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Article

Effects of Seasonality on the Large and Medium-Sized Mammal Community in Mountain Dry Forests

by
Carmen Julia Quiroga-Pacheco
1,2,*,
Ximena Velez-Liendo
3,4 and
Andreas Zedrosser
1,5
1
Department of Natural Sciences and Environmental Health, University of Southeast Norway, 3800 Bø, Norway
2
Department of Mammalogy, Natural History Museum “Alcide d’Orbigny”, Av. Potosi 1458, Cochabamba, Bolivia
3
WildCRU, The Recanti-Kaplan Centre, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL, UK
4
Chester Zoo, Cedar House Caughall Road Upton Chester, Cheshire CH2 1LH, UK
5
Institute of Wildlife Biology and Game Management, University of Natural Resources and Life Sciences, A-1180 Vienna, Austria
*
Author to whom correspondence should be addressed.
Diversity 2024, 16(7), 409; https://doi.org/10.3390/d16070409
Submission received: 13 June 2024 / Revised: 27 June 2024 / Accepted: 4 July 2024 / Published: 14 July 2024
(This article belongs to the Section Animal Diversity)
Browse Figures
Versions Notes

Abstract

:
Seasonality drives natural processes, impacting environmental factors like temperature and resource availability, leading to shifts in wildlife communities. The Andean dry forests exhibit a marked seasonality, with a dry and cold season (May–September) and a warm, wet season (October–April). In a year-long remote camera survey in Southern Bolivia, we identified 29 medium to large mammal species, 18 outside their known distribution ranges. While overall species richness remained stable, photographic records varied between seasons. Capture rates, reflecting species richness and abundance, were more influenced by season and habitat. Wet season rates were lower, but higher in all other habitats compared to the mountain bush and grasslands. Rates increased with altitude and distance to hiking trails, but decreased with increasing distance from main roads. Medium to large mammals were more active during the dry season, indicating adjustments in response to seasonal changes. Our results suggest a cumulative impact of various factors beyond mere seasonality, and call for adjustments in global species distributions. Moreover, emphasize the need for biodiversity monitoring in dry forest habitats, particularly regarding responses to environmental shifts and human-induced alterations.

1. Introduction

Seasonality profoundly influences natural systems, governing biological and evolutionary processes like reproduction, predator-prey interactions, and migration [1]. Its impact on resource availability shapes wildlife communities, influencing species behavior and system composition [2,3,4]. Understanding these effects is crucial for predicting population trends, responses to environmental alterations, human-caused and otherwise, and aids in conservation and management planning [5].
Seasonal environmental changes, such as temperature and water variations, directly affect plant biodiversity and growth, thereby shaping wildlife communities’ structure [6]. Mammals, integral to biodiversity, play roles in resource control through processes like seed dispersal or predation [7]. Seasonality significantly influences mammalian species richness and community structure [6,8]. For instance, carnivore home range sizes, as found by Duncan et al. [9], are affected by life-history traits, such as body size, resource availability, and seasonality. Small mammal communities, according to Butet et al. [8], are influenced by seasonality and habitat, while Norbu et al. [10] noted differences in the number of small mammals trapped between seasons.
Mountainous areas commonly display strong seasonality tied to temperature and water availability variations, in the form of precipitation, soil humidity, and the effect of the elevational gradient on surface water sources [11,12]. The Andean dry forests experience an annual rainfall of 200–650 mm, with a dry period from May to September, the coldest months [13]. These forests have evolved in relative isolation, which has resulted in a high level of species endemism but with restricted population sizes in these dry forests [14,15]. Historical human use has left only patches of the original vegetation, posing conservation threats, yet they harbor unique diversity [16]. Unfortunately, research on these ecosystems is limited with Andean dry forests being the most neglected in terms of conservation knowledge in the region [17]. In Bolivia, these forests are patchily distributed in the center and south, and limited literature is available about them [18,19].
We conducted a year-long remote camera survey of medium to large-sized mammals in southern Bolivian Andean dry forest patch, with the aim to understand how seasonality affects mammalian species richness and abundance in a mountain dry forest. Wildlife inventories are important conservation tools, providing spatio-temporal data on species distributions, richness, abundance, and community structure [20,21]. This inventory marks the first large-scale study of mammalian species this habitat and region. Given the pronounced seasonal differences, we predict that (a) the medium- and large-sized mammal community structure, i.e., number of species, is higher during the wet compared to the dry seasons; (b) seasonality has a stronger effect on mammalian species richness and abundance compared to the physical characteristics of a study site, i.e., altitude, distance to roads and rivers; and (c) the daily mammalian activity patterns, i.e., the number of observations of a given species within 24 h, are higher during the wet compared to the dry season.

2. Materials and Methods

2.1. Study Area

The study focuses on an area of ~800 km2 privately owned lands by five communities in the provinces of Cercado (~21°17′ S, 64°24′ W) and Mendez (~21°11′ S, 64°27′ W) in the Tarija Department, southern Bolivia (Figure 1). Situated within the Boliviano-Tucumano Ecoregion, the area experiences marked seasonality and slightly colder climate than neighboring ecoregions, and elevations range from 898 to 2631 (m) above sea level [16]. These dry forests exhibit diverse microclimates and are characterized by steep hillsides, cliffs, valleys, and distinctive vegetation makeup, forming a transitional xeric habitat between drier habitats to the west and southeast and the more humid habitats to northeast [22]. Seasonal precipitation fluctuations are significant, with <200 mm the May-September dry season and around 650 mm during the October-April wet season [22,23]. Mean maximum temperatures range between 22–26 °C through the year, while mean minimum temperatures drop from 14–16 °C in the wet season to 4–8 °C, during the dry season [24].
The area’s vegetation, adapted to xeric climate, is dominated by white quebracho (Schinopsis haenkeana), Argentine saguaro (Trichocereus terscheckii), bromeliads (e.g., Deuterocohnia longipetala, Puya alba, and Bromelia cerra), leguminous plants (Caesalpinia pluviosa and Caesalpinia paraguariensis), cat-claw acacia (Acacia praecox), and cacti (e.g., Cereus heankeanus) [22]. Human densities range from 2–25 people/km2. The study area is officially classified as agricultural lands by regional and national administrations [16,19], and is characterized by large uninhabited areas interspersed by small rural communities dedicated to either agriculture or extensive cattle ranching [16]. Local communities have mostly kept their traditional land use practices, i.e., small-scale slash-and-burn style agriculture for local consumption, small-scale firewood extraction, and extensive livestock management in forested areas.

2.2. Remote Camera Survey

We deployed 56 remote cameras along game trails on an altitudinal range from 1002–2393 m between December 2018 and October 2019 [25]. On the large scale, we selected sites for camera deployment based on optimal coverage of the entire study area as well as on the potential presence of Andean bears (Tremarctos ornatus), an umbrella species of high regional, national, and international conservation importance [26]. On a small scale, selection of study locations was based on landowners’ permissions to set up camera stations at a given location. A camera station was installed at a central location along a game trail and from there other camera stations were installed ~2 km apart in a “spider web design” along game trails. This distance, minimum distance travelled by a bear in a day [27], was chosen to keep independence of camera trap stations. The mean number of cameras installed along a specific trail was 2.4 ± 0.6 (SD) and varied in relation to the length of the game trail as well as the accessibility of the area and terrain. Each camera station included a single camera facing the game trail at approximately 50 cm above the ground from ~5 m [25,28]. Camera-trap locations were recorded using a handheld GPS device (Garmin 64 s, Garmin International, Inc., Olathe, Kansas, USA). We used the camera models Bushnell TrophyCam and Cuddeback Ambush IR, and randomly chose which model to use at a given location. Cameras were set to take a burst of 3 consecutive photos with no delay between bursts as long as the motion sensor was triggered [21]. Date and time of day of photos was automatically stored together with the photos. Cameras worked continuously during the study period, and its no-glow technology allowed to take pictures during night-time and under low visibility conditions.

2.3. Data Handling and Analysis

Images from remote camera stations were downloaded and processed using Digikam 7.0.2 (www.digikam.org, accessed on 22 February 2021). Medium-sized and large mammals on images were identified at the species level by experts with the help of guidebooks [29,30,31]. We only selected images of medium to large-sized terrestrial mammals, i.e., >250 g [31]. The Bolivian chinchilla rat (Abrocoma boliviensis) was the only small-sized mammal included into the analysis due to its high conservation importance and the unique physical characteristics that make it easily identifiable [32,33]. All other small-sized mammals were excluded from further analysis due to the difficulty in species identification and the low capture rates due to study design as well as due to camera model and/or camera set up. For the resulting species, we recorded the conservation status and potential distribution according to the IUCN Red List of Threatened Species [34].
We divided the study period into the dry season (June–September) and wet season (December–March) [22]. April, May, October, and November were considered as transitional months in terms of precipitation and temperature changes and therefore excluded from the analysis. We defined an independent photographic event as an image or group of images of the same species at a given location within a 1-h time span [21,35]. For each species and season, we calculated the Relative Abundance Index (RAI) as the number of independent photographic events divided by the number of camera days multiplied by 100 [36,37]. We also calculated the naïve occupancy, i.e., the number of camera stations where a species was found divided by the total number of camera stations [20]. Following Chao & Chiu [38], we defined abundance as the number of observed individuals per species at a given location per season. We further calculated 9 different species richness estimators, because the performance of estimators may vary in relation to sample size and coverage [38,39,40]. Following Chao et al. [41], to compare species richness between the dry and wet seasons, we estimated the overall number of shared species based on 4 estimators that account for possible undetected species in the compared samples.
We used the statistical software R V. 4.3.1 [42], and all analyses were performed in the package SpadeR [43]. We used the package iNEXT [41] to produce sample-based species diversity plots to visualize species diversity estimations differences between seasons, which combine the rarefaction/extrapolation and Hill numbers (species richness, Shannon diversity and Simpson diversity) frameworks, to measure and estimate species diversity, based on sample size and coverage [40,44].
We used a generalized linear model with a Poisson distribution function to evaluate differences in the photographic capture rate in relation to season and habitat characteristics. We used the photographic capture rate, i.e., the number of independent photographic events at each camera-trap station, as dependent variable, and season (as factor variable, with dry season = 0, wet season = 1), altitude (m), distance (m) to nearest main road (i.e., road accessible for cars), distance (m) to nearest hiking trail, distance (m) to nearest river, distance (m) to the nearest location where a fire event had occurred ≤2 years (as measure of human slash-and-burn activity), and habitat type according to Navarro & Ferreira [45] as explanatory variables. Six different habitat types were identified in the study area, (1) Mountain subhumid-humid kewinha forest, (2) Phreatophyte carob forest, (3) Mountain bush and grasslands, (4) Chira and Tipa Boliviano-Tucumano Forest, (5) Carapari and Soto inter-Andean semiarid forest, and (6) Valley Mara and Soto inter-Andean dry forest [45]. Habitat features were extracted from satellite imagery and available vegetation shape files of the area [45,46,47].
To evaluate seasonal differences in daily mammalian activity patterns, i.e., the number of observations of a given species within 24 h, we chose seven species based on their ecological role in the area and/or the presence of sympatric species, and a species’ conservation status. In combination with day and time stamps from remote images, these species provide a general measure of activity [48,49]. We chose (1) Geoffroy’s cat (Leopardus geoffroyi), a small-sized predator specialized on small mammals, including (2) the Bolivian chinchilla rat (Abrocoma boliviensis), a critically endangered species endemic to Bolivia; (3) the red brocket (Mazama sarae), listed as Data Deficient according to the IUCN Red List; and its main predator (4) the puma (Puma concolor), a generalist top predator and the largest felid in the area, listed as Least Concern. Two sympatric fox species, (5) the Pampa’s fox (Lycalopex gymnocercus), listed as Least Concern, and the (6) the Crab-eating fox (Cerdocyon thous), also listed as Least Concern; finally, (7) the Andean bear (Tremarctos ornatus), the largest carnivore in the study area listed as Vulnerable. To estimate daily seasonal and differences in mammalian activity patterns, we first compared the number and proportion of independent photographic events per hour during the dry and the wet season using a Fisher exact test. Then we plotted the daily activity patterns, as the percentage of these photographic independent events, within a 24-h period.

3. Results

We documented 29 medium- to large-sized mammals (Table 1), and it is noteworthy that we obtained records of 18 species outside their currently known global distribution range (IUCN, 2016). We registered 20 species during 6832 camera-trap days during the dry season, and 18 species during 6776 camera days during the wet season. Geoffroy’s cat was the most commonly species recorded (Table 1), representing 36.7% of the independent records for the dry season, and 24.5% for the wet season, and is present in ~40% of the study area. The red brocket, a data deficient species, and the Bolivian chinchilla rat, that is critically endangered, were also commonly recorded across seasons (Table 1). The red brocket exhibited a higher frequency of photographic records during the wet season, but in fewer camera-trap stations when compared with to the dry season. Both large carnivores were observed during both seasons, however, despite having fewer observations, pumas were recorder in more camera-trap stations compared to the Andean bear (Table 1). Records of the sympatric fox species varied greatly across seasons, and the Pampa’s fox was more often observed during the dry season and the crab-eating fox more often during the wet season (Table 1).
Estimations for species richness, i.e., the number of species observed, was slightly higher during the dry season (20.0 ± 1.8 to 29.9 ± 12.9 species) compared to the we season (18.0 ± 0.8 to 21.8 ± 6.2 species). However, estimators used for the analysis performed better with the data obtained for the wet season compared to the dry season (Table 2). Overall, the Homogeneous (MLE) and 1st order Jackknife estimators performed better for both seasons. Because the homogeneous model considers that all species have the same detection probability, we considered the 1st order Jackknife estimator to be the best estimator for our study [40]. In contrast, Chao and ACE estimators performed poorly with our data.
The estimates of the number of species shared between seasons (Table 3) ranged from 17.5 ± 1.3 to 25.8 ± 5.4, and we found no differences in species composition between seasons, as the similarity estimates ranged from 0.81 ± 0.07 to 0.99 ± 0.01 (Table 4). The similarity measures are all >80% and the sample-based species diversity plots display only small variations (Figure 2), suggesting that the species assemblage does not significantly change from one season to the other [40].
Photographic capture rates, as a measure of species richness and abundance, were higher during the dry season for most species. According to the regression results (Table 5), variation in photographic capture rate was significantly affected by season and habitat type, i.e., photographic capture rates were lower during the wet season, but higher in all other habitats compared to the mountain bush and grasslands. Further, the photographic capture rate increased with increasing altitude and increasing distance to hiking trails but decreased with increasing distance from main roads (Table 5). The rest of the predictors were not significant and removed from the model (Table 5).
When plotting hourly activity patterns for the seven selected species (Figure 3), these were active for ~4.37 more hours during the dry season compared to the wet season (Fisher exact test, p < 0.05). The crab-eating fox was the only species that was more active during the wet season and was captured only once during the dry season. The plots of Geoffroy’s cat and the Bolivian chinchilla rat suggest a strong temporal overlap (Figure 3); the Geoffroy’s cat spends over 80% of their daily activity during time periods when the Bolivian chinchilla rat was active (80.8% during wet season and 91.9% during the dry season, Figure 3). In contrast, puma and red brocket activity pattern did not temporally overlap (Figure 3). The two fox species showed statistically different activity patterns between seasons, i.e., the crab-eating fox (paired t-test, t = 3.19, p < 0.05) was more active during the wet season and the pampas fox (paired t-test, t = −5.41, p < 0.05) was more active during the dry season. The Bolivian chinchilla rat was also statistically more active during the dry season (t = −3.15, p < 0.05). The rest of the species did not exhibit statistical differences in activity between seasons (Figure 3).

4. Discussion

We evaluated the effects of seasonality on large and medium-sized mammalian species richness and abundance in an Andean dry forest in southern Bolivia. Our analysis indicated that there were no significant differences in overall species richness between the dry and the wet seasons. However, during the dry season, most species exhibited higher photographic capture rates, suggesting the existence of seasonal variations within the community (partial support prediction 1). In addition to seasonality, changes in species richness are influenced by habitat type, altitude, distance to hiking trails, and distance to main roads (no support prediction 2). The activity patterns of selected species suggest that medium- to large-sized mammals in the area generally spend more hours active during the dry compared to the wet season, except for the crab-eating fox (partial support prediction 3).
Our results show no significant differences in species richness between the wet and the dry season. Andrews and O’Brien [6] found that seasonality does affect mammal species richness, however, they also suggest that the effect of seasonal climatic characteristics (i.e., temperature and precipitation) is greater for smaller mammals. Therefore, body size, i.e., our focus on medium to large mammals, could explain the stability in the number of species found throughout the year in our study area. Palmer et al. [50] also found non-significant seasonal changes in RAI for large herbivores in western Africa. As for the estimators used in our analysis, the long sampling period in both seasons helped to reduce the heterogeneity of capture rates, which is likely the reason why the best estimator for our data was the 1st order Jackknife, because this estimator can handle some levels of heterogeneity [20]. Chao and Chao1 estimators performed poorly, likely because our sample sizes were either too small or the differences in the capture rates of rare species were too large [40]. ACE estimators do not work well either, likely because these require more heterogenic samples [40]. Therefore, we propose to follow Tobler et al. [20] suggestion for achieving cost-effective results, by focusing on achieving a minimum number of sampling days rather than covering a bigger area, unless habitats are too different.
Overall, during dry season we obtained a greater number of photographic records, and for longer periods of time during the day. Because camera-trap locations were often visited and maintained, we do not consider species detection disruptors, such as vegetation growth, to influence our results. Therefore, the significant effect of season on the photographic capture rate can be interpreted as a response to resources availability. As the study area shows a marked difference in precipitation levels and temperature range between seasons [23], hence in resource availability, our results suggest that terrestrial mammals spend more time or expand their activity range in search for food and water during the dry season, hence the higher photographic capture rates. Moreover, Brown [11] suggests that variations in species richness in mountainous areas are influenced by more than a single factor, such as precipitation, habitat heterogeneity, or altitude. We found this to be true for our study site, as habitat and altitude, along with season, are the variables that best explained the photographic capture rate. This is in accordance with results by Butet et al. [8] and Carmignotto et al. [51] who found that season and habitat were the best explanatory variables for differences in their capture rates for small mammals in western France and the Brazilian Cerrado, respectively. Similarly, Palmer et al. [50] found that habitat type did influence their results, which the authors attributed to species preferences and life history traits, such as migratory behavior. Based on our regression results, it appears that the Mara and Soto inter-Andean dry forests, Carapari and Soto inter-Andean semiarid forests, and the phreatophyte carob forest have a higher likelihood of yielding higher photographic records of mammals. These three habitats are all located close to the Pilaya River (less than 5 km distance), which is the primary water body in our study area. However, it is important to note that the variable “distance to river” showed no significant effect in our analysis. Distance to fire events, one of the variables measuring human presence, did not yield statistically significant results either. These findings partially contrast with those from Feng et al. [52], who reported that that human presence variables had a greater influence on mammal species richness in northeastern China compared to other environmental variables. In our case, environmental variables, such as habitat, altitude, and season have a stronger effect compared to variables estimating human presence, such as distance to roads and hiking trails.
Seasonal variations in daily activity patterns are especially pronounced in regions where mammals need to abrupt changes in temperature and daylight [53,54]. However, our study aligns with research in tropical regions, which also reveals seasonally shifting activity patterns in mammals. Marques & Fabian [55] discovered that mammals in South Brazil adjust their activity periods according to season, avoiding colder temperatures and making the most of available daylight. Our findings indicate that mammals in our study area exhibit longer activity periods during the dry season, in line with the observations of Blake et al. [56], who noted reduced activity during wetter periods in certain species. We emphasize the significantly different seasonal activity pattern for the Bolivian chinchilla rat, an essential discovery on the scientific knowledge for a critically endangered and poorly known species. The case of the sympatric crab-eating fox and the pampa’s fox exemplifies avoidance strategies, mirroring the results of Neiswenter et al. [57], who found that sympatric skunk species in Texas adapt their activity patterns based on the other species’ activities. Additionally, Di Bitetti et al. [58] documented similar adjustments in temporal activity patters of the same two fox species, facilitating their coexistence.
Species inventories are indispensable to obtain species richness and abundance estimations [20]. In Bolivia, such inventories have been largely focused on the Amazon ecosystem and to national parks. These biased research efforts have ignored other sites, such as mountain ecosystems. This study is the first long-term camera trapping study in the highly fragmented, and critically endangered Andean dry forest. Records of the vulnerable Andean bear as well as the critically endangered Bolivian chinchilla rat, along with 18 new global distributions are just an example of the hidden richness of this highly neglected ecosystem. When comparing our research results to the modeled distributions by Wallace et al. [31] for Bolivian mammals, we found that 21 species were observed outside. Notably, we recorded 15 carnivore species, 8 of which represent new records for the region. Our results suggest that the large and medium size mammalian communities inhabiting the Andean dry forests are highly diverse, as our results yielded comparable number of species to those registered for Amazonian forests [20], considered among the most diverse worldwide.
Although there is a noticeable contrast in the climatic conditions between wet and dry season, the changes in the mammal community observed in the study are subtle. These changes appear to occur due to adjustments in daily activity patterns in response to the season. Furthermore, other environmental factors contribute to the higher rates of mammal recordings during the dry season. This suggests a cumulative impact of various factors influencing the results beyond mere seasonality. Our findings suggesting adjustments of global species distribution ranges highlight the importance of further biodiversity monitoring in dry forest habitats, especially the responses to anthropogenic changes in the environment.

Author Contributions

Study design: X.V.-L. and C.J.Q.-P.; data collection: X.V.-L. and C.J.Q.-P.; data analysis: C.J.Q.-P.; writing: X.V.-L., A.Z. and C.J.Q.-P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Chester Zoo, Whitley Fund for Nature, and Darwin Initiative.

Data Availability Statement

Data are available on request.

Acknowledgments

This project, within “The Great Bear Landscape” program, is funded by Chester Zoo, Whitley Fund for Nature, and Darwin Initiative. We appreciate the invaluable collaboration of WildCRU, PROMETA and Museo “Alcide d’Orbigny”. Thanks to Kenny Ure and Mauricio Penaranda for their invaluable support in the data collection and processing. A special knowledge to the dedicated efforts of the parabiologists who ensured the success of the study in the field.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of the study area for a survey of medium and large-sized mammals in San Lorenzo, Tarija Department, southern Bolivia, 2018–2019. The dry forest in the area is comprised of 7 different habitat types (see legend). Locations of 56 remote camera sites are represented by black diamonds.
Figure 1. Map of the study area for a survey of medium and large-sized mammals in San Lorenzo, Tarija Department, southern Bolivia, 2018–2019. The dry forest in the area is comprised of 7 different habitat types (see legend). Locations of 56 remote camera sites are represented by black diamonds.
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Figure 2. Comparison of sample-based species diversity estimation plots of medium- and large-sized mammals in wet and dry seasons in San Lorenzo, Tarija Department, southern Bolivia, 2018–2019. Solid lines represent rarefactions, and the dashed ones represent extrapolations of mammal diversity for Hill numbers: (a) Species richness (q = 0), (b) Shannon diversity (q = 1) and (c) Simpson diversity (q = 2).
Figure 2. Comparison of sample-based species diversity estimation plots of medium- and large-sized mammals in wet and dry seasons in San Lorenzo, Tarija Department, southern Bolivia, 2018–2019. Solid lines represent rarefactions, and the dashed ones represent extrapolations of mammal diversity for Hill numbers: (a) Species richness (q = 0), (b) Shannon diversity (q = 1) and (c) Simpson diversity (q = 2).
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Figure 3. Daily activity patterns of selected mammal species in San Lorenzo, Tarija Department, southern Bolivia, 2018–2019. Activity is represented as hourly capture rate percentage plots, one per season (wet and dry) for (a) Geoffroy’s cat (Leopardus geoffroyi), (b) Bolivian chinchilla rat (Abrocoma boliviensis), (c) red brocket (Mazama sarae), (d) puma (Puma concolor), (e) Pampas fox (Lycalopex gymnocercus), (f) crab-eating fox (Cerdocyon thous) and (g) Andean bear (Tremarctos ornatus).
Figure 3. Daily activity patterns of selected mammal species in San Lorenzo, Tarija Department, southern Bolivia, 2018–2019. Activity is represented as hourly capture rate percentage plots, one per season (wet and dry) for (a) Geoffroy’s cat (Leopardus geoffroyi), (b) Bolivian chinchilla rat (Abrocoma boliviensis), (c) red brocket (Mazama sarae), (d) puma (Puma concolor), (e) Pampas fox (Lycalopex gymnocercus), (f) crab-eating fox (Cerdocyon thous) and (g) Andean bear (Tremarctos ornatus).
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Table 1. List of medium to large mammals captured during a remote camera survey in the San Lorenzo area, in the Boliviano-Tucmano Ecoregion, Bolivia, 2018–2019. The camera survey was conducted during dry (May-September) and wet season (October-April). Relative Abundance Index (RAI) is the number of photographic events divided by the number of camera-trap days and multiplied by 100. Naïve occupancy is the proportion of camera-trap stations where the species was present. IUCN category describes a species conservation status.
Table 1. List of medium to large mammals captured during a remote camera survey in the San Lorenzo area, in the Boliviano-Tucmano Ecoregion, Bolivia, 2018–2019. The camera survey was conducted during dry (May-September) and wet season (October-April). Relative Abundance Index (RAI) is the number of photographic events divided by the number of camera-trap days and multiplied by 100. Naïve occupancy is the proportion of camera-trap stations where the species was present. IUCN category describes a species conservation status.
Dry SeasonWet Season
Taxonomic FamilyScientific NameCommon NameIndependent Records **Stations with Records ***RAINaïve OccupancyIndependent Records **Stations with Records ***RAINaïve OccupancyIUCN Category
DidelphidaeDidelphis albiventris ±White-eared opossum22 (3.16%)5 (3.16%)0.320.8913 (2.50%)5 (8.93%)0.190.89LC
Lutreolina massoia * ±Massoia’s lutrine opossum0 (0%)0 (0%)--0 (0%)0 (0%)--LC
DasypodidaeDasypus novemcintusNine-banded armadillo0 (0%)0 (0%)--0 (0%)0 (0%)--LC
MyrmecophagidaeTamandua tetradactyla ±Lesser tamandua27 (3.87%)6 (10.71%)0.400.1114 (2.70%)5 (8.93%)0.210.09LC
CanidaeCerdocyon thous ±Crab-eating fox1 (0.14%)1 (1.78%)0.010.0212 (2.31%)4 (7.14%)0.180.07LC
Lycalopex gymnocercus ±Pampas fox98 (14.06%)6 (10.71%)1.430.119 (1.73%)3 (5.36%)0.130.05LC
FelidaePuma concolor ±Puma20 (2.87%)12 (21.43%)0.290.2118 (3.46%)10 (17.86%)0.270.18LC
Herpailurus yagouaroundi ±Jaguarundi1 (0.4%)1 (1.78%)0.010.020 (0%)0 (0%)00LC
Leopardus garleppi * Pampas cat0 (0%)0 (0%)--0 (0%)0 (0%)--NT
Leopardus pardalisOcelot0 (0%)0 (0%)001 (0.19%)1 (1.78%)0.010.018LC
Leopardus wiedii ±Margay2 (0.29%)2 (3.57%)0.030.032 (0.38%)1 (1.78%)0.030.02NT
Leopardus geoffroyiGeoffroy’s cat186 (36.68%)24 (42.86%)2.720.43127 (24.47%)22 (39.28%)1.870.40LC
Leopardus tigrinusNorthern tiger cat8 (1.15%)2 (3.57%)0.120.040 (0%)0 (0%)00VU
MustelidaeEira barbaraTayra52 (7.46%)20 (35.71%)0.760.1147 (9.05%)13 (23.21%)0.690.23LC
Galictis cujaLesser grison8 (1.15%)7 (12.5%)0.120.1216 (3.08%)3 (5.36%)0.240.05LC
MephitidaeConepatus chingaMolina’s hog-nosed skunk99 (14.20%)15 (26.78%)1.450.2764 (12.33%)12 (21.43%)0.940.21LC
ProcyonidaeNasua nasua ±Coati1 (0.14%)1 (1.78%)0.010.024 (0.77%)3 (5.36%)0.060.05LC
Procyon cancrivorus ±Crab-eating racoon3 (0.43%)2 (3.57%)0.040.031 (0.19%)1 (1.78%)0.010.02LC
UrsidaeTremarctos ornatus ±Andean bear34 (4.88%)10 (17.86%)0.500.1832 (6.16%)6 (10.71%)0.470.11VU
TayassuidePecari tajacu ±Collared peccary19 (2.72%)5 (8.93%)0.280.0943 (8.28%)8 (14.28%)0.630.14LC
CervidaeMazama sarae ±Red brocket53 (7.60%)19 (33.93%)0.770.3475 (14.45%)14 (28%)1.110.25DD
Mazama gouazoibira ±Grey brocket47 (6.74%)8 (14.28%)0.690.1430 (5.78%)7 (12.5%)0.440.12LC
SciuridaeNotosciurus pucheranii * Squirrel0 (0%)0 (0%)--0 (0%)0 (0%)--LC
ErethizontidaeCoendou prehensilis * ±Brazilian porcupine0 (0%)0 (0%)--0 (0%)0 (0%)--LC
DasyproctidaeDasyprocta azarae * ±Agouti0 (0%)0 (0%)--0 (0%)0 (0%)--DD
CaviidaeHydrochoerus hidrochaeris * Capybara0 (0%)0 (0%)--0 (0%)0 (0%)--LC
Galea sp. **Cavy0 (0%)0 (0%)--0 (0%)0 (0%)---
LeporidaeLepus europeaus ±European hare1 (0.14%)1 (1.78%)0.010.020 (0%)0 (0%)00LC
AbrocomidaeAbrocoma boliviensis ±Bolivian chinchilla rat15 (2.15%)8 (14.28%)0.160.1411 (2.12%)5 (8.93%)0.220.09CR
* Species that are not included in the analysis because they were captured outside the sampling period. ** Independent records and percentage of the total of records during the sampling period. *** Number of camera trap stations where the species was recorded. ± Species with photographic records outside their known distribution change, according to IUCN (2016). In the case of puma, is considered as “possibly extant” for the area.
Table 2. Estimates of mammalian species richness using 9 different estimators during the dry and wet season in an Andean dry forest in the San Lorenzo area, southern Bolivia, 2018–2019. Overall, 29 medium- to large-sized mammals were recorded during the study period. The best estimations are highlighted with *.
Table 2. Estimates of mammalian species richness using 9 different estimators during the dry and wet season in an Andean dry forest in the San Lorenzo area, southern Bolivia, 2018–2019. Overall, 29 medium- to large-sized mammals were recorded during the study period. The best estimations are highlighted with *.
EstimatorDry SeasonWet Season
EstimateSEEstimateSE
Homogeneous Model21.521.7718.671.09
Homogeneous (MLE)20.001.04418.000.77
Chao127.9911.6519.993.74
Chao1-bc22.994.1820.463.49
iChao128.8611.6521.746.18
ACE25.836.3020.263.49
ACE-129.9312.9221.756.18
1st order Jackknife23.99 *2.8219.99 *1.99
2nd order Jackknife26.994.8920.993.45
Table 3. Estimates of mammalian species observed in both the dry and wet season, using 4 types of estimators, in an Andean dry forest, San Lorenzo, southern Bolivia, 2018–2019. The best estimation is highlighted with *.
Table 3. Estimates of mammalian species observed in both the dry and wet season, using 4 types of estimators, in an Andean dry forest, San Lorenzo, southern Bolivia, 2018–2019. The best estimation is highlighted with *.
EstimatorEstimateS.E.
Homogenous22.252.46
Heterogenous (ace-shared)25.80 *5.36
Chaoi1-shared19.504.36
Chaoi2-shared17.501.30
Table 4. Estimates of medium to large-sized mammal community species composition similarity, using 8 empirical measures, between dry and wet season in an Andean dry forest in the San Lorenzo area, southern Bolivia, 2018–2019. The best estimations are highlighted with *.
Table 4. Estimates of medium to large-sized mammal community species composition similarity, using 8 empirical measures, between dry and wet season in an Andean dry forest in the San Lorenzo area, southern Bolivia, 2018–2019. The best estimations are highlighted with *.
IndexEstimateS.E.
Classic Sørensen 0.890.05
Classic Jaccard0.810.07
Horn0.92 *0.01
Morisita-Horn0.90 *0.02
Regional overlap0.95 *0.01
Chao-Sørensen0.990.01
Chao-Jaccard0.980.01
Horn size-weighted0.920.01
Table 5. Results of a regression models evaluating the factors affecting photographic capture rate of mammalian species in an Andean dry forest in the San Lorenzo area, southern Bolivia, 2018–2019. The explanatory variables are season (wet and dry), altitude (m), distance (m) to main road, distance (m) to nearest hiking trail, and habitat type [45].
Table 5. Results of a regression models evaluating the factors affecting photographic capture rate of mammalian species in an Andean dry forest in the San Lorenzo area, southern Bolivia, 2018–2019. The explanatory variables are season (wet and dry), altitude (m), distance (m) to main road, distance (m) to nearest hiking trail, and habitat type [45].
Variable EstimateS.E.zp
Season (dry)000
Season (wet)−0.29060.058−5.007<0.005
Habitat 0000
Habitat 10.6180.1645.304<0.005
Habitat 21.4130.13810.250<0.005
Habitat 32.1550.13615.797<0.005
Habitat 40.5350.0945.648<0.005
Habitat 50.8380.1565.374<0.005
Altitude0.00050.00013.353<0.005
Distance to main road−0.000030.00001−2.5150.011
Distance to hiking trail0.00010.000062.1150.034
Habitat 0: Mountain bush and grasslands; Habitat 1: Chira and Tipa Boliviano-Tucumano forest; Habitat 2: Carapari and Soto inter-Andean Forest; Habitat 3: Mara and Soto inter-Andean dry forest; Habitat 4: Mountain subhumid-humid kewinha forest; Habitat 5: Phreatophyte carob forest.
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Quiroga-Pacheco, C.J.; Velez-Liendo, X.; Zedrosser, A. Effects of Seasonality on the Large and Medium-Sized Mammal Community in Mountain Dry Forests. Diversity 2024, 16, 409. https://doi.org/10.3390/d16070409

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Quiroga-Pacheco CJ, Velez-Liendo X, Zedrosser A. Effects of Seasonality on the Large and Medium-Sized Mammal Community in Mountain Dry Forests. Diversity. 2024; 16(7):409. https://doi.org/10.3390/d16070409

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Quiroga-Pacheco, Carmen Julia, Ximena Velez-Liendo, and Andreas Zedrosser. 2024. "Effects of Seasonality on the Large and Medium-Sized Mammal Community in Mountain Dry Forests" Diversity 16, no. 7: 409. https://doi.org/10.3390/d16070409

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