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. 2024 Mar 11;87(1):e23615. doi: 10.1002/ajp.23615

Losing lemurs: Declining populations and land cover changes over space and time

Pamela R Narváez‐Torres 1, Nicola K Guthrie 1, Typhenn A Brichieri‐Colombi 2, Cressant P Razafindravelo 3, Zachary S Jacobson 1, Fredo Tera 3, Daniel V Rafidimanana 4, Zé‐Elinah Rahasivelo 5, Melody A Petersen 6, Hasinala Ramangason 1, Lea Randall 2, Jana M McPherson 2, Cynthia L Frasier 7, Axel Moehrenschlager 8, Sheila M Holmes 9, Edward E Louis Jr 3,7, Steig E Johnson 1,✉
PMCID: PMC11650950  PMID: 38467477

Abstract

Forest loss and degradation due to land cover changes imperil biodiversity worldwide. Subtropical and tropical ecosystems experience high deforestation rates, negatively affecting species like primates. Madagascar's endemic lemurs face exceptionally high risks of population declines and extirpation. We examined how short‐term land cover changes within a fragmented landscape in southeastern Madagascar impacted the density of lemur species. Using line transects, we assessed density changes in nine lemur species across five forest fragments. Diurnal surveys were conducted monthly from 2015 to 2019 on 35 transects (total effort = 1268 km). Additionally, 21 transects were surveyed nocturnally in 2015 and 2016 (total effort = 107.5 km). To quantify forest cover changes, we generated land use/land cover (LULC) maps from Sentinel‐2 imagery using supervised classification for each year. For the LULC maps, we overlayed species‐specific buffers around all transects and calculated the proportion of land cover classes within them. We observed declines in the annual densities of four diurnal and cathemeral lemur species between 2015 and 2019, with species‐specific declines of up to 80% (Varecia variegata). While the density of two nocturnal species decreased, one increased fivefold (Cheirogaleus major) between 2015 and 2016. By 2019, Grassland was the dominant land type (50%), while Paddy Fields had the smallest coverage (1.03%). Mature Agricultural Land increased the most (63.37%), while New Agricultural Land decreased the most (–66.36%). Unexpectedly, we did not find evidence that higher forest cover supported a higher lemur population density within sampled areas, but we found support for the negative impact of degraded land cover types on three lemur species. Our study underscores the urgent need to address land‐use changes and their repercussions for primate populations in tropical ecosystems. The diverse responses of lemur species to modified habitats highlight the complexity of these impacts and emphasize the importance of targeted conservation efforts.

Keywords: conservation, deforestation, density, habitat loss, Madagascar

Losing lemurs: declining populations and land cover changes over space and time.

graphic file with name AJP-87-e23615-g004.jpg

Research Highlights

  • Kianjavato experienced a substantial loss of forest cover between 2015 and 2019, with an average annual forest loss (3.4%) that is considerably higher than the national average (1.1%).

  • We observed a decrease in the density of six out of seven lemur species in the Kianjavato forest fragments.

  • Structurally simpler land cover types such as grasslands emerged as a factor negatively influencing the density of three lemur species, while tree fallow positively influenced three species.

Abbreviations

AOI

area of interest

COFAV

Corridor Forestier d'Ambositra‐Vondrozo

GLMM

generalized linear mixed models

HMM

hurdle mixed models

KAFS

Kianjavato Ahmanson Field Station

LULC

Land Use Land Cover

MBP

Madagascar Biodiversity Partnership

RNP

Ranomafana National Park

VOI

Vondron'Olona Ifotony

1. INTRODUCTION

Human activities have been modifying the natural surface of our planet for thousands of years (Goudie, 2018; Pyne, 1997). However, the current extent and speed of land cover changes far exceed historical levels (IPCC, 2023). These changes have a significant impact on ecosystems and environmental processes at local, regional, and global scales (Hasan et al., 2020; IPCC, 2023; Roy et al., 2022). Moreover, they represent one of the most critical threats to terrestrial biodiversity, notably due to the loss, degradation, and fragmentation of forests, largely attributed to their conversion into other land types, such as agricultural areas (Almeida‐Rocha et al., 2017; Estrada et al., 2017; 2018; Maxwell et al., 2016). The impact of these threats is particularly pronounced in tropical regions, where subtropical and tropical ecosystems experience alarmingly high rates of forest loss, accounting for more than 90% of global deforestation between 2000 and 2018 (FAO, 2022). Primates, being mainly arboreal and forest‐dwelling species inhabiting tropical regions, are highly susceptible to land cover changes (Almeida‐Rocha et al., 2017; Fernández et al., 2022). This vulnerability has led to around 67% of primate species worldwide being threatened with extinction, with human‐induced deforestation being one of the leading causes International Union for Conservation of Nature (IUCN, 2020; Torres‐Romero et al., 2023).

In fragmented landscapes, forest patches—large or small—do not exist in isolation from the matrix that surrounds them. The matrix, sometimes referred to as “nonhabitat” for forest‐reliant species, is the mosaic of different land cover types that surround forests and may include anything from urban developments to agricultural lands to anthropogenic grasslands (Fahrig, 2017). These land cover types can influence the ability of forest‐dwelling organisms to travel between habitat patches and/or find resources or breeding sites and, thus, can impact the local abundance and the persistence of these organisms in fragmented landscapes (Fahrig, 2007; Prevedello & Vieira, 2010; Sanches et al., 2022; Tiang et al., 2021). However, the persistence of primates in disturbed or fragmented landscapes is closely tied to their ecological traits, for example, degree of dietary specialization, activity pattern, body size, and home range extent (Boyle & Smith, 2010; Eppley et al., 2020; Machado et al., 2023). Species with highly specific habitat and dietary requirements are likely to be more affected than generalist species, as the latter can access and make use of a wider variety of resources (Boyle & Smith, 2010; Calle‐Rendón et al., 2019; Eppley et al., 2020; Machado et al., 2023). Large‐bodied species with highly frugivorous diets are more vulnerable to habitat loss and fragmentation because they require larger home ranges, which can limit their ability to respond to habitat alteration (Boyle & Smith, 2010; Lehman et al., 2006a; Lenz et al., 2014; Machado et al., 2023). Folivorous species, on the other hand, have been observed to do well with certain levels of disturbance and even increase in abundance in disturbed areas due to increased leaf quality and productivity (Ganzhorn, 1995; Johns & Skorupa, 1987; Lenz et al., 2014). For instance, omnivorous primate species like Sapajus flavius (Lins & Ferreira, 2019) and Rungwecebus kipunji (Bracebridge et al., 2013) or highly folivorous species like Colobus angolensis palliatus (Anderson et al., 2007; Dunham, 2017) and Chlorocebus djamdjamensis (Mekonnen et al., 2017, 2018) have been documented to use agricultural lands.

Despite some species’ ability to use the matrix, it may be challenging for some arboreal animals to navigate the matrix if its structure is too different or isolated from the forest. Matrix‐use depends on factors such as canopy height, food availability, and the distance from the forest (Galán‐Acedo et al., 2019; Pozo‐Montuy et al., 2011). In general, navigating the matrix can also lead to increased risks of predation or being hunted by humans or dogs due to lack of vegetation cover, as well as transmission of parasites and diseases between humans and primates (Candelero & Pozo‐Montuy, 2010; Chapman et al., 2005; Chaves et al., 2022; Estrada et al., 2012; Galán‐Acedo et al., 2019; Sanches et al., 2022).

Madagascar is a biodiversity hotspot where forest loss and fragmentation have occurred on a large scale since the 1950s (Harper et al., 2007; Ralimanana et al., 2022). Over the past 70 years, this country has lost 44% of its natural forest cover, and approximately 46% of the remaining forests are located within 100 m of an edge (Vieilledent et al., 2018a). In eastern Madagascar, forests are converted to agricultural land for small‐scale shifting agriculture, locally known as tavy. However, after a few crop cycles, soil quality often decreases, and the land is abandoned, forming grasslands or eroded, bare soils (Styger et al., 2007). The remaining forests are crucial habitats for most of the island's endemic species, including over 100 species of lemurs, which are some of the world's most imperiled mammals (Schwitzer et al., 2014). Due to diverse factors such as forest loss, fragmentation, and hunting, 96.3% of these species are now at risk of extinction (Borgerson et al., 2022; Fernández et al., 2022; IUCN, 2020), with declining population trends recorded for most species (IUCN, 2020). As many lemur species are nearly exclusively arboreal (Mittermeier et al., 2023), they are highly susceptible to the effects of forest conversion to other land cover types. However, some lemur species, especially nocturnal or cathemeral species, are capable of navigating matrix environments, either as areas of regular use (e.g., Cheirogaleus shethi, Hending et al., 2017; Microcebus spp., Guthrie et al., 2022; Steffens et al., 2021), for intermittent foraging (e.g., Lemur catta, LaFleur & Gould, 2009), or for transitory use while moving among forest patches (e.g., Eulemur flavifrons, Prodger et al., 2018). Additionally, Webber et al. (2019) reported that five species of nocturnal and mostly solitary lemurs (Mirza zaza, Phaner parienti, Microcebus sp., Cheirogaleus sp., and Lepilemur dorsalis) are regularly found in shade plantations in Northern Madagascar. Despite these observations of nonforest use (see Eppley & Goodman, 2022 for a review), little is currently known about how variation in matrix composition may impact lemur populations.

We investigated lemur population trends in the forest fragments of Kianjavato Commune and surrounding areas, a priority site for lemur conservation in southeastern Madagascar (Schwitzer et al., 2013). Our objectives were to assess lemur density trends and evaluate how land cover composition outside of forests impacts lemur density. We hypothesized that the impact of land cover in the matrix on the density of lemurs in remaining forest fragments would depend on (a) how qualitatively similar to forest the vegetation structure of land cover types in the matrix is; and (b) the ecological traits, specifically diet and home range size, of each lemur species. In particular, we anticipated that a matrix dominated by open land cover types with limited vertical structure (e.g., rice fields or grassland) would have a greater negative impact on lemur density in remaining forest fragments than a matrix containing forest‐like vegetation structures (e.g., tree fallows or fruit tree plantations). Forest‐like vegetation structure might, after all, facilitate lemur mobility and provide foraging opportunities, and thus improve connectivity among remaining forests (Anderson et al., 2007; Prevedello & Vieira, 2010; Steffens et al., 2021). We also anticipated that highly frugivorous lemur species that require relatively larger home ranges, such as Varecia variegata and Eulemur rufifrons will be most negatively affected by land cover changes, while lemur species with folivorous or omnivorous diets and smaller home ranges, such as Avahi peyrierasi, Hapalemur griseus, and Microcebus jollyae, would be the least affected by land cover changes. We expected Cheirogaleus major and Eulemur rubriventer with mainly frugivorous diets and small home ranges, Daubentonia madagascariensis, with an omnivorous diet and large home range, and Prolemur simus, with a folivorous diet and large home range, to show an intermediate response to land cover changes. These expectations reflect the greater likelihood for folivorous and omnivorous species than frugivores to find appropriate food resources in nonforest land‐use types and the relatively lower importance of large tracts of contiguous forest for species with smaller home ranges (Boyle & Smith, 2010; Galán‐Acedo et al., 2023; Johns & Skorupa, 1987).

2. METHODS

2.1. Ethics statement

The research reported was covered by ACC Certificates AC15‐0041 and AC19‐0026, issued by the University of Calgary Animal Care Committee. The study followed all legal requirements for conducting research in Madagascar (permits 074/14/MEF/SG/DGF/DCB. SAP/SCB, 036/15/MEEF/SG/DGF/DCB. SAP/SCB, 161/16/MEEF/SG/DGF/DSAP/SCB, 97/17/MEEF/SG/DGF/DSAP/SCB. Re, 164/18/MEEF/SG/DGF/DSAP/SCB. Re, 050/19/MEEF/SG/DGF/DSAP/SCB. Re) and adhered to the ethical principles for the treatment of nonhuman primates set forth by the American Society of Primatologists (ASP).

2.1.1. Study site and study species

This study was based at the Kianjavato Ahmanson Field Station (KAFS), located in the Kianjavato Commune in southeastern Madagascar (Figure 1) and managed by the Madagascar Biodiversity Partnership, a Malagasy nongovernmental organization. The study site ranges in elevation from 52 to 571 m above sea level and is characterized by an annual rainfall of 1800 mm, falling mostly between December and March (Manjaribe et al., 2013). The vegetation consists of remnants of lowland primary and secondary humid forest and dense bamboo (Manjaribe et al., 2013). Seven forest fragments in the area serve as habitat for nine lemur species (Table 1; Holmes et al., 2015). We collected data for this study from five forest fragments: Karakandatra, Sangasanga, Tsiazombazaha, Tsitola, and Vatovavy. These fragments vary in size (from 45 to 518 ha, Tsitola being the largest) and isolation distances (from 67 to 6900 m, Vatovavy being the most isolated).

Figure 1.

Figure 1

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Map of the area of interest (AOI) for 2019, including five forest fragments in the Kianjavato region. Satellite imagery obtained from ESRI (2021).

Table 1.

Lemur species that inhabit the Kianjavato region, their diet, activity pattern, mean body mass, home range size (species mean ± SD), and IUCN status.

Scientific name Common name Diet Activity pattern Body mass (kg) Home range size (ha) IUCN Red list statusb
Avahi peyrierasi Peyrieras’ woolly lemur Folivorousc Nocturnalc 1.04d 1.4e VU
Cheirogaleus major Greater dwarf lemur Frugivorousf Nocturnalc 0.34g 4.4h VU
Daubentonia madagascariensis a Aye‐aye Omnivoroush Nocturnali 2.40i 973.12j EN
Eulemur rubriventer Red‐bellied lemur Frugivorousi Cathemerali 2.00i 15.27 ± 4.5k VU
Eulemur rufifrons Red‐fronted brown lemur Frugivorousi Cathemerali 2.25i 45.9 ± 24.2k VU
Hapalemur griseus a Eastern lesser bamboo lemur Folivorousl Diurnalm 0.80c 15l VU
Microcebus jollyae Jolly's mouse lemur Omnivorousi Nocturnali 0.064o 0.26 ± 0.14o EN
Prolemur simus Greater bamboo lemur Folivorousp Cathemeralc 2.61q 50.2r CR
Varecia variegata Black‐and‐white ruffed lemur Frugivorousi Diurnalc 3.81e 37.41 ± 6.4k CR
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Abbreviations: CR, critically endangered; EN, endangered; IUCN, international union for conservation of nature; VU, vulnerable.

a

Species not included in the analyses due to a small number of detections during transect surveys (see below).

b

IUCN (2020).

c

Rowe and Myers (2017.)

d

Lei et al. (2008).

e

Harcourt (1991).

f

Lahann (2007).

g

Lei et al. (2014).

h

Burrows and Nash (2010).

i

Mittermeier et al. (2023).

j

Randimbiharinirina et al.(2018).

k

Holmes et al. (2019).

l

Tan (1999).

m

Kappeler (2012).

n

N. Guthrie, unpublished data.

o

Mihaminekena et al. (2024).

p

Frasier et al. (2015).

q

McGuire et al. (2009).

r

Baden et al. (2008).

Aerial photography in the 1950s indicates that forest cover was high in and around the Kianjavato area, with estimates suggesting 62.50% of our area of interest (see Figure 1 for AOI) was covered in forest at the time. However, more recent satellite imagery revealed a significant decrease in forest cover since; by 1973, only 9.89% (38.59 km2) remained, and this further decreased to 4.65% (18.12 km2) by 2010 (data for calculation obtained from Vieilledent et al., 2018b). Deforestation in the Kianjavato commune is mainly caused by tavy. This practice is similar to that described in Styger et al. (2007) and involves cycling through different crops with fallow periods to restore soil nutrients. The cycle consists of cutting down and burning the forest before growing crops, eventually leading to soil nutrient depletion and land abandonment. The land is left to regrow vegetation, but only grasses and ferns tend to grow back after several crop cycles (Styger et al., 2007). In some cases, fires used to clear the land can become uncontrolled and spread to neighboring crops, forests, and reforestation areas (Styger et al., 2007).

2.1.2. Lemur survey methods

To assess the abundance of lemurs in the Kianjavato forest fragments, we conducted diurnal and nocturnal surveys using a line‐transect distance sampling methodology (Buckland et al., 2010). We conducted diurnal surveys monthly (thus all seasons were included) from 2015 to 2019, with a total of 35 transects across five forest fragments. We surveyed 10 transects in Karakandatra, three in Sangasanga, three in Tsiazombazaha, 11 in Tsitola, and eight in Vatovavy (Figure 2). Each diurnal transect was walked 6–29 times per year, with a mean of 14 walks per year. All transects were 0.5 km long, making the total effort for diurnal surveys 1268 km (annual average = 211 km; annual range 194–300.5 km). We conducted nocturnal surveys in 2015 and 2016 using a subset of 21 transects in Sangasanga, Tsiazombazaha, Tsitola, and Vatovavy. As some nocturnal species go into torpor or hibernation during the Austral winter months (e.g., C. major; Blanco et al. 2018), we conducted all nocturnal surveys between September and May. The nocturnal transects were walked three to seven times per year with an average of five walks per year, resulting in a total effort of 107.5 km (annual average = 53.75 km; annual range 51–56.5 km). We conducted all surveys at a speed of approximately 1 km/h, from 6:30 to 14:00 h for diurnal surveys and 18:30 to 23:00 h for nocturnal surveys. We collected the following data during surveys: lemur species sighted, the number of individuals detected, and the height of the animals in the canopy. Additionally, the distance and bearing from the observer were recorded to calculate the perpendicular distance from the transect to the detected individuals. We considered all species that were detected more than 20 times for our density estimation and further analysis.

Figure 2.

Figure 2

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(a) Map of the area of interest in Kianjavato, Madagascar, with the classification of the land use and land cover types for 2019. The map also shows the 35 transects used in this study. (b) The upper panel shows an example of the species‐specific buffers overlayed around each transect from 2015 to 2019. (c) The radii of the buffers (range: 29–400 m) were selected using the reported home ranges of each study species. Satellite imagery obtained from ESRI (2021).

2.1.3. Lemur annual density estimations

Annual density estimates were produced for seven lemur species using the Distance package (Miller et al., 2019) in R (R Core Team, 2022). We could not estimate annual densities for H. griseus and D. madagascariensis as they were only sighted two times. To model detection functions for the remaining species, that is, the probability of an animal being detected based on its distance from a given line (Buckland et al., 2010), we pooled each species’ sightings across years (2015–2019 for diurnal and cathemeral species, 2015–2016 for nocturnal species) given the low number (N < 30) of annual sightings (Buckland et al., 2015; Rodríguez‐Caro et al., 2017). By combining the data for all years, we assumed that the detection probability of a species was the same throughout the study period. We modeled detection functions and evaluated the fit of the half‐normal, hazard rate, and uniform key functions with and without cosine series adjustments (Buckland et al., 2015; Rodríguez‐Caro et al., 2017). The best models were selected based on Akaike's Information Criterion corrected for small sample sizes akaike information criterion (ΔAICc < 2; Hurvich & Tsai, 1989), the goodness of fit, and the shape of the detection function (Buckland et al., 2001). We calculated species‐specific annual densities by including year as a stratification factor (Buckland et al., 2015).

2.1.4. Land‐Use Land Cover (LULC) changes

We derived LULC maps of our field site for each year (2015–2019) from Sentinel 2 A—Level 1 C imagery with 10 m spatial resolution. Five satellite images were downloaded from the USGS EarthExplorer website (https://earthexplorer.usgs.gov/); these images were taken on 29/09/2015, 04/08/2016, 20/07/2017, 14/08/2018, and 29/08/2019. We chose images with <10% cloud cover over our study area and within the same season across years to reduce the effects of seasonal variation in vegetation and agricultural practices (The European Space Agency, 2023).

We used the CATALYST Professional program version 2222.0.1 (PCI Geomatics, 2024) to crop each image to the study extent and run atmospheric corrections, which decreased the spatial resolution to 20 m. Based on familiarity with the region and Environmental Systems Research Institute (ESRI) World Imagery Wayback (ESRI, 2021), local guides and authors identified 18 land cover types. We ran a supervised maximum likelihood classification using 800 polygons generated in ArcGIS 10.8.1 (ESRI, 2020) of the 18 different land cover types. We retained 30% of polygons for each land cover type to test the map classification accuracy with confusion matrices and Cohen's Kappa statistic (Cohen, 1960). We combined some land cover types to achieve an overall classification accuracy >80% for each year. The final land cover types derived from image classification included Forest, Tree Fallow, Grassland, Mature Agricultural Land, New Agricultural Land, Cleared Land, Paddy Fields, Sand, Water, and Rock, as well as Shadow and Cloud (Table 2). Average and overall classification accuracy for each year were: 2015 kappa = 0.849 (sd = 0.003), overall accuracy = 86.93%; 2016 kappa = 0.765 (sd = 0.004), overall accuracy 80.16%; 2017 kappa = 0.844 (sd = 0.003), overall accuracy 85.37%; 2018 kappa = 0.810 (sd = 0.004), overall accuracy 83.44%; 2019 kappa = 0.827 (sd = 0.004), overall accuracy 85.45%. We identified seven of the 12 land cover types as relevant to our research question (i.e., Forest, Tree Fallow, Grassland, Mature Agricultural Land, New Agricultural Land, Cleared Land, and Paddy Fields) and used them as predictor variables in the models (Table 2). We excluded Sand, Water, Rock, Shadow, and Cloud from our analyses as we were focused on the impacts of changing land cover types. Sand, Water, and Rock remained unchanged during the study period. We also made an effort to obtain satellite images that did not include Shadow and Cloud.

Table 2.

Description of the land cover types in the Kianjavato landscape used in the analysis and qualitative assessment of canopy cover.

Land cover type Description Canopy coverage
Forest Primary and secondary rainforests, as well as a mature agroforest planted within a forest fragment that retained the canopy structure of the rainforest. Higher coverage
Tree Fallow Treed fallow land that included the following tree species: Ravenala sp., Valiha diffusa, Albizia spp, and Macaranga obovata Higher coverage
Mature Agricultural Land Plantations of fruit trees with crown connections that were maintained for several years. These plantations were usually a mix of large trees (e.g., Artocarpus heterophyllus, and Litchi chinensis) with dense canopy cover, as well as banana and coffee trees. Higher coverage
New Agricultural Land Areas where crops were planted without crown connections. These may eventually become mature agricultural land as the plants grow larger or could be crops on a cycle where they are entirely removed during harvesting. Lower coverage
Grassland Land consisting of grasses with the occasional tree or shrub. Also known as roranga and usually 0.4‐1.5 m in height (see Manjaribe et al., 2013 for more details). Lower coverage
Paddy Fields Lowland, usually irrigated, rice fields. Lower coverage
Cleared Land was recently cleared of vegetation. Lower coverage
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Source: S. Andriamampianina, unpublished data; N. Guthrie, unpublished data.

We refined LULC maps in ArcGIS; we removed clouds and shadows from known forested areas and combined shadows in unknown areas to ensure consistency for any changes detected between years. Shadow and cloud coverage could cause some negligible impact on summary metrics. We digitized the roads (both the paved highway and secondary unpaved roads), large and small rivers, and villages using high‐resolution imagery (0.5 m) ESRI World Imagery Wayback from 2019 (23/09/2019; ESRI, 2020). Because high‐resolution Wayback Imagery was unavailable for all years, we assumed that roads, rivers, villages, and lone houses did not vary between years, and the digitized features for 2019 were integrated into the LULC classification maps for each year (2015–2018; See supporting Information S1: Figure S1).

From the refined LULC maps, we measured land cover change across years within species‐specific buffer distances around the 35 transects (Figure 2) created in QGIS 3.28.2 (QGIS.org, 2022). Buffer radii (range: 29–400 m) were selected using the reported home range sizes of the seven species for which we calculated annual density estimates (Table 1). We then overlayed the species‐specific buffers on the LULC maps for each year and calculated the proportion of land cover types within the buffer extents using the LecoS plugin in QGIS (Jung, 2016).

2.1.5. LULC and lemur abundance

Using multivariate models, we investigated the relationship between the proportion of different land cover types and lemur density. To avoid overfitting the models, we merged Paddy Fields, Cleared Land, and Grassland into a single predictor “Open Land” as we considered the biological relevance of these three variables to be similar. Forest was significantly correlated with multiple variables and thus was not included in models with other predictor variables; we ran separate models with Forest only to evaluate the effect of this land cover type on lemur density.

We followed a two‐step approach in our models (Buckland et al., 2015; Rodríguez‐Caro et al., 2017). First, we modeled the detection functions and included animal height in the canopy (meters), forest fragment ID, and year as detection covariates (i.e., covariates that could influence the probability of detection). We evaluated the fit of the key functions, and the best‐fitting models were selected based on AICc (Hurvich & Tsai,1989), the goodness of fit, and the shape of the detection function (Buckland et al., 2001). The best‐fitting model was then utilized to estimate the effective area, which we included as an offset in models in the second step (Buckland et al., 2015). Second, we used mixed models to model lemur density, with the number of individuals sighted per transect per year as the response variable. We used hurdle mixed models (HMM) due to the zero inflation and overdispersion present in the data of some species (E. rubriventer, E. rufifrons, P. simus, and V. variegata) and generalized linear mixed models (GLMM) for the other species (Avahi peyrierasi, C. major, and M. jollyae). Both types of models assumed negative binomial distributions. We ran the models using the glmmTMB package (Brooks et al., 2017), and ΔAICc values were compared using the AICcmodavg package (Mazerolle, 2020). The predictor variables were the proportions of the different types of land cover types within the buffer of each species (i.e., Mature Agricultural Land, New Agricultural Land, Tree Fallow, and Open Land); we also included the log‐transformed effective area as an offset term and transect and fragment ID as random effects. We selected candidate models with variables that best explained the variation in lemur density based on ΔAICc scores and weights (Burnham & Anderson, 2002).

3. RESULTS

3.1. Changes in lemur density

We recorded all nine species of lemurs living in the Kianjavato forests during line‐transect surveys. However, two species, H. griseus and D. madagascariensis, were only observed during the first year of our study. E. rufifrons was the only day‐active species observed in all five forest fragments surveyed, while nocturnal M. jollyae and A. peyrierasi were the only nocturnal species observed in all the fragments surveyed at night.

There was an apparent decrease in the annual density of the diurnal and cathemeral lemur species over 5 years (Table 3). Note that due to our low number of sightings, standard errors (SE) were high for some species and years (see Table 3; Buckland et al., 2015). For the day‐active species, V. variegata, with the highest density in 2015, decreased by approximately 80% by 2019. Further, P. simus was not sighted in the last year of surveys. E. rubriventer densities decreased by roughly 70% during the study period, and E. rufifrons by almost 40%. Among nocturnal species (with only 2015–2016 data), we observed a substantial decrease ( > 30%) in the densities of A. peyrierasi and M. jollyae during the second year of the study, while C. major increased five‐fold.

Table 3.

Estimated annual densities (individuals/km2 ± SE) of the lemur species present in the Kianjavato forest fragments.

Year Avahi peyrierasi Cheirogaleus major Eulemur rubriventer Eulemur rufifrons Microcebus jollyae Prolemur simus Varecia variegata
2015 17.24 ( ± 5.66) 3.45 ( ± 1.99) 1.59 ( ± 0.88) 6.16 ( ± 1.78) 72.32 ( ± 16.43) 1.29 ( ± 1.01) 7.40 ( ± 2.40)
2016 11.67 ( ± 4.42) 17.21 ( ± 10.15) 2.65 ( ± 1.18) 4.81 ( ± 1.31) 42.34 ( ± 12.29) 1.92 ( ± 1.34) 4.08 ( ± 1.44)
2017 ‐ ‐ 0.80 ( ± 0.78) 4.32 ( ± 1.72) ‐ 1.06 ( ± 0.77) 4.45 ( ± 2.44)
2018 ‐ ‐ 0.73 ( ± 0.55) 2.55 ( ± 0.87) ‐ 0.72 ( ± 0.56) 2.66 ( ± 1.14)
2019 ‐ ‐ 0.47 ( ± 0.22) 2.33 ( ± 0.98) ‐ 0a 1.61 ( ± 0.81)
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Note: Nocturnal surveys were only conducted in 2015–2016.

a

The estimated density of Prolemur simus in 2019 does not indicate the absence of the species in our study site; other studies and long‐term monitoring have confirmed its presence in the study area.

3.2. Changes in LULC

In 2019, across our area of interest (total area = 390 km2), Grassland was the most prevalent land cover type, covering ~49.49% of the total area (Figure 3), followed by Tree Fallow (25.83%) and Mature Agricultural Land (9.30%). Forest, New Agricultural Land, Cleared Land, and Paddy Fields each covered less than 5% of the total area (Figure 3). Forest, decreased by 2.13 km2 between 2015 and 2019 (Figure 4), with a mean annual loss of 0.53 km2 (3.51%; see Supporting Information S1: Table S1). However, it is worth noting that even though we saw a decrease in forest area overall, between 2015 and 2016, we saw an increase in forest of 0.46 km2 or 2.97%. During the 5‐year period, Mature Agricultural Land had the largest increase in land cover (63.37%), while New Agricultural Land saw the largest decline (66.39%).

Figure 3.

Figure 3

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Changes in total area for seven land use and land cover types over five years in Kianjavato, Madagascar.

Figure 4.

Figure 4

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Forest loss between 2015 and 2019 in Kianjavato, Madagascar. Satellite imagery obtained from ESRI (2021).

The general trends across the land cover types in the Kianjavato landscape between 2015 and 2019 (Figure 3) were also found within the species‐specific buffers (Table 4). Forest and New Agricultural Land decreased, while Tree Fallow, Grassland, and Mature Agricultural Land increased. Cleared Land and Paddy Fields increased by less than 2% at the landscape level but remained stable at the lemur species‐specific level (see Supporting Information for areas per land cover type within the species‐specific buffers; Supporting Information S1: Tables S2‐S8).

Table 4.

Percentage change in mean area of the land cover types within species‐specific buffers from 2015 to 2019 in Kianjavato, Madagascar.

Land cover type Avahi peyrierasi Cheirogaleus major Eulemur rubriventer Eulemur rufifrons Microcebus jollyae Prolemur simus Varecia variegata
Forest –6.9% –6.5% –6.4% –5.3% –6.1% –5.2% –5.5%
Tree fallow 9.0% 8.1% 7.0% 6.2% 9.8% 6.0% 6.5%
Mature Agricultural Land 2.3% 3.4% 3.5% 3.4% 0.9% 3.5% 3.4%
New Agricultural Land –2.4% –2.6% –3.0% –4.4% –2.0% –4.5% –4.2%
Grassland 1.2% 1.0% 2.4% 4.2% 0.0% 4.3% 3.9%
Paddy Fields –0.1% –0.1% 0.0% 0.0% –0.1% –0.1% 0.0%
Cleared –0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
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3.3. LULC changes and lemur density

For the first step of our modeling (lemur detection functions), the key functions and covariates included in the best detection models varied per species (Supporting Information S1: Table S9). The null detection model was the best‐fitting model for A. peyrierasi (hazard rate key function), E. rubriventer (half‐normal key function), E. rufifrons (hazard rate key function), and V. variegata (hazard rate key function). The detection model with year as a covariate and half‐normal key function provided the best fit for C. major. For M. jollyae and P. simus, the best‐fitting model included animal height in the canopy as a covariate and hazard rate and half‐normal key functions, respectively.

In the second step of our analysis (land cover predictors of lemur density), our results showed that Open Land was the only land cover type included in the top models of most species. This land cover type had a negative effect on the density of C. major, E. rufifrons, and V. variegata but had a positive effect on E. rubriventer (Table 5). Tree Fallow positively affected three species: C. major, E. rubriventer, and M. jollyae. Additionally, New Agricultural Land positively affected the density of E. rufifrons and V. variegata, while Mature Agricultural Land had a positive effect on E. rufifrons and a negative effect on C. major. None of the land cover types predicted the densities of A. peyrierasi or P. simus. Due to the high correlation of Forest with other land types, we ran separate models with this predictor only and found that it was included in the top model only for C. major (with a positive effect on density).

Table 5.

Models predicting the variation in the density of seven lemur species in the Kianjavato forest fragments.

Species Top models K ΔAICc AICc w LogLik
Avahi peyrierasi Null model 3 0 1 –46.91
Cheirogaleus major Open Land (–35.71) 4 0 0.23 –33.94
Null model 2 1.17 0.13 –36.91
Mature Agricultural Land (–9.55) 4 1.58 0.11 –34.73
Forest (3.10) 4 1.82 0.09 –34.85
Open Land (‐48.88) + Tree Fallow (7.10) 5 1.86 0.09 –33.58
Mature Agricultural Land (‐4.69) + Open Land (–34.68) 5 1.98 0.09 –33.64
Eulemur rubriventer Open Land (5.48) 3 0 0.43 –106.02
Open Land (1.95) + Tree Fallow (3.88) 9 0.66 0.31 –104.15
Eulemur rufifrons Mature Agricultural Land (2.56) + New Agricultural Land (2.16) 11 0 0.31 –250.41
Mature Agricultural Land (2.46) 9 1.08 0.18 –253.22
Mature Agricultural Land (2.46) + Open Land (‐0.04) 11 1.35 0.16 –251.09
Microcebus jollyae Null model 2 0 0.28 –90.20
Tree Fallow (4.08) 4 1.75 0.12 –88.69
Prolemur simus Null model 3 0 0.97 –49.23
Varecia variegata New Agricultural Land (11.09) + Open Land (‐0.92) 11 0 0.32 −190.77
New Agricultural Land (10.51) 9 0.99 0.20 −193.53
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Note: Each model includes: the variables in the model with effect sizes in parenthesis, the number of model parameters (K), the difference in AICc between the given model and the best model (ΔAICc), the AICc weight (AICc w), and the log‐likelihood (LogLik). “Open Land” included the land use types: Grassland (including upland rice), Paddy Fields, and Cleared Land. We only included models ΔAICc <2 units from the top model.

4. DISCUSSION

We observed a trend of declining lemur densities across six of the seven species for which we were able to gather sufficient data within the Kianjavato forest fragments. We also observed an increase in the density of the 7th species. The dominant land cover type within the Kianjavato landscape was Open Land, primarily comprised of Grassland, and roughly 50% of the area throughout the study period. During our study period, Mature Agricultural Land experienced the greatest increase, while New Agricultural Land experienced the most substantial decrease. Open Land impacted the densities of four lemur species (mostly negatively), followed by Tree Fallow with a positive effect on the densities of three species. Forest cover in our area of interest decreased by 13.60% from 2015 to 2019, with a mean annual deforestation rate of 3.51%. However, Forest was included as a (positive) predictor in the best‐fitting model for only a single species, C. major. Overall, our findings show partial support for the hypothesis that a more treed matrix, including Tree Fallow and Mature Agricultural Land, supports a higher lemur densities; whereas a greater proportion of structurally simpler land cover types in the matrix impacts lemur densities negatively in adjacent forest patches. Although these effects appear to be modulated by species traits to some degree, the trait categorizations considered in our study are likely too coarse to provide an adequate picture of the interactions between species‐specific needs and behaviors on the one hand and habitat loss and fragmentation on the other.

4.1. Trends in lemur densities

We observed a decline in the density of six lemur species in the Kianjavato forest fragments. Our estimates for the densities of all day‐active species in 2019, the final year of our study, were lower than those reported for these species in the literature. In particular, the densities of E. rubriventer (0.47 individuals/km2), E. rufifrons (2.33 individuals/km2), and V. variegata (1.61 individuals/km2) were substantially lower than those found in nearby continuous, relatively pristine forest in Ranomafana National Park (RNP; E. rubriventer: 8.17–13.96 individuals/km2; E. rufifrons: 8.05–26 individuals/km2; Herrera et al., 2011; V. variegata: 24.3 individuals/km2; Baden, 2011). In the case of P. simus, the Kianjavato population was estimated to be around 100 individuals in 2008 (McGuire et al., 2009). Although our estimate for 2019 suggests that the P. simus population may have been extirpated from the study site, other studies (Chen et al., 2021; Narváez‐Torres et al., 2022; Rakotonanahary et al., 2021) and a monitoring program by the Madagascar Biodiversity Partnership (MBP; Madagascar Biodiversity Partnership, 2023) have confirmed its continued presence in the area. Density estimates for this group‐living species were low throughout the study. It is possible that we saw so few P. simus or none at all during our surveys because our transects were located inside the forest fragments, whereas this species uses food sources that are often located outside the forest fragments (see below).

Our estimates for 2016 (the latest survey year) yielded mixed results for the nocturnal species. The density of M. jollyae (42.32 individuals/km2) was notably higher when compared with those of other Microcebus species, such as Microcebus rufus (15.81 individuals/km2) at RNP (Herrera et al., 2011), while the density of A. peyrierasi (11.67 individuals/km2) was lower than the reported range for RNP (19.31–40.31 individuals/km2; Herrera et al., 2011). Conversely, our density estimates for C. major (17.21 individuals/km2) fell within the range reported for this species in Vohibola III (10.4–62.5 individuals/km2; Lehman et al., 2006b), another continuous eastern rainforest site north of RNP. C. major was also the only species that increased density within the 2015–2016 period. These findings suggest that the cheirogaleids M. jollyae and C. major, with relatively high or increasing densities, may display greater resilience to land cover types characterized by intense human activities, particularly areas previously forested but converted to different land uses. Species in both genera are known to regularly use nonforest habitats (Hending, 2021; Steffens et al., 2021), and their population densities can vary positively with lower tree cover and a higher degree of human impact (Hending, 2021).

Even though we only observed H. griseus and D. madagascariensis at the beginning of our study, we know that these species are still present in the area (Chen et al., 2021; Madagascar Biodiversity Partnership, 2023; Rakotondrazandry et al., 2021). The lack of observations of these species is likely attributed to their elusive behavior. D. madagascariensis is particularly challenging to study due to its unique traits such as its nocturnal pattern, solitary nature, and a very large home range (Table 1; Sterling & McCreless, 2007).

4.2. LULC changes and effects on lemur densities

Although Kianjavato was designated as a priority site for lemur conservation (Schwitzer et al., 2013), the area has gone through substantial forest loss. In 2010, the forest in Kianjavato covered 18.12 km2, representing 4.65% of the total area (Vieilledent et al., 2018b). However, by 2019, further deforestation had reduced the forested area to 13.53 km2, accounting for only 3.49% of the land cover. During the 5‐year span of our study (2015–2019), the forest cover experienced a substantial decline of 2.14 km2 or 13.60%. This resulted in a mean annual deforestation rate of 3.51%, far exceeding the national rate reported by Vieilledent et al. (2018a) for the 2010–2014 period, which was 1.1% per year. Additionally, when compared with other similarly affected eastern sites like the Mananara Nord District (Schüßler et al., 2018), the deforestation rate at Kianjavato remained notably higher. However, a devastating forest fire in 2017 destroyed a large area within the Tsitola fragment, which is still recovering, and this may account for some of the forest loss during our study.

Our results aligned with our prediction that land cover changes would most strongly affect highly frugivorous species with relatively larger home ranges (i.e., V. variegata and E. rufifrons). Open Land had a negative effect on the density of V. variegata, whose density declined by 80% over the study period, and E. rufifrons, whose density declined by roughly 40%. The negative effects of Open Land on these species may reflect these lemurs avoiding structurally simpler land cover types that lack physical supports, such as branches or trunks big enough to accommodate the locomotion needs of arboreal primates (Cudney‐Valenzuela et al., 2022; Pozo‐Montuy et al., 2011). Moreover, V. variegata is known to be sensitive to disturbance and to prefer forested habitats with abundant resources (Balko & Brian Underwood, 2005). However, recent research by Petersen et al. (2023) suggests that the diet of V. variegata in Kianjavato can be flexible, showing higher dietary diversity compared with populations in continuous forests. For example, the diet of V. variegata at this site includes a high percentage of Ravenala flowers (Petersen, 2022); Ravenala is mostly found in Tree Fallow (see Table 2). Nonetheless, and in line with the density decline observed in our study, Kianjavato's small forest fragments represent low‐quality habitat for V. variegata (Petersen et al., 2023).

Species we had expected to be least affected by land cover changes due to their folivorous or omnivorous diets and smaller home range sizes (i.e., H. griseus, A. peyrierasi, and M. jollyae) were not immune to land cover change. We were unable to test land cover effects on H. griseus due to the low sample size of observations and the consequent lack of density estimates for this species. For A. peyrierasi, however, we observed a (1‐year) 32% decrease in density in our study. Yet none of the selected predictor variables could explain this decrease. Given observations elsewhere of higher densities at lightly disturbed sites compared with highly disturbed ones (Herrera et al., 2011), it seems that A. peyrierasi prefers higher quality habitats. M. jollyae also declined in density by 42% over the 1 year for which we had data. Our models suggested that its density responded positively to Tree Fallow. It is possible that M. jollyae prefers Tree Fallow over intact forest. Other Microcebus species prefer more degraded habitats, such as edge or matrix vegetation over forest interiors, likely due to the presence of insects, insect secretions, and other food sources that increase in the forest edge or in areas with moderate disturbance (Ganzhorn, 1995; Guthrie et al., 2022; Lehman et al., 2006b). More highly disturbed habitats, like the grasslands dominating in Kianjavato, may lack such food sources and explain the species’ overall decline.

We expected an intermediate response to land cover changes from D. madagascariensis (a relatively large, nocturnal and omnivorous species), C. major (small, nocturnal, and frugivorous species), E. rubriventer, and P. simus (relatively large, cathemeral and folivorous species). We were unable to test land cover effects on D. madagascariensis due to the low sample size of observations. We found that the predictor variables we selected could not explain density changes for P. simus, perhaps because our transects did not cover core areas of the species’ habitat. However, throughout this species’ known range, P. simus populations were typically observed in matrix vegetation (e.g., Tree Fallow) or edge habitats (e.g., King et al., 2013, Mihaminekena et al. 2024, Olson et al., 2013). Mihaminekena et al. (2024) reported that P. simus are not obligate bamboo feeders and can expand their realized dietary niche to include a greater variety of food items than previously documented. This lemur species seems to specialize in plants that thrive in low to moderately disturbed areas such as woody bamboos, Clidemia hirta, Setaria sp., and Ravenala spp., along with numerous agricultural crops often grown near human settlements (Dransfield, 1998; King et al., 2013; Mihaminekena et al. 2024; Tan, 2007). C. major was the only species to increase in density, at least in the 1‐year interval we had data for. In alignment with the species’ preference for using the upper parts of trees and reliance on tree holes for sleeping and hibernating (Lahann, 2008), our models suggested that its density responded positively to forest area and Tree Fallow, but negatively to Mature Agricultural Landscapes and Open Land. This suggests that degraded but structurally forest‐like habitats such as Tree Fallow provide at least some of the physical structures the species requires, but that these are absent in plantations and structurally simple, open habitats.

E. rubriventer, another frugivore with a small home range, showed large declines in density, suggesting it remains vulnerable to environmental changes at Kianjavato. Surprisingly, however, our models suggested that Open Land influenced its density positively. The higher density in presumably more marginal habitat might result from reduced competition with closely related E. rufifrons (which, as noted, had lower densities in proximity to Open Land). While evidence for active avoidance is mixed, these species can maintain nonoverlapping home ranges in the Kianjavato landscape (Holmes et al., 2019). Another possibility is that density increases in response to Open Land reflect (temporary) crowding as individuals concentrate in the shrinking area of suitable habitat (Bolt et al., 2022; Grez et al., 2004). Given that E. rubriventer is highly territorial (Overdorff, 1993), it is also possible that its density responds positively to more Open Land if that equates to greater habitat fragmentation. Territorial species have been observed to benefit from habitat fragmentation when patch boundaries help define territories and reduce conflict with conspecifics (Grez et al. 2004).

Our findings revealed that no individual land cover type impacted all lemur species. Even variables included in most top models, such as Open Land, could have opposing (species‐specific) effects on lemur densities. Notably, forest cover did not emerge as a robust predictor of lemur densities, despite all of these species being arboreal and dependent on forest habitats to varying degrees. Heavily treed areas in proximity to natural forest (Tree Fallow) did have the predicted positive impact on the densities of several species that we expected to be more resilient (e.g., C. major and M. jollyae); however, this effect was not universal and other relatively intensive land cover categories (e.g., both New and Mature Agricultural Land) also had positive effects on the densities of some species. The limitations of these variables in predicting lemur densities may be due to other important but unmeasured drivers of lemur abundance. Importantly, hunting may be a major threat to lemur populations across Madagascar (Borgerson et al., 2022; Schwitzer et al., 2013). However, recent analysis suggests that consumption of lemurs is very low for all species in the Kianjavato region, except for the highly abundant (but potentially declining) M. jollyae (Borgerson et al., 2018). Aspects of fragmentation that were not specifically considered here, for example, patch size and isolation (i.e., fragmentation per se), as well as different aspects of vegetation structure (e.g., tree height, tree diameter at 1.3 m height, canopy openness), may have impacted species densities (Arroyo‐Rodríguez et al., 2013; Hilário et al., 2022). Moreover, the temporal scope of our study, spanning 5 years, may encompass population changes within the bounds of normal fluctuations. Lemur populations may not be responding to immediate or recent changes in landscape structure. Rather, the declines may follow previous land cover changes, the impacts of which are only now registering in lemur densities. In the extreme, this may represent an extinction debt, where populations have been set on a path of steep decline from such earlier environmental disturbances (Kuussaari et al., 2009).

5. CONSERVATION IMPLICATIONS

Lemurs are involved in a complex set of interactions with their habitat. Through activities like seed dispersal and pollination, lemurs play a critical ecological role in maintaining healthy and diverse forests that, in turn, provide essential support for the well‐being of local human communities (Ganzhorn et al., 1999; Razafindratsima et al., 2018; Valenta & Lehman, 2016). Thus, the decline in lemur populations could have severe ecological and social consequences.

Our study did not reveal a clear link between the decrease in forest cover and the densities of all lemur species. However, the reduction in forest cover in the Kianjavato area, combined with the consistent declines and low densities of five lemur species, highlights the urgent need to continue conservation efforts in the region. The observed negative relationship between several lemur species and structurally simpler land cover types also emphasizes the importance of preserving forest habitats. Since 2008, MBP and the local community have worked together on an extensive reforestation initiative (Madagascar Biodiversity Partnership, 2023). As of February 2024, they have successfully planted 6,697,664 trees in the area, aiming to establish lemur habitats and create corridors connecting forest fragments (Madagascar Biodiversity Partnership, 2023)—actions which, owing to slow maturation of forest trees, will likely take many years to positively impact lemur populations.

Meanwhile, during our study, in 2015, the Corridor Forestier d'Ambositra‐Vondrozo (COFAV) was formalized as a vast protected area safeguarding forests between the major National Parks of southeastern Madagascar. This new protected area includes a portion of the study area (Tsitola and Sangasanga). By the end of 2017, there was also greater awareness in the local community of the severe legal penalties for engaging in tavy in protected areas. Finally, toward the end of our study, local bodies called Vondron'Olona Ifotony (VOI) were established to monitor and enforce land‐use laws. The VOIs, including Tsitola (finalized in July 2018), Vatovavy (finalized in April 2019), and Sangasanga (finalized in December 2020), were created to address forest loss and land cover changes. While their impact may not be fully reflected in our study period, the VOIs represent a crucial response to the environmental challenges the Kianjavato community faces. These efforts, coupled with MBP's reforestation project and the establishment of stringent laws and community awareness, offer hope for mitigating deforestation and fostering a positive trajectory for lemur habitats in the region.

AUTHOR CONTRIBUTIONS

Pamela R. Narváez‐Torres: Conceptualization (equal); data curation (lead); formal analysis (lead); funding acquisition (supporting); investigation (equal); methodology (equal); project administration (equal); resources (supporting); validation (lead); writing—original draft (lead); writing—review & editing (equal). Nicola K. Guthrie: Conceptualization (equal); data curation (lead); formal analysis (lead); investigation (equal); methodology (equal); project administration (equal); resources (supporting); validation (lead); visualization (lead); writing—original draft (equal); writing—review & editing (equal). Typhenn A. Brichieri‐Colombi: Conceptualization (supporting); data curation (equal); formal analysis (equal); investigation (supporting); methodology (supporting); validation (equal); writing—original draft (supporting); writing—review & editing (equal). Cressant P. Razafindravelo: Data curation (equal); investigation (equal); methodology (supporting); project administration (equal); writing—review & editing (equal). Zachary S. Jacobson: Conceptualization (equal); data curation (supporting); formal analysis (supporting); investigation (equal); methodology (supporting); visualization (supporting); writing—original draft (supporting); writing—review & editing (equal). Fredo Tera: Investigation (supporting); methodology (supporting); project administration (equal); writing—review & editing (equal). Daniel Rafidimanana: Investigation (equal); methodology (equal); writing—review & editing (equal). Zé‐Elinah Rahasivelo: Investigation (equal); writing—review & editing (equal). Melody A. Petersen: Conceptualization (equal); writing—original draft (supporting); writing—review & editing (equal). Hasinala Ramangason: Conceptualization (equal); formal analysis (supporting); writing—original draft (supporting); writing—review & editing (equal). Lea Randall: Conceptualization (equal); writing—review & editing (equal). Jana M. McPherson: Conceptualization (equal); formal analysis (supporting); writing—review & editing (equal). Cynthia L. Frasier: Data curation (supporting); funding acquisition (supporting); resources (supporting); writing—review & editing (supporting). Axel Moehrenschlager: Conceptualization (equal); funding acquisition (equal); supervision (equal); writing—review & editing (equal). Sheila M. Holmes: Data curation (equal); funding acquisition (supporting); investigation (equal); methodology (equal); project administration (equal); resources (equal); supervision (supporting); writing—review & editing (equal). Edward E. Louis: Funding acquisition (equal); project administration (equal); resources (equal); supervision (equal); writing—review & editing (equal). Steig E. Johnson: Conceptualization (equal); formal analysis (supporting); funding acquisition (equal); methodology (equal); project administration (equal); resources (equal); supervision (equal); validation (equal); writing—original draft (equal); writing—review & editing (equal).

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

Supporting information

Supporting information.

AJP-87-e23615-s001.docx (9.8MB, docx)

ACKNOWLEDGMENTS

The authors would like to thank the editor, Karen Bales; the organizers of this special issue, Amanda Melin and Julie Teichroeb; the anonymous reviewers; and Madagascar National Parks for their permission to conduct research. The authors would also like to acknowledge the invaluable contribution of Randrianasolo Rantomampianina, Razafindrenabo Gilbert, Rabesoa Delphin, Tiana Berthin, Ramarolahy Jean Fulbert, Rakotoson Emilys Edgarçon, Mbana Ferdinah, Zafimahazo Pierre and Rakotoarisoa Patrick, the local guides who assisted with data collection in the field. The authors would also like to thank Danica J. Stark for the assistance with data analysis. Furthermore, the authors are thankful for the generous funding provided by the Natural Sciences and Engineering Research Council of Canada, the Government of Alberta, Mexico's National Council for Science and Technology, Mitacs, Conservation International, Primate Conservation Inc., the International Primatological Society, the Calgary Zoological Society, Omaha's Henry Doorly Zoo and Aquarium, and the Ahmanson Family Foundation.

Narváez‐Torres, P. R. , Guthrie, N. K. , Brichieri‐Colombi, T. A. , Razafindravelo, C. P. , Jacobson, Z. S. , Tera, F. , Rafidimanana, D. , Rahasivelo, Z.‐E. , Petersen, M. A. , Ramangason, H. , Randall, L. , McPherson, J. M. , Frasier, C. L. , Moehrenschlager, A. , Holmes, S. M. , Louis, E. E. , & Johnson, S. E. (2025). Losing lemurs: Declining populations and land cover changes over space and time. American Journal of Primatology, 87, e23615. 10.1002/ajp.23615

Pamela R. Narváez‐Torres and Nicola K. Guthrie contributed equally to this study.

DATA AVAILABILITY STATEMENT

The data supporting this study's findings are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting information.

AJP-87-e23615-s001.docx (9.8MB, docx)

Data Availability Statement

The data supporting this study's findings are available from the corresponding author upon reasonable request.

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