployed at three locations: in the shade coffee zone, in the sun coffee zone and in the forest fragment (Fig. 1C). We used solar-powered BUGG acoustic recording devices (Sethi et al., 2018; www.bugg.xyz; Fig. 1F), which collected audio data between 20 February 2022 and 20 February 2023. At each site, audio data were recorded continuously 24 hours a day, except when the battery could not be sufficiently charged by the solar panel due to cloud and vegetation cover, and then saved into 5-min audio files. The audio data were retrieved once a month from the devices to be analysed. Automatic vocalization detection was conducted on the collected audio data using BirdNET, the largest open-source bird vocalization classifier (Kahl et al., 2021), which is highly precise in many regions of the world (Sethi et al., 2023). Details about data collection and processing used for considering and validating species detections are described in Supplementary Material.
Species’ temporal ranges
The species detection datasets (one per site) were split into 73 5-day periods. Adapting a methodology that has been used for analysing species detections in eBird checklists (Freeman et al., 2022), for each period and each species, we calculated a detection rate as the number of 5-min audio files with a detection for this species divided by the total number of 5-min audio files, which was then rescaled between 0 and 1 by dividing by the maximum detection rate value. The species was considered present during a given period if the detection rate was above 0.05, which is the threshold that was used in Freeman et al. (2022). To limit the effect of temporal variation in recording effort and variation in species detectability, which can generate false absences (i.e. period where the species’ detection rate is not above 0.05 despite the species being locally present during that period), we assumed that if the species was absent for fewer than eight 5-day periods (40 days) between two periods of presence, then the species was considered present continuously during that time span. This assumes that the species must be absent for at least 40 days to be considered as having migrated away from the area (and back), otherwise, it is considered as being present but not detected. We also considered that a period of presence that is separated from any other period of presence by more than four 5-day periods (20 days) is a false presence and therefore was set to an absence.
Functional signatures of avian assemblages
We obtained the following functional trait data for bird species from the AVONET dataset (Tobias et al., 2022): migration, habitat, habitat density and trophic niche. In addition, we collected data on foraging strata for the species detected in our dataset from Birds of the World (Billerman et al., 2022), which we encoded as binary values for foraging in three strata: understory, mid-height vegetation and canopy (a species can forage in more than one strata). From these trait data, we calculated functional signatures of avian assemblages for each 5-day period at each site, by dividing the number of occurring species with the following functional traits by the total number of occurring species: (i) species whose habitat is classified as “forest” and habitat density classified as “dense habitat” (giving a proportion of forest specialists), (ii)_ species that are foraging only in understory (giving a proportion of understory specialists), (iii) species that are foraging on all three strata, that is understory, mid-height vegetation and canopy (giving a proportion of strata generalists), (iv) species that are seasonal migrants (giving a proportion of migrants), (v) species that forage on invertebrates (giving a proportion of invertivores) and (vi) omnivorous species (giving a proportion of omnivores).
Results

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Vegetation scans from both handheld and spaceborne LiDAR indicated a decrease in foliage density, as measured by LAI/PAI, with increasing vegetation height for all three study zones (Fig. 2A,B). This decrease was less abrupt and more linear for the spaceborne LiDAR data than for handheld LiDAR. PAI from spaceborne LiDAR was consistently significantly lower in sun coffee than in forest (Table 1; Fig. 2B) and shade coffee below 10 m (Table 1; Fig. 2B). However, it was not significantly different between forest and shade coffee across all vegetation strata (Table 1). Differences between study zones for LAI measured from handheld LiDAR were consistent with the PAI results from spaceborne LiDAR (Fig. 2A,B) but without being statistically significant, except below 5 m where it was significantly higher in the forest fragment compared to both sun coffee and shade coffee (Table 1), which could be due to the lower number of scans from this method. Corresponding trends were found for FHD,

Figure 2. Vegetation structure measured by LiDAR. (A) Leaf area index (LAI) estimated using the handheld device for different ranges of height for each zone; grey boxplots indicate the values obtained at the exact location of deployment of the autonomous acoustic monitoring units at the start and end of the year, showing similar LAI trends to the rest of the scans but with a tendency towards higher values in shade coffee above 5 m. (B) Plant area index (PAI) estimated using GEDI footprints for different ranges of height for each zone. Foliage height diversity (FHD) is shown for each zone as calculated based on (C) ground handheld LiDAR and (F) GEDI. Canopy cover is shown for each zone as calculated based on (D) visual estimate and (G) GEDI. Canopy height is shown for each zone as calculated based on (E) ground handheld LiDAR and (H) GEDI. The boxplots indicate the first, second and third quartiles of the variation in vegetation metrics across the handheld LiDAR scans and GEDI footprints obtained within each study zone (Fig. 1C).

Table 1. Summary of results of the pairwise t-tests comparing the vegetation metrics between the study zones.
Method	Metric	Vegetation layer	Study zones comparison	
			Sun–Shade	Sun–Forest	Shade–Forest	
Handheld LiDAR	LAI	Understory	1.19	3.29*	3.18*	
		Mid	0.472	0.846	0.663	
		Canopy	0.526	0.250	^0.680	
	FHD	All	2.08	4.41***	3.94***	
	CH	All	5.64***	4.58***	^0.702	
Visual	Cover	All	11.6***	29.3***	12.4***	
Spaceborne LiDAR	PAI	Understory	2.88*	3.57**	1.58	
		Mid	2.86*	3.64**	1.88	
		Canopy	1.95	3.20*	2.17	
	FHD	All	4.98***	4.58***	1.36	
	CH	All	2.06	3.64**	2.57	
	Cover	All	4.21***	4.34***	1.22	
The table indicates the values for the t statistic and its associated significance level based on the Bonferroni correction adjusted P-value from pairwise t-tests. Stars represent significance levels at P < 0.05 (*), 0.01 (**) or 0.001 (***).

canopy cover and canopy height as measured by space- borne LiDAR, with forest tending to have higher values than the coffee plots, and shade coffee having higher values than sun coffee (Table 1; Fig. 2F–H). These results largely matched the ones obtained from the ground using handheld LiDAR and visual canopy cover estimates (Table 1; Fig. 2C–E), except for canopy height which was not higher in the forest than in shade coffee based on handheld LiDAR scans (Table 1; Fig. 2E).
Throughout the study period, the autonomous acoustic monitoring units recorded 17,654 hours of audio data together, and applying BirdNET to this audio data (using the data processing and validation described in SM) yielded 123,355 species detections. Overall, we were able to estimate the temporal ranges of occurrence of 50 species across our 3 sites and throughout the study period (Fig. 3). In sun coffee, 32 species were recorded. This was lower than in shade coffee, where 45 species were recorded. Finally, in forest, 26 species were recorded, but because of the significantly lower recording effort in the forest (Fig. S1), alpha diversity measures could not be readily compared to the coffee plots.
Compared to the coffee sites, the forest site had a consistently higher proportion of forest specialists (Fig. 4A,G) and understory specialists (Fig. 4B,H), and a lower proportion of migrants (Fig. 4D,J), strata generalists (Fig. 4C, I) and omnivores (Fig. 4F,L), throughout the year. Between the coffee sites, shade-grown coffee had a consistently higher proportion of forest specialists (Fig. 4A,G) and invertivores (Fig. 4E,K) throughout the year, but a lower proportion of understory specialists during the dry season (Fig. 4B). The proportion of migrants is similar in both coffee sites during the rainy season, but it exhibits wide variations during the dry season, with the first part of the dry season (November–December) corresponding to a peak in the proportion of migrants in sun coffee and a dip in shade coffee. The later, drier part of the dry season (February–March) corresponds to a peak in the proportion of migrants in shade coffee and in the forest (Fig. 4D).
Compared to the dry season, the rainy season had a higher proportion of forest specialists (two-sample t-test: t = ^7.397, P \ 0.001) combined with a lower proportion of strata generalists (t = 4.206, P \ 0.001) in the forest; a lower proportion of forest specialists (t = 5.868, P \ 0.001), strata generalists (t = 3.157, P = 0.003) and invertivores (t = 2.794, P = 0.008) in shade coffee; and higher proportion of invertivores (t = ^8.130, P \ 0.001) in sun coffee.
Discussion
Coffee production is intensifying in many regions across the world, notably through a decrease in the abundance and diversity of shade trees (Jha et al., 2014; Perfecto et al., 1996). This decrease in vegetation density and complexity in coffee plantations ultimately affects habitat quality for animals (Moguel & Toledo, 1999; Perfecto et al., 1996). Our results indicate that LiDAR technology can capture this pattern. Quantitative metrics derived from LiDAR scans to estimate vegetation density and complexity, namely LAI/PAI, FDH, canopy cover and canopy height, are all markedly higher in the forest than in sun coffee, with shade coffee in between the two (Fig. 2). These results are in line with (i) visual estimates of canopy cover at the locations of the scans (Fig. 2D), (ii) general differences in vegetation observed between Finca Hamburgo (i.e. the sun coffee farm), Finca Irlanda

Figure 3. Species’ temporal ranges in the study area. Lines indicate the range of presence of a species in a given site (shade coffee, sun coffee or forest). Species are ordered in descending order by body mass, with forest specialists separated from the rest. The blue vertical range in the background indicates the rain season, and the orange vertical ranges indicate the shoulder seasons between rain and dry seasons.
(the shade coffee farm) and the forest fragment (Perfecto (H1) that LiDAR can capture relevant variation in vegetaet al., 2003; Fig. 1D,E), and (iii) the literature on habitat tion across the habitat gradient in coffee systems.
degradation associated with the intensification of coffee In this study, we used two different types of LiDAR production (Moguel & Toledo, 1999; Perfecto technology: spaceborne LiDAR from GEDI, which scans et al., 1996). Thus, these results support the hypothesis the vegetation from above, and handheld LiDAR, which

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scans vegetation from the ground. We found good congruence between the two LiDAR datasets in terms of estimating the variation in vegetation across our study system, which reinforces the support for hypothesis H1. However, we found that handheld LiDAR appears limited for estimating vegetation density and structure in the canopy as it cannot differentiate between the different sites above the understory for both LAI and canopy height, which contrasts with visual estimates, previous work (Perfecto et al., 2003) and GEDI data (Fig. 2). Overall, our results suggest that GEDI data, which is freely available globally and does not require labour on the ground, is potentially a powerful and appropriate tool for estimating habitat quality along gradients of human land use intensity in tropical forests. It must be noted, however, that additional information on the richness and composition of tree species present in shade coffee and forest, and on the use of chemical pesticides, will be important to fully assess how habitat shapes animal biodiversity (Bakermans et al., 2012; Gebremichael et al., 2022; Perfecto & Van- dermeer, 2008; Philpott et al., 2008).
Birds are excellent indicators of ecosystem health as they occupy a wide range of ecological niches, and they are affected by the intensification of coffee production (Greenberg et al., 1997; Sekercioglu et al., 2019; Tejeda-Cruz & Sutherland, 2004). Our results support the hypothesis (H2) that the automated analysis of audio collected by PAM can capture known patterns in how coffee production and its intensification affect various functional signatures of avian communities. In particular, we found that the proportion of forest specialists is higher in forest while the proportion of generalists (i.e. strata generalists and omnivores) tends to increase with increasing habitat degradation associated with agricultural intensification, thus matching prediction P2.1 (Fig. 4; Greenberg et al., 1997; Tejeda-Cruz & Sutherland, 2004; Newbold et al., 2013; Buechley et al., 2015; Sekercioglu et al., 2019; Jarrett et al., 2020; Ong’ondo et al., 2022). We also found that with increasing habitat degradation the proportion of migrant species increases and the proportion of inverti- vores decreases (prediction P2.2; Fig. 4; Greenberg et al., 1997; Tejeda-Cruz & Sutherland, 2004; Newbold et al., 2013; Buechley et al., 2015; Sekercioglu et al., 2019; Jarrett et al., 2020), and that the proportion of understory specialists tends to be higher in forest than in coffee, in agreement with prediction P2.3. However, during the dry season the proportion of understory specialists tends to be higher in sun coffee than in shade coffee (Fig. 4B,H), which warrants further investigations to determine whether this pattern is generally observed across coffee agroecosystems. Overall, our results support the use of bioacoustics as a robust tool for estimating avian community composition along gradients of human land use intensity in tropical forests. PAM is increasingly affordable and widespread in ecological research (Ross et al., 2023; Schonfield & Bayne, 2017), and together with the availability of vocalization classifier like BirdNET, it renders the use of bioacoustics much more accessible and scalable (Sethi et al., 2023; Wood et al., 2022). In turn, this has the potential to be widely used to monitor biodiversity to inform sustainable agricultural practices.
In this study, we used an automated approach to processing bioacoustics data based on power-autonomous devices that continuously record audio data to be analysed by BirdNET. This approach allows for the consistent collection of data throughout the full annual cycle, which has previously not been possible with traditional survey methods. Where cellular coverage allows, this approach could be made fully autonomous with the use of SIM cards to continuously and remotely upload the data (BUGG devices have this capacity but the mobile signal was insufficient at our study sites). Our automated approach is particularly pertinent in mid-elevation areas in the northern tropics, where much of the world’s coffee is produced (Perfecto et al., 1996), as these regions are home to highly dynamic bird communities that host many non-breeding migrants during the northern winter (Fig. 1A; Somveille et al., 2013), many passage migrants during migration and altitudinal migrants. These regions are therefore well suited for examining the effect of land use intensity on the seasonal cycle of avian communities. Our results reveal that while several functional signatures of avian communities remain relatively stable throughout the year, some do vary. The proportion of migrants tends to be higher during the dry season across the study area, which makes sense given that it is located in a region of high richness in non-breeding migratory birds from the northern hemisphere (Fig. 1A; Somveille et al., 2013). In addition, in the forest during the rainy season, which corresponds to the breeding season for most species in southern Mexico, the proportion of forest specialists is higher than for the rest of the year, and the proportion of strata generalists is lower, while in sun coffee, the proportion of invertivores is lower during the dry season. These results indicate that forest specialists are potentially particularly dependent on the forest during the breeding season, and that diet specialists, particularly those relying on forest resources (i.e. invertivores), might be more affected by habitat degradation during the non-breeding season, corroborating previous findings in temperate forest birds (Alatalo, 1981; Michielsen et al., 2024).
While bioacoustics appears to be a robust tool for monitoring the functional signatures of avian communities along coffee production intensity gradients, it relies on several assumptions and has limitations. Firstly, bioacoustics technology can only detect vocal species, which

Figure 4. Annual trends in functional signatures. Functional signatures per 5-day period throughout the annual cycle (A–F) and per season (G–L) for each site (shade coffee, sun coffee and forest), showing trends in the proportion of (A, G) forest specialists, (B, H) understory specialists, (C, I) strata generalists, (D, J) migrants, (E, K) invertivores and (F, L) omnivores. Lines indicate smoothing curves fitted using local polynomial regression with a smoothing parameter of α= 0:25.

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might represent a limited subset of the local avian communities. Adding to this bias, bird vocalization classifiers like BirdNET necessitate large training datasets which tend to only be available for common North American and European species. Secondly, many forest birds tend to be more vocal and detectable during the breeding season, which may lead to seasonal biases in detections. We indeed found that detection rates were higher during the spring, which corresponds to the breeding season for most birds in our study region, with peaks in April–May in the forest and shade coffee and in May–July in sun coffee (Fig. 2C). Thirdly, the acoustic signal does not propagate uniformly depending on habitat structure (Ross et al., 2023), resulting in reductions in detection range as the vegetation gets denser. As we are investigating bird communities along a gradient of vegetation density and complexity, this might be associated with a corresponding gradient in detection probability. Finally, in addition to the increased occlusion of acoustic signals, the gradient in vegetation density and complexity is associated with a gradient in the occlusion of light, which leads to a lower capacity for recharging the devices’ batteries using solar power with increasing canopy cover. Recording effort was indeed markedly lower in forest, particularly during the rainy season (Fig. S1A), which results in a limited capacity for comparing our estimates of species temporal ranges between the different sites, thus affecting our ability to investigate alpha diversity across sites and throughout the year. Overall, as we focus on functional signatures of avian communities that are based on the proportional occurrence of functional groups (Sekercioglu et al., 2019), we assumed that these limitations do not generate consistent biases in the acoustic detection probability for species from the different functional groups of interest, for example generalists versus specialists. More research is however needed to investigate whether this assumption is valid to support the use of bioacoustics for monitoring avian communities in coffee agroecosystems. For example, the bioacoustics results could be validated by targeted seasonal bird censuses. More generally, methodological development, such as occupancy modelling that accounts for acoustic detection bias and misclassification (Brunk et al., 2023; Wood et al., 2024), and extensive validation of acoustic monitoring technology are required to reduce the biases and limitations described above.
An additional limitation of our study is the lack of replication as we sampled only one coffee landscape, comprising a single sun coffee farm, a single shade coffee farm and a single forest fragment. This could be an issue if our current sampling suffers from a bias in failing to detect regionally occurring species from a functional group of interest at the studied sites, for example missing a disproportionate amount of forest specialists in our forest fragment due to stochasticity in the distribution of species. Therefore, although we found support for hypotheses H1 and H2 in this coffee landscape, with estimates of vegetation structure and avian functional signatures matching known visual observation (for vegetation), previous studies in this system (for vegetation), and the general literature in coffee agroecosystems (for both vegetation and functional signatures), the sampling needs to be replicated to multiple coffee landscapes to obtain more robust statistical results.
This study contributes to the growing literature combining environmental data with species detections from bioacoustics (e.g. Brunk et al., 2023; Wood et al., 2024). Here, we show that remote sensing and the automated analysis of bioacoustics data can be reliable approaches to monitoring avian communities along tropical land use intensity gradients. Specifically, spaceborne LiDAR can be used to assess the vegetation structure profile of a delineated patch of land and determine how degraded it is compared to tropical forest, and bioacoustics can be used on the ground to evaluate the state of biodiversity in that patch using the functional signatures of the avian community as indicators. To scale this methodology and be able to use it for testing the efficacy of sustainable agricultural practices and certifying biodiversity-friendly production of tropical agricultural commodities, several steps are needed for future work. Firstly, more patches of natural, minimally disturbed vegetation need to be sampled to determine the GEDI-derived expected vegetation profile of natural forest across the ecoregion, so that it can be used as a baseline. More sampling is also required along the gradient of land use intensity in order to estimate the shape of the continuous response curves of community composition to habitat degradation. It would also be important to investigate how ARUs could be made more energy efficient to limit the decreasing recording effort with increasing vegetation density. For example, it might be possible to robustly estimate functional signatures by
only turning on the recorders during a certain time of day (de Arau´ jo et al., 2021) or at a certain temporal frequency (Wood et al., 2020). With a reduced amount of recording per unit of time, it would be possible to use other types of recorders, which are not solar-powered, for long-enough periods of time in less accessible patches of dense forest without having to regularly change batteries, therefore avoiding the reduced recording effort due to the occlusion of sunlight.
Patches of forest, low-intensity and high-intensity agriculture are not isolated systems as they are embedded into agroecological landscapes. In addition to estimating the shape of the continuous habitat–biodiversity relationship, designing biodiversity-friendly productive landscapes in tropical regions requires considering the spatial context and configuration (Bennett et al., 2020; Landa´zuri et al., 2024; Liso´ n et al., 2022; Valente et al., 2022). While patches of protected natural forest are crucial for the conservation of specialist species, as highlighted in our results, productive landscapes in the tropics have typically few natural forests remaining, and biodiversity-friendly agricultural practices, such as shade-grown coffee and cocoa, could play a critical conservation role as a refuge for biodiversity in areas that have been hard-hit by deforestation (Perfecto et al., 1996) but also a high-quality matrix connecting patches of natural forest (Estrada-Carmona et al., 2022; Perfecto & Vanderm- eer, 2010). The monitoring approach that we propose in this study could be used across a spatial context based on statistical matching techniques (Schleicher et al., 2019) and combined with connectivity theory (Albert et al., 2017), to guide the design of biodiversity-friendly productive tropical landscapes. This way, it could be used to contribute to the ongoing monitoring efforts of the Kunming-Montreal Global Biodiversity Framework to stem the decline of tropical biodiversity.
Acknowledgements
This work was made possible by support from the Wolfson Foundation and UCL Grand Challenges to M.S.
AUTHOR CONTRIBUTIONS
Marius Somveille: Conceptualization; methodology; data curation; investigation; formal analysis; supervision; funding acquisition; visualization; writing – original draft; writing – review and editing; project administration. Joe Grainger-Hull: Investigation; methodology; formal analysis; writing – review and editing; data curation. Nicole Ferguson: Methodology; investigation; writing – review and editing; formal analysis; data curation. Sarab S. Sethi: Methodology; data curation; investigation; writing –
Consistent and Scalable Monitoring of Birds and Habitats review and editing. Fernando Gonza´lez-Garc´ıa: Validation; writing – review and editing; formal analysis. Valentine Chassagnon: Methodology; data curation; investigation; writing – review and editing. Cansu Oktem: Investigation; data curation; writing – review and editing; formal analysis. Mathias Disney: Methodology; investigation; funding acquisition; writing – review and editing; formal analysis; data curation. Gustavo Lo´ pez Bautista: Investigation; data curation; project administration. John Vandermeer: Writing – review and editing; resources; investigation. Ivette Perfecto: Writing – review and editing; resources; investigation.
Data Availability Statement
The spaceborne LiDAR data from GEDI is open-access and free to use for research. The raw audio data and handheld LiDAR data are available upon request. Data for sampling effort, species detections obtained from BirdNET and habitat quality metric obtained from processing LiDAR data, together with the computer code used for the analysis, are available at: https://github.com/ msomveille/coffee_birds.git.
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Supporting Information
Additional supporting information may be found online in the Supporting Information section at the end of the article.
Figure S1.		
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Acoustic recording effort. Recording effort (A), detections (B) and detection rate (C) per 5-day period collected by each acoustic monitoring unit throughout a full annual cycle. Lines indicate smoothing
curves fitted using local polynomial regression with a smoothing parameter of α = 0:25. Whilst recording effort was consistent and nearly continuous in both shade and sun coffee (despite a couple of drops in sun coffee), it was significantly lower and varied substantially in forest, with a broad decrease during the rainy season (May– September). The total number of species detections was substantially higher in shade and sun coffee than in forest during the rainy season, but it was more variable during the dry season. Detection rate, however, was more comparable between the three sites. Figures S2–S9 below show the results of the sensitivity analysis, which assess how the functional signatures of avian communities captured through bioacoustics vary with changes in parameter values used for data processing for species detections and temporal ranges.
Figure S2.		Annual trends in functional signatures for a BirdNET precision threshold of 0.7, as part of the sensitivity analysis. Functional signatures per 5-day period throughout the annual cycle (A–F) and per season (G–L) for each site (shade coffee, sun coffee, and forest), showing trends in the proportion of (A, G) forest specialists, (B, H) understory specialists, (C, I) strata generalists, (D, J) migrants, (E, K) invertivores, and (F, L) omnivores. Lines indicate smoothing curves fitted using local polynomial regression with a smoothing parameter of α = 0:25.
Figure S3.		Annual trends in functional signatures for a BirdNET precision threshold of 0.9, as part of the sensitivity analysis. Functional signatures per 5-day period throughout the annual cycle (A–F) and per season (G–L) for each site (shade coffee, sun coffee, and forest), showing trends in the proportion of (A, G) forest specialists, (B, H) understory specialists, (C, I) strata generalists, (D, J) migrants, (E, K) invertivores, and (F, L) omnivores. Lines indicate smoothing curves fitted using local polynomial regression with a smoothing parameter of α = 0:25.
Figure S4.		Annual trends in functional signatures for a minimum of 10 detections per species over the whole annual cycle at a site, as part of the sensitivity analysis. Functional signatures per 5-day period throughout the annual cycle (A–F) and per season (G–L) for each site (shade coffee, sun coffee, and forest), showing trends in the proportion of (A, G) forest specialists, (B, H) understory specialists, (C, I) strata generalists, (D, J) migrants, (E, K) invertivores, and (F, L) omnivores. Lines indicate smoothing curves fitted using local polynomial regression with a smoothing parameter of α = 0:25.
Figure S5.		Annual trends in functional signatures for a minimum of 30 detections per species over the whole annual cycle at a site, as part of the sensitivity analysis.
Functional signatures per 5-day period throughout the annual cycle (A–F) and per season (G–L) for each site (shade coffee, sun coffee, and forest), showing trends in the proportion of (A, G) forest specialists, (B, H) understory specialists, (C, I) strata generalists, (D, J) migrants, (E, K) invertivores, and (F, L) omnivores. Lines indicate smoothing curves fitted using local polynomial regression with a smoothing parameter of α = 0:25.
Figure S6.		Annual trends in functional signatures for a false absence maximum time span of 20 days, as part of the sensitivity analysis. Functional signatures per 5-day period throughout the annual cycle (A–F) and per season (G–L) for each site (shade coffee, sun coffee, and forest), showing trends in the proportion of (A, G) forest specialists, (B, H) understory specialists, (C, I) strata generalists, (D, J) migrants, (E, K) invertivores, and (F, L) omnivores. Lines indicate smoothing curves fitted using local polynomial regression with a smoothing parameter of α = 0:25.
Figure S7.		Annual trends in functional signatures for a false absence maximum time span of 60 days, as part of the sensitivity analysis. Functional signatures per 5-day period throughout the annual cycle (A–F) and per season (G–L) for each site (shade coffee, sun coffee, and forest), showing trends in the proportion of (A, G) forest specialists, (B, H) understory specialists, (C, I) strata generalists, (D, J) migrants, (E, K) invertivores, and (F, L) omnivores. Lines indicate smoothing curves fitted using local polynomial regression with a smoothing parameter of α = 0:25.
Figure S8.		Annual trends in functional signatures for a false presence maximum separation period of 10 days, as part of the sensitivity analysis. Functional signatures per 5-day period throughout the annual cycle (A–F) and per season (G–L) for each site (shade coffee, sun coffee, and forest), showing trends in the proportion of (A, G) forest specialists, (B, H) understory specialists, (C, I) strata generalists, (D, J) migrants, (E, K) invertivores, and (F, L) omnivores. Lines indicate smoothing curves fitted using local polynomial regression with a smoothing parameter of α = 0:25.
Figure S9.		
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Annual trends in functional signatures for a false presence maximum separation period of 40 days, as part of the sensitivity analysis. Functional signatures per 5-day period throughout the annual cycle (A–F) and per season (G–L) for each site (shade coffee, sun coffee, and forest), showing trends in the proportion of (A, G) forest specialists, (B, H) understory specialists, (C, I) strata generalists, (D, J) migrants, (E, K) invertivores, and (F, L) omnivores. Lines indicate smoothing curves fitted using local polynomial regression with a smoothing parameter of α = 0:25.