Consistent daily activity patterns across tropical forest mammal communities

doi:10.21203/rs.3.rs-1330544/v1

Download PDF

Article

Consistent daily activity patterns across tropical forest mammal communities

https://doi.org/10.21203/rs.3.rs-1330544/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 19 Nov, 2022

Read the published version in Nature Communications →

Version 1

posted

You are reading this latest preprint version

Most animals follow distinct daily activity patterns reflecting their adaptations1, requirements, and interactions2-4. Specific communities provide specific opportunities and constraints to their members that further shape these patterns3,4. Here, we ask whether community-level diel activity patterns among long-separated biogeographic regions differ or converge and whether the resulting patterns indicate top-down (predation risk) or bottom-up processes (prey availability)? We estimated the diel activity of ground-dwelling and scansorial mammals in 16 protected areas across the tropics, using an extensive network of camera traps, and examined the relationship to body mass and trophic guild. We found that mammalian guilds exhibited consistent diel activity patterns across regions, indicating similar responses to similar evolutionary and ecological opportunities and constraints. Larger herbivores tended to be more nocturnal than smaller herbivores, whereas carnivores and omnivores showed the opposite pattern. Insectivores were exceptions, revealing regional differences in which larger insectivorous species were more nocturnal than smaller ones in the Afrotropical and Indo-Malayan regions, while the pattern reversed in the Neotropics. The consistent contrast between predators and prey suggests that diel activity within these communities is primarily determined by large predators and associated risk of predation.

Diel activity patterns—the distribution of activity throughout the daily cycle—are fundamental in animal ecology⁵. These patterns reflect when organisms seek food, socialize, and perform other necessary tasks while also accounting for risks^1,2. Activity patterns vary among species. Some organisms may maintain activity over extended periods while others exhibit brief peaks⁶. They may be predominantly active during the night (nocturnal), day (diurnal), twilight (crepuscular), or may lack pronounced peaks with relation to day and night (cathemeral). Furthermore, there can be substantial variation within species and between populations⁶. Mammals illustrate a broad range of such behaviours.

While mammals today occupy all temporal niches (day, night, twilight), early mammal species are thought to have been primarily nocturnal to avoid the predation risk imposed by diurnal dinosaurs—an idea known as the “nocturnal bottleneck” hypothesis⁷. Following the extinction of the non-avian dinosaurs (66 Ma)⁸, mammals diversified and adapted to fill the available temporal niches^7,9. Physiologic, morphological, and behavioural adaptations⁹ including endothermy, eye forms¹⁰, and enhanced sensorial systems allowed mammals to thrive under the different illumination and temperatures associated with day and night.

Endothermy permits mammals to exploit multiple temporal niches^11,12. Nonetheless, species-specific physiological characteristics, in interaction with morphology (e.g., body size), may still favour activity schedules that moderate thermal stress¹³. For instance, in the absence of other factors, large species in warm regions may be forced to avoid overheating by avoiding activity in the hottest periods¹⁴. By contrast, small species that can lose heat rapidly may avoid cold and focus activity in warmer periods^15,16. Small mammals such as mice and rats avoid diurnal predation by favouring nocturnal activity but may nonetheless be active during the daytime due to food scarcity, low nighttime temperatures, or low risks from diurnal predation^1,17.

Species interactions may influence and control diel activity patterns within communities^3,4. For instance, predators may favour periods where their prey are active, whereas prey species may avoid periods when their predators are active^5,18. Potentially, this can involve both top-down or bottom-up processes^19–21. Bottom-up and top-down are key classifiers for the regulation of food web dynamics^19–21 and have the potential to influence how species within an assemblage may behave²². In a top-down process, the temporal activities of certain species (e.g., prey) seek to avoid the time use of others (e.g., predators)²³. For example, small carnivores may avoid activity in periods when they are more likely to encounter larger predators, with similar avoidance expected for prey species to avoid their predators^18,23. Alternatively, this can be a bottom-up process in which predator species match their activities to that of their prey or competitors²². For instance, mesopredators in south-western Europe were found to match their activity to that of their prey²⁴. There is evidence for both bottom-up and top-down determination of activity patterns in a few sympatric species^22–24. Yet, we do not know the degree to which bottom-up and top-down processes operate in nature and whether the resulting patterns are consistent across regions.

Humid tropical forests provide a useful context for exploring these questions as the influence of seasonality is low and similar environmental conditions are found in biogeographically distinct regions¹³. These forests encompass many of the most diverse and rich terrestrial biomes on earth and the maintenance of such diversity likely involves biotic interactions²⁵. Trophic composition of tropical forest mammal communities appears relatively consistent across tropical regions²⁶ and has been attributed to convergent evolution, likely due to similarities in environment and adaptations across distant forests²⁷. We expect that the processes that shape trophic interactions and composition may also influence diel activity patterns.

We studied the daily activity patterns of ground-dwelling and scansorial mammals inhabiting protected tropical forests across the Neotropics, Afrotropics, and Indo-Malayan tropics (Fig. 1). We used time-stamped images from standardized large-scale camera-trap surveys implemented by the Tropical Ecology Assessment and Monitoring (TEAM) Network in 16 protected areas (Table S1)²⁸. Using multinomial analysis, we investigated how diurnal, nocturnal, and crepuscular activity was related to trophic guild and body size and whether any such patterns were consistent among regions.

We tested three hypotheses (Fig. 2). First, if top-down processes regulate the diel activity of animals in a community (H1), we predicted (1a) that prey species (e.g., herbivores) should exhibit diel activity patterns that avoid those of predators (e.g., carnivores and omnivores) of a similar size (interguild avoidance), and (1b) smaller members of a trophic guild (especially carnivores and omnivores) should exhibit diel activity patterns that avoid that of larger members of the same guild (intraguild avoidance). If bottom-up processes regulate the diel activity of animals in a community (H2), then (2) diel activity patterns of predators should match that of prey species (herbivores, insectivores, and small omnivores). Finally, if the energetic cost of thermoregulation constrains diel activity of tropical mammals (H3), then (3) large mammals should be more active during the night when it is colder and small mammals more active during the day when it is warmer.

We extracted the probability for the activity (0–1) during day, night, and twilight, and the correspondent upper (UCI) and lower (LCI) 95% confidence intervals for the given range of body mass and trophic guild derived from the multinomial model in every region with the lowest AIC. Diel activity was best modelled when including body mass, trophic guild, and their interaction for the three regions (Fig. 3, Table S2).

Fig. 3. Distribution of daily activity in relation to body size and trophic guilds of tropical ground-dwelling and scansorial mammals in three regions. Estimates correspond to the probability of activity during the day, night, and twilight extracted from the model fitted to TEAM camera-trap data. Tick marks above the x-axis indicate the typical body mass of species analysed. Colour hue indicates where the model interpolates among observations of the sizes presented (darker) versus extrapolates beyond values in the data for that trophic guild and region (lighter).

We found consistent patterns of diel activity in relation to trophic guild and body mass across regions (Fig. 3, Fig. S1, Table S2) indicative of top-down processes playing a dominant role in shaping community activity patterns (H1). Following our prediction 1a, the interguild relationships between nocturnality and body mass showed contrasting patterns for predators (i.e., carnivores, omnivores) and prey (i.e., herbivores, and perhaps insectivores) indicating avoidance of predators by prey across regions. In general, larger prey species were nocturnal whereas larger predators were diurnal (Fig. 3). For example, In the Neotropics, the highest diurnal probability for large predators was 0.64 (LCI:0.63, UCI:0.74, body mass = 96 kg). Herbivores (i.e., prey) were more likely to exhibit a high nocturnal activity as the body mass increased to a maximum probability of 0.60 (LCI:0.48, UCI:0.71, body mass = 210 kg).

Among carnivores, we found a negative relationship between body mass and nocturnality supporting prediction 1b. Thus, small carnivores, which risk predation by larger carnivores²⁹, were more likely to be nocturnal than larger carnivores. For example, carnivores in the Afrotropics decreased nocturnality probability from 0.81 (LCI: 0.74, UCI:0.87, body mass = 1 kg) to 0.21 (LCI: 0.14, UCI: 0.28, body mass = 61 kg) as size increased. Such temporal partitioning has previously been identified as a strategy for mitigating intraguild predation among carnivores, thus aiding their coexistence^{3,4,18,22,29−31}. Finally, our analyses indicate that among both herbivores and insectivores, smaller species were more likely to be diurnal than larger species which we suggest is likely a consequence of avoiding small and medium-sized predators.

The high degree of diurnality among large carnivores evident in our study sites contrasts with reports from other forests, as in Madagascar and North America where carnivores were largely active at night^32,33. These previous studies focused on more anthropogenic landscapes, where carnivores appear to avoid interacting with humans by becoming more nocturnal^32–34. Our sites are within protected areas and therefore suffer lower human impacts than elsewhere and may permit greater diurnality.

While top-down processes appear to shape overall activity patterns within each community, notable variation among species persists, even within the same trophic guild and for comparable body sizes (Fig. S4). Species-specific diel activity patterns likely arise from a combination of bottom-up and top-down processes, and other influences (e.g., habitat features, environmental conditions, intra-specific dynamics, etc.). Furthermore, some patterns cannot be attributed unambiguously to one process or factor, for example, the nocturnal activity of small omnivores may reflect avoidance to top predators (top-down) and/or following of omnivore prey (bottom-up, Prediction 2, Fig. 2, Fig. 3). Both explanations have merits when we consider better-known species such as the ocelot (Leopardus pardalis), a neotropical felid, which is known to prey on various species including nocturnal omnivores such as opossums and racoons³⁵, and is also known to avoid jaguars. Although bottom-up regulation can influence the abundance of species³⁶, we did not find further evidence for this process in the activity of other trophic groups.

Larger-bodied herbivores and insectivores tended to be more nocturnal consistent with the thermoregulatory constraint hypothesis (H3). For example, for Afrotropical herbivores, nocturnality probability increased from 0.09 (LCI: 0.06, UCI: 0.11, body mass = 0.70 kg) to 0.60 (LCI: 0.51, UCI: 0.69, body mass = 4334 kg) as the body mass increased (Fig. 3). Similarly, the probability of being nocturnal among insectivores in the Indo-Malayan increased with body mass from 0.01 to 0.98 (Fig. 3). While daily temperature is more stable in tropical rainforests than in many other ecosystems, it does vary³⁷. Most tropical mammals are adapted to survive in a narrow thermal tolerance range^38,39, thus both high and low temperatures can increase energy expenditure⁴⁰. Small-bodied species can reduce energy loss by being active during warmer periods of the day¹⁵, while large-bodied animals (e.g., tapirs⁴¹, aardvark⁴²) can reduce thermal stress by focusing activity during cooler periods of the day^14,41,43. For example, in the Neotropics the probability of being active during the night was two times higher for a 290 kg herbivore (e.g., Tapirus bairdii) than for one of 1 kg (e.g., Myopracta acouchy). In contrast, we found a positive relationship between size and diurnality for carnivores, omnivores and neotropical insectivores. If thermoregulatory constraints were sufficiently powerful, we might anticipate it to manifest across all trophic guilds. Perhaps this was not apparent because interactions may be more influential than other factors (eg., physiology) in tropical forests compared to other biomes²⁵ due to more stable climatic conditions. Megafaunal species were also scarce among non-herbivores and thus thermal stress may be less influential.

Although all our study areas are relatively well-protected none are completely free of human impacts²⁸ raising the question of how this may influence the observed patterns. Clearly, human presence influences animal activity patterns too; for example, some species have become more nocturnal to avoid hunters⁴⁴. This was recognised in one of our study sites, where ungulates became more nocturnal as hunting increased⁴⁵. In this context, it is remarkable that the general patterns were so robust and remained consistent across sites despite variation in hunting pressure. We acknowledge the inability of our study to clarify the role of large carnivores and hunters in determining the specific details of the patterns reported. However, simple approaches using human activity may be misleading as evasive responses among mammals are not universal and can change over time (for example, the gorillas in Bwindi have been habituated to humans), and in some locations, animals favour human settlements to access certain foods or avoid predation. At some of our sites, certain large predators (e.g., leopards in Biwindi⁴⁶) are now absent due to earlier extinctions and more recent losses^47,48. This, however, does not necessarily mean release from diurnal risks and disturbance from omnivorous mammals (e.g., chimpanzees), birds of prey, reptiles (e.g., pythons, anacondas), and humans (tourists and hunters). Furthermore, current activity patterns may reflect the anachronistic top-down regulation by “ghosts of predators past”. Further work is needed to explore these nuances. To ensure we are not misunderstood, we underline that the robust and consistent patterns we observed in these comparatively well protected forest communities do not contradict past work indicating that widespread species decline and loss can have a devastating impact on ecosystems^49–51.

The odd-one-out: Neotropical insectivores

Insectivores were an exception to the consistent patterns across regions: while Afrotropical and Indo-Malayan species revealed a positive relationship between greater body mass and the likelihood of nocturnal activity (e.g., Afrotropical increased from 0.01 to 0.91), a negative relation was found in the Neotropics with a decrease of nocturnality with greater body mass, from a probability of 0.99 (LCI: 0.99, UCI: 0.99, body mass = 0.12 kg) to 0.32 (LCI: 0.22, UCI: 0.44, body mass = 43.30 kg). We do not know the cause for this exception but can speculate. The pattern reported for insectivores in Afrotropical and Indo-Malaya regions is consistent with the thermoregulatory constraints hypothesis (H3). However, the higher diurnality of large insectivore species than small ones in the Neotropics, was mostly driven by three species (Myrmecophaga tridactyla, Tamandua tetradactyla, and Tamandua mexicana) which may derive from the distinct biogeographic history of the Neotropics, where insectivores are among the few native lineages that persisted after the great interchange⁵². In any case, the difference may reflect different characteristic requirements (e.g., African aardvarks dig burrows, whereas neotropical anteaters live above ground).

Despite their distinct origins, biogeographic histories, and taxonomic compositions, community level diel activity patterns for tropical forest mammals, examined by trophic guild and body size, are remarkably consistent across 16 sites and three tropical regions. As shown previously for trophic structures⁴⁷, diel activity patterns appear shaped by common processes regardless of biogeography. Convergent evolution across regions appears manifested in many ways including, as we see here for the first time, diel activity strategies. These community-level activity patterns appear shaped primarily by larger predators through top-down processes

1) Study areas and camera trapping

We used camera-trap data from the Tropical Ecology Assessment and Monitoring (TEAM) Network⁴⁷. TEAM data comprise data from three tropical biogeographic regions (Neotropics, Afrotropics and Indo-Malayan tropics) and 16 protected areas (TEAM Network, 2011) (Fig. 1). Camera-traps were deployed following a standardized protocol through all protected areas during the dry season between 2008 and 2017. At each protected area the monitoring run from two to ten years with the deployment of 60 to 90 cameras. Camera-traps were placed at a density of 0.5 - 1 camera/km² (1 camera every km² or 1 camera every 2 km²) and remained active for ~30 consecutive days^28,47. We excluded data from camera-trap sites with inconsistent date-time stamps, yielding a total of 60-89 cameras per protected area (Fig. 1, Table S1).

2) Data

A total of 2 312 635 camera-trap pictures corresponded to mammals. We further filtered the dataset to delimitate our study for species with a body mass greater than 75 g (smaller species have high uncertainty of identification and are difficult to detect) and species strictly terrestrial or scansorial (i.e., we excluded all arboreal and aquatic species)^26,53. A total of 166 species, 38 families, and 15 orders of ground-dwelling and scansorial species were detected (Table S1). Since camera-traps usually take consecutive pictures, we avoided pseudo-replication of individuals by establishing independent events (time interval between pictures > 1-hour per camera for a given species). This resulted in a total of 126 382 independent events (Supplementary Material 2). To analyse diel activity, we used the time-stamp recorded in each independent event⁵⁴ and summarized the number of events for each of the following three categories 1) day, 2) twilight, or 3) night. Each event was classified by protected area, location, time, and date to specify the sunrise, sunset, nautical dawn, and dusk using the R library ‘maptools’⁵⁵. Twilight was defined as the interval between dawn and sunrise and between sunset and “nautical dusk”⁵⁶. Day was defined as the interval between sunrise and sunset. Night was the interval between nautical dusk and nautical dawn.

As species characteristics we used 1) trophic guild and 2) body mass(g) which we extracted from the PHYLACINE database⁵⁷ (Fig. S2). We classified each mammal species into four trophic guilds: carnivore, herbivore, insectivore, or omnivore. Categories were based on diet reported in the PHYLACINE database and we classified as carnivore species feeding on ≥ 80% vertebrates, herbivore species feeding on ≥ 80 % plant materials, insectivore feeding on ≥ 80 % insects, the remaining species were categorized as omnivores (e.g., feeding on vertebrates and fruits)^57,58.

3) Analysis

To test how trophic guild (carnivores, herbivorous, insectivores, and omnivores) and body mass (log-transformed) is associated with the number of independent events of each diel activity (day, night, twilight) of tropical ground-dwelling and scansorial mammals we fitted a multinomial logit model⁵⁹ using package ‘mclogit’⁶⁰. Multinomial modelling allowed us to assess three instead of two response classes (day, night, and twilight). We built a set of candidate models for each tropical region using maximum likelihood (ML) and with a convergence tolerance (Ɛ) of 1e-6 (Table S1). To account for the variability between the activity of species in different protected areas we include protected areas as a random effect within all models. We selected the best model for each tropical region using Akaike information criterion (AIC). We ranked models using ΔAIC and considered models with a ΔAIC <2 to equally be supported. Once we selected the best models, we run the models with a restricted maximum likelihood (REML) to arrive at final estimates for each tropical region. We predicted relative activity with the package ‘mpred’⁶⁰. This allowed us to extract the predicted probability of activity in each diel category for the range of body mass in each trophic guild and region.

To show the diversity of activity patterns we characterized species-specific activity patterns when the number of independent events was 25 or more⁶¹. We gathered the data of all protected areas in each biogeographic region to display species activity patterns (Fig. 1, Fig. S3). To correct for diel differences on the delimitation of day, night and twilights between protected areas and distinct dates of the year of sampling we anchored activity patterns to sunrise and sunset⁶² using the ‘activity’ package⁶³ (Fig. S3). Then we plotted species activity with the package ‘overlap’, which employs kernel density estimation that circumvents the conflation of data required for histograms⁶¹.

Acknowledgment

We thank the funding by Research Council of Norway (project NFR301075). This work was made possible by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Smithsonian Tropical Research Institute and the Wildlife Conservation Society. We acknowledge the effort of all TEAM site managers and collaborators who helped collecting data as well as Wildlife Insight for the data process and availability, Emmanuel Akampurira, and David Kenfack. Finally, we thank John Megahan for species illustrations of Fig. 1.

Author contribution statements

DS and RB proposed the study and accessed funding. A.F.V-V., R.B. and D.S. developed the approach and hypotheses presented here. A.F.V-V. developed and performed the analyses. R.B. verified the analysis. A.F.V-V. wrote the manuscript with support from R.B., D.S., A.S-P., and L.B. The authors D.S. J.A., R.Bitariho., S.E., V.E., P.A.J., C.K., E.H.M., M.G.M.L, B.M., F.R., J.S., F.S., W.R.S., and E.U. were responsible of the camera trap data collection in the TEAM sites. A.F.V-V. R.B and D.S. finalized the manuscript with input and approval from all authors.

Hut, R. A., Kronfeld-Schor, N., van der Vinne, V. & De la Iglesia, H. In search of a temporal niche: environmental factors. Progress in brain research 199, 281–304 (2012).
Cox, D., Gardner, A. & Gaston, K. Diel niche variation in mammals associated with expanded trait space. Nature Communications 12, 1–10 (2021).
Schoener, T. W. Resource partitioning in ecological communities. Science 185, 27–39 (1974).
Richards, S. A. Temporal partitioning and aggression among foragers: modeling the effects of stochasticity and individual state. Behavioral Ecology 13, 427–438 (2002).
Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annual review of ecology, evolution, and systematics 34, 153–181 (2003).
Refinetti, R. The diversity of temporal niches in mammals. Biological Rhythm Research 39, 173–192 (2008).
Maor, R., Dayan, T., Ferguson-Gow, H. & Jones, K. E. Temporal niche expansion in mammals from a nocturnal ancestor after dinosaur extinction. Nature ecology & evolution 1, 1889–1895 (2017).
Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).
Grossnickle, D. M., Smith, S. M. & Wilson, G. P. Untangling the multiple ecological radiations of early mammals. Trends in ecology & evolution 34, 936–949 (2019).
Baker, J. & Venditti, C. Rapid change in mammalian eye shape is explained by activity pattern. Current Biology 29, 1082–1088. e1083 (2019).
McNab, B. K. The evolution of endothermy in the phylogeny of mammals. The American Naturalist 112, 1–21 (1978).
Crompton, A., Taylor, C. R. & Jagger, J. A. Evolution of homeothermy in mammals. Nature 272, 333–336 (1978).
Bennie, J. J., Duffy, J. P., Inger, R. & Gaston, K. J. Biogeography of time partitioning in mammals. Proc Natl Acad Sci U S A 111, 13727–13732, doi:10.1073/pnas.1216063110 (2014).
Mccain, C. M. & King, S. R. Body size and activity times mediate mammalian responses to climate change. Global change biology 20, 1760–1769 (2014).
Riede, S. J., van der Vinne, V. & Hut, R. A. The flexible clock: predictive and reactive homeostasis, energy balance and the circadian regulation of sleep–wake timing. Journal of Experimental Biology 220, 738–749, doi:10.1242/jeb.130757 (2017).
van der Vinne, V. et al. Maximising survival by shifting the daily timing of activity. Ecology letters 22, 2097–2102 (2019).
Harper, G. & Bunbury, N. Invasive rats on tropical islands: their population biology and impacts on native species. Global Ecology and Conservation 3, 607–627 (2015).
Sunarto, S., Kelly, M., Parakkasi, K. & Hutajulu, M. Cat coexistence in central Sumatra: ecological characteristics, spatial and temporal overlap, and implications for management. Journal of Zoology 296, 104–115 (2015).
Beschta, R. L. & Ripple, W. J. Large predators and trophic cascades in terrestrial ecosystems of the western United States. Biological conservation 142, 2401–2414 (2009).
Duffy, J. E. Biodiversity and ecosystem function: the consumer connection. Oikos 99, 201–219 (2002).
Sinclair, A., Mduma, S. & Brashares, J. S. Patterns of predation in a diverse predator–prey system. Nature 425, 288–290 (2003).
Hayward, M. W. & Slotow, R. Temporal partitioning of activity in large African carnivores: tests of multiple hypotheses. South African Journal of Wildlife Research-24-month delayed open access 39, 109–125 (2009).
Cunningham, C. X., Scoleri, V., Johnson, C. N., Barmuta, L. A. & Jones, M. E. Temporal partitioning of activity: rising and falling top-predator abundance triggers community‐wide shifts in diel activity. Ecography 42, 2157–2168 (2019).
Monterroso, P., Alves, P. C. & Ferreras, P. Catch me if you can: diel activity patterns of mammalian prey and predators. Ethology 119, 1044–1056 (2013).
Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).
Rovero, F. et al. A standardized assessment of forest mammal communities reveals consistent functional composition and vulnerability across the tropics. Ecography 43, 75–84, doi:https://doi.org/10.1111/ecog.04773 (2020).
Penone, C. et al. Global mammal beta diversity shows parallel assemblage structure in similar but isolated environments. Proceedings of the Royal Society B: Biological Sciences 283, 20161028 (2016).
Ahumada, J. A. et al. Community structure and diversity of tropical forest mammals: data from a global camera trap network. Philosophical Transactions of the Royal Society B: Biological Sciences 366, 2703–2711 (2011).
Donadio, E. & Buskirk, S. W. Diet, morphology, and interspecific killing in Carnivora. The American Naturalist 167, 524–536 (2006).
Di Bitetti, M. S., De Angelo, C. D., Di Blanco, Y. E. & Paviolo, A. Niche partitioning and species coexistence in a Neotropical felid assemblage. Acta Oecologica 36, 403–412 (2010).
Bischof, R., Ali, H., Kabir, M., Hameed, S. & Nawaz, M. A. Being the underdog: an elusive small carnivore uses space with prey and time without enemies. Journal of Zoology 293, 40–48 (2014).
Beckmann, J. P. & Berger, J. Rapid ecological and behavioural changes in carnivores: the responses of black bears (Ursus americanus) to altered food. Journal of Zoology 261, 207–212 (2003).
Gerber, B. D., Karpanty, S. M. & Randrianantenaina, J. Activity patterns of carnivores in the rain forests of Madagascar: implications for species coexistence. Journal of Mammalogy 93, 667–676 (2012).
Gehrt, S. D. & McGraw, M. Ecology of coyotes in urban landscapes. (2007).
Emmons, L. & Feer, F. Neotropical rainforest mammals: a field guide. (1997).
Santos, F. et al. Prey availability and temporal partitioning modulate felid coexistence in Neotropical forests. PloS one 14, e0213671 (2019).
Beaudrot, L. et al. Local temperature and ecological similarity drive distributional dynamics of tropical mammals worldwide. Global Ecology and Biogeography 28, 976–991, doi:10.1111/geb.12908 (2019).
Janzen, D. H. Why mountain passes are higher in the tropics. The American Naturalist 101, 233–249 (1967).
Khaliq, I., Hof, C., Prinzinger, R., Böhning-Gaese, K. & Pfenninger, M. Global variation in thermal tolerances and vulnerability of endotherms to climate change. Proceedings of the Royal Society B: Biological Sciences 281, 20141097 (2014).
Willmer, P., Stone, G. & Johnston, I. Environmental physiology of animals. (John Wiley & Sons, 2009).
Cruz, P., Paviolo, A., Bó, R. F., Thompson, J. J. & Di Bitetti, M. S. Daily activity patterns and habitat use of the lowland tapir (Tapirus terrestris) in the Atlantic Forest. Mammalian Biology 79, 376–383 (2014).
Taylor, W. & Skinner, J. Adaptations of the aardvark for survival in the Karoo: a review. Transactions of the Royal Society of South Africa 59, 105–108 (2004).
Levy, O., Dayan, T., Porter, W. P. & Kronfeld-Schor, N. Time and ecological resilience: can diurnal animals compensate for climate change by shifting to nocturnal activity? Ecological Monographs 89, e01334 (2019).
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).
Espinosa, S. & Salvador, J. Hunters landscape accessibility and daily activity of ungulates in Yasuní Biosphere Reserve, Ecuador. Therya 8, 45–52 (2017).
Butynski, T. M. Ecological survey of the impenetrable (Bwindi) forest, Uganda, and recommendations for its conservation and management. (1984).
Rovero, F. & Ahumada, J. The Tropical Ecology, Assessment and Monitoring (TEAM) Network: An early warning system for tropical rain forests. Science of the Total Environment 574, 914–923 (2017).
Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the causes of late Pleistocene extinctions on the continents. science 306, 70–75 (2004).
Peres, C. A., Emilio, T., Schietti, J., Desmoulière, S. J. & Levi, T. Dispersal limitation induces long-term biomass collapse in overhunted Amazonian forests. Proceedings of the National Academy of Sciences 113, 892–897 (2016).
Gardner, C. J., Bicknell, J. E., Baldwin-Cantello, W., Struebig, M. J. & Davies, Z. G. Quantifying the impacts of defaunation on natural forest regeneration in a global meta-analysis. Nature communications 10, 1–7 (2019).
Wilkie, D. S., Bennett, E. L., Peres, C. A. & Cunningham, A. A. The empty forest revisited. Annals of the New York Academy of Sciences 1223, 120–128 (2011).
Simpson, G. G. Splendid isolation: the curious history of South American mammals. Vol. 11 (Yale University Press New Haven, 1980).
Gorczynski, D. et al. Tropical mammal functional diversity increases with productivity but decreases with anthropogenic disturbance. Proceedings of the Royal Society B 288, 20202098 (2021).
Frey, S., Fisher, J. T., Burton, A. C. & Volpe, J. P. Investigating animal activity patterns and temporal niche partitioning using camera-trap data: challenges and opportunities. Remote Sensing in Ecology and Conservation 3, 123–132 (2017).
Bivand, R. et al. Package ‘maptools’. (2021).
Ensing, E. P. et al. GPS based daily activity patterns in European red deer and North American elk (Cervus elaphus): indication for a weak circadian clock in ungulates. PLoS One 9, e106997 (2014).
Faurby, S. et al. PHYLACINE 1.2: The phylogenetic atlas of mammal macroecology. Ecology 99, 2626 (2018).
Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world's birds and mammals. Ecology 95, 2027–2027, doi:https://doi.org/10.1890/13-1917.1 (2014).
Elff, M., Heisig, J. P., Schaeffer, M. & Shikano, S. Multilevel analysis with few clusters: improving likelihood-based methods to provide unbiased estimates and accurate inference. British Journal of Political Science (2020).
Elff, M. Mclogit: mixed conditional logit models (R package version 0.5. 1). Retrieved on 15 (2018).
Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. Journal of Agricultural, Biological, and Environmental Statistics 14, 322–337 (2009).
Vazquez, C., Rowcliffe, J. M., Spoelstra, K. & Jansen, P. A. Comparing diel activity patterns of wildlife across latitudes and seasons: Time transformations using day length. Methods in Ecology and Evolution 10, 2057–2066, doi:https://doi.org/10.1111/2041-210X.13290 (2019).
Rowcliffe, J. M., Kays, R., Kranstauber, B., Carbone, C. & Jansen, P. A. Quantifying levels of animal activity using camera trap data. Methods in Ecology and Evolution 5, 1170–1179 (2014).
Beaudrot, L. et al. Standardized Assessment of Biodiversity Trends in Tropical Forest Protected Areas: The End Is Not in Sight. PLOS Biology 14, doi:10.1371/journal.pbio.1002357 (2016).

Table S1. Additional information about the protected areas included in the study.
Table S2. Candidate models by biogeographic region.
Figure S1. Multinomial models’ coefficients estimates by each region with carnivores as the reference group
Figure S2. a) Distribution of body mass for the three different biogeographic regions. b) Number of species in each trophic guild and each biogeographic region.
Figure S3. Density plot of activity by biogeographic region and trophic guild
Figure S4. Predicted probability of diurnal, crepuscular, and nocturnal activity for a sequence of body mass values and raw proportions of all species included in the study.

There is NO Competing Interest.

SupplementaryMaterial.docx

Download PDF

Journal Publication

published 19 Nov, 2022

Read the published version in Nature Communications →

Version 1

posted

You are reading this latest preprint version

Consistent daily activity patterns across tropical forest mammal communities

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Consistent Patterns

Explanations

The odd-one-out: Neotropical insectivores

Conclusion

Methods

Declarations

References

Supplementary Material

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1