9 research outputs found

    On the scaling of activity in tropical forest mammals

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    Activity range – the amount of time spent active per day – is a fundamental aspect contributing to the optimization process by which animals achieve energetic balance. Based on their size and the nature of their diet, theoretical expectations are that larger carnivores need more time active to fulfil their energetic needs than do smaller ones and also more time active than similar‐sized non‐carnivores. Despite the relationship between daily activity, individual range and energy acquisition, large‐scale relationships between activity range and body mass among wild mammals have never been properly addressed. This study aimed to understand the scaling of activity range with body mass, while controlling for phylogeny and diet. We built simple empirical predictions for the scaling of activity range with body mass for mammals of different trophic guilds and used a phylogenetically controlled mixed model to test these predictions using activity records of 249 mammal populations (128 species) in 19 tropical forests (in 15 countries) obtained using camera traps. Our scaling model predicted a steeper scaling of activity range in carnivores (0.21) with higher levels of activity (higher intercept), and near‐zero scaling in herbivores (0.04). Empirical data showed that activity ranges scaled positively with body mass for carnivores (0.061), which also had higher intercept value, but not for herbivores, omnivores and insectivores, in general, corresponding with the predictions. Despite the many factors that shape animal activity at local scales, we found a general pattern showing that large carnivores need more time active in a day to meet their energetic demands. Introduction Activity range – the amount of time, in hours, spent active per day – is a fundamental outcome of the complex physiological and behavioral optimization process by which animals ensure that energy input keeps pace with energy output. In addition to basal metabolism, animals face costs of foraging, acquiring mates and shelter, building reserves for lean times and escaping predators (Carbone et al. 2007, Halle and Stenseth 2012). Environmental and ecological factors that vary through the day (e.g. luminosity, temperature, predation risk and competition avoidance) constrain activity to certain times, depending on morpho‐physiological limitations (Castillo‐Ruiz et al. 2012, Hut et al. 2012). In addition, animals need time to rest in order to recover their cognitive or physical condition (Siegel 2005). Thus, they must optimize their activity range to meet their resource requirements, while dealing with natural daily cycles and saving time for sleep/rest (Downes 2001, Siegel 2005, Cozzi et al. 2012). The resource requirements of mammals are related to basal metabolic rate, which scales positively with body mass (Kleiber 1932, Isaac and Carbone 2010), while predation risk decreases with body mass (Sinclair et al. 2003, Hopcraft et al. 2009). Because high predation risk constrains activity while high resource needs increases activity range (Cozzi et al. 2012, Suselbeek et al. 2014), the question arises whether and how activity range also scales with body mass. Day range (total distance travelled in a day) and home range (area in which animals perform their daily activities) scales positively with body mass and are key metrics to understand the resource requirements of an animal (McNab 1963, Kelt and Van Vuren 2001, Carbone et al. 2005, Tamburello et al. 2015). As activity range is related to space‐use metrics (i.e. home range and day range), it is hence, also related to the acquisition of energy. Given that, one might expect activity range to increase with body mass. However, we have a poor understanding of how this relationship actually looks. Previous work developed predictions of body mass scaling with day range (Garland 1983, Carbone et al. 2005) and travel speed (Carbone et al. 2007, Rowcliffe et al. 2016). From a simple physical viewpoint, activity range should equal the day range divided by average travel speed. It should thus be possible to infer the scaling of activity range with body mass from these relationships. Some of the variation in space use across species that is not explained by body mass is associated with different evolutionary histories and ecological traits (McNab 1963, Kelt and Van Vuren 2001, Price and Hopkins 2015, Tamburello et al. 2015). Diet is the most conspicuous of these, because primary and secondary productivity present different overall yields and accessibility for consumers (Jetz et al. 2004), which in turn influence individual movements (Carbone et al. 2005) and potentially activity range, when exploiting resources at different trophic levels. The nature of the diet aggravates the higher energetic demands of larger carnivores. Predators have considerable energetic constraints related to hunting and handling their prey (Gorman et al. 1998, Carbone et al. 1999) as animal prey can be rare, widely dispersed, unpredictable in time and space and not storable (Jetz et al. 2004, Carbone et al. 2007). Therefore, carnivores have the lowest energy supply rates (supply rate of usable resources available inside the home range), independent of body mass, when compared to other diet categories (Jetz et al. 2004) besides exploring larger areas and traveling greater daily distances (McNab 1963, Kelt and Van Vuren 2001, Carbone et al. 2005, Tamburello et al. 2015). Therefore, larger animals occupy larger areas than small ones, and carnivores occupy larger areas than do similar‐sized non‐carnivores (Jetz et al. 2004, Tamburello et al. 2015). To date, few studies have considered interspecific variation in activity range with body mass and other species traits. For example, van Schaik and Griffiths (1996) and Gómez et al. (2005) anecdotally suggested that larger mammal species are cathemeral (i.e. active day and night), which implies that they can be active during a larger proportion of the 24‐h cycle. Rowcliffe et al. (2014) found that activity range is positively correlated with body mass in tropical forest mammals in Panama. Ramesh et al. (2015) found a negative relationship between body mass and activity concentration (i.e. how concentrated in few hours is the activity of an animal during the day) in Indian mammals, also equating to a positive association between activity range and body mass. However, no study has explored variation in activity range across a diverse range of species, while controlling for phylogeny and diet. This has been, at least in part, due to a lack of consistent data available on a wide range of species. Recent work using camera traps (Oliveira‐Santos et al. 2013, Rowcliffe et al. 2014), however, has demonstrated that accurate estimates of activity range can be obtained from photographic records from camera traps. Given the large and rapidly increasing volume of camera‐trapping data available globally (Burton et al. 2015), these approaches, consistently applied across a wide range of studies, can provide an important basis for the large‐scale study of activity. Here, we provided simple empirical predictions for the scaling of activity range with body mass for mammals of different trophic guilds. To test these predictions, we estimated the activity range for 249 populations of 128 terrestrial mammal species across 19 tropical forests, and used a phylogenetically controlled mixed model to determine how activity range scales with body mass by diet. As larger animals occupy larger areas than small ones, and carnivores occupy larger areas than do similar‐sized non‐carnivores (Jetz et al. 2004), we hypothesize that carnivores will present a higher scaling of activity range with body mass and also higher activity ranges for a given mass (higher intercept) when compared to herbivores, omnivores and insectivores

    Development and validation of quantitative PCR assays for HIV-associated cryptococcal meningitis in sub-Saharan Africa: a diagnostic accuracy study

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    Background: HIV-associated cryptococcal meningitis is the second leading cause of AIDS-related deaths, with a 10-week mortality rate of 25–30%. Fungal load assessed by colony-forming unit (CFU) counts is used as a prognostic marker and to monitor response to treatment in research studies. PCR-based assessment of fungal load could be quicker and less labour-intensive. We sought to design, optimise, and validate quantitative PCR (qPCR) assays for the detection, identification, and quantification of Cryptococcus infections in patients with cryptococcal meningitis in sub-Saharan Africa. Methods: We developed and validated species-specific qPCR assays based on DNA amplification of QSP1 (QSP1A specific to Cryptococcus neoformans, QSP1B/C specific to Cryptococcus deneoformans, and QSP1D specific to Cryptococcus gattii species) and a pan-Cryptococcus assay based on a multicopy 28S rRNA gene. This was a longitudinal study that validated the designed assays on cerebrospinal fluid (CSF) of 209 patients with cryptococcal meningitis at baseline (day 0) and during anti-fungal therapy (day 7 and day 14), from the AMBITION-cm trial in Botswana and Malawi (2018–21). Eligible patients were aged 18 years or older and presenting with a first case of cryptococcal meningitis. Findings: When compared with quantitative cryptococcal culture as the reference, the sensitivity of the 28S rRNA was 98·2% (95% CI 95·1–99·5) and of the QSP1 assay was 90·4% (85·2–94·0) in CSF at day 0. Quantification of the fungal load with QSP1 and 28S rRNA qPCR correlated with quantitative cryptococcal culture (R2=0·73 and R2=0·78, respectively). Both Botswana and Malawi had a predominant C neoformans prevalence of 67% (95% CI 55–75) and 68% (57–73), respectively, and lower C gattii rates of 21% (14–31) and 8% (4–14), respectively. We identified ten patients that, after 14 days of treatment, harboured viable but non-culturable yeasts based on QSP1 RNA detection (without any positive CFU in CSF culture). Interpretation: QSP1 and 28S rRNA assays are useful in identifying Cryptococcus species. qPCR results correlate well with baseline quantitative cryptococcal culture and show a similar decline in fungal load during induction therapy. These assays could be a faster alternative to quantitative cryptococcal culture to determine fungal load clearance. The clinical implications of the possible detection of viable but non-culturable cells in CSF during induction therapy remain unclear. Funding: European and Developing Countries Clinical Trials Partnership; Swedish International Development Cooperation Agency; Wellcome Trust/UK Medical Research Council/UKAID Joint Global Health Trials; and UK National Institute for Health Research

    Development and validation of quantitative PCR assays for HIV-associated cryptococcal meningitis in sub-Saharan Africa: a diagnostic accuracy study

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    Background: HIV-associated cryptococcal meningitis is the second leading cause of AIDS-related deaths, with a 10-week mortality rate of 25–30%. Fungal load assessed by colony-forming unit (CFU) counts is used as a prognostic marker and to monitor response to treatment in research studies. PCR-based assessment of fungal load could be quicker and less labour-intensive. We sought to design, optimise, and validate quantitative PCR (qPCR) assays for the detection, identification, and quantification of Cryptococcus infections in patients with cryptococcal meningitis in sub-Saharan Africa. Methods: We developed and validated species-specific qPCR assays based on DNA amplification of QSP1 (QSP1A specific to Cryptococcus neoformans, QSP1B/C specific to Cryptococcus deneoformans, and QSP1D specific to Cryptococcus gattii species) and a pan-Cryptococcus assay based on a multicopy 28S rRNA gene. This was a longitudinal study that validated the designed assays on cerebrospinal fluid (CSF) of 209 patients with cryptococcal meningitis at baseline (day 0) and during anti-fungal therapy (day 7 and day 14), from the AMBITION-cm trial in Botswana and Malawi (2018–21). Eligible patients were aged 18 years or older and presenting with a first case of cryptococcal meningitis. Findings: When compared with quantitative cryptococcal culture as the reference, the sensitivity of the 28S rRNA was 98·2% (95% CI 95·1–99·5) and of the QSP1 assay was 90·4% (85·2–94·0) in CSF at day 0. Quantification of the fungal load with QSP1 and 28S rRNA qPCR correlated with quantitative cryptococcal culture (R2=0·73 and R2=0·78, respectively). Both Botswana and Malawi had a predominant C neoformans prevalence of 67% (95% CI 55–75) and 68% (57–73), respectively, and lower C gattii rates of 21% (14–31) and 8% (4–14), respectively. We identified ten patients that, after 14 days of treatment, harboured viable but non-culturable yeasts based on QSP1 RNA detection (without any positive CFU in CSF culture). Interpretation: QSP1 and 28S rRNA assays are useful in identifying Cryptococcus species. qPCR results correlate well with baseline quantitative cryptococcal culture and show a similar decline in fungal load during induction therapy. These assays could be a faster alternative to quantitative cryptococcal culture to determine fungal load clearance. The clinical implications of the possible detection of viable but non-culturable cells in CSF during induction therapy remain unclear

    Consistent diel activity patterns of forest mammals among tropical regions

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    An animal’s daily use of time (their “diel activity”) reflects their adaptations, requirements, and interactions, yet we know little about the underlying processes governing diel activity within and among communities. Here we examine whether community-level activity patterns differ among biogeographic regions, and explore the roles of top-down versus bottom-up processes and thermoregulatory constraints. Using data from systematic camera-trap networks in 16 protected forests across the tropics, we examine the relationships of mammals’ diel activity to body mass and trophic guild. Also, we assess the activity relationships within and among guilds. Apart from Neotropical insectivores, guilds exhibited consistent cross-regional activity in relation to body mass. Results indicate that thermoregulation constrains herbivore and insectivore activity (e.g., larger Afrotropical herbivores are ~7 times more likely to be nocturnal than smaller herbivores), while bottom-up processes constrain the activity of carnivores in relation to herbivores, and top-down processes constrain the activity of small omnivores and insectivores in relation to large carnivores’ activity. Overall, diel activity of tropical mammal communities appears shaped by similar processes and constraints among regions reflecting body mass and trophic guilds

    Occupancy winners in tropical protected forests: a pantropical analysis

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    The structure of forest mammal communities appears surprisingly consistent across the continental tropics, presumably due to convergent evolution in similar environments. Whether such consistency extends to mammal occupancy, despite variation in species characteristics and context, remains unclear. Here we ask whether we can predict occupancy patterns and, if so, whether these relationships are consistent across biogeographic regions. Specifically, we assessed how mammal feeding guild, body mass and ecological specialization relate to occupancy in protected forests across the tropics. We used standardized camera-trap data (1002 camera-trap locations and 2–10 years of data) and a hierarchical Bayesian occupancy model. We found that occupancy varied by regions, and certain species characteristics explained much of this variation. Herbivores consistently had the highest occupancy. However, only in the Neotropics did we detect a significant effect of body mass on occupancy: large mammals had lowest occupancy. Importantly, habitat specialists generally had higher occupancy than generalists, though this was reversed in the Indo-Malayan sites. We conclude that habitat specialization is key for understanding variation in mammal occupancy across regions, and that habitat specialists often benefit more from protected areas, than do generalists. The contrasting examples seen in the Indo-Malayan region probably reflect distinct anthropogenic pressures

    Development and validation of quantitative PCR assays for HIV-associated cryptococcal meningitis in sub-Saharan Africa: a diagnostic accuracy study

    No full text
    Background: HIV-associated cryptococcal meningitis is the second leading cause of AIDS-related deaths, with a 10-week mortality rate of 25–30%. Fungal load assessed by colony-forming unit (CFU) counts is used as a prognostic marker and to monitor response to treatment in research studies. PCR-based assessment of fungal load could be quicker and less labour-intensive. We sought to design, optimise, and validate quantitative PCR (qPCR) assays for the detection, identification, and quantification of Cryptococcus infections in patients with cryptococcal meningitis in sub-Saharan Africa. Methods: We developed and validated species-specific qPCR assays based on DNA amplification of QSP1 (QSP1A specific to Cryptococcus neoformans, QSP1B/C specific to Cryptococcus deneoformans, and QSP1D specific to Cryptococcus gattii species) and a pan-Cryptococcus assay based on a multicopy 28S rRNA gene. This was a longitudinal study that validated the designed assays on cerebrospinal fluid (CSF) of 209 patients with cryptococcal meningitis at baseline (day 0) and during anti-fungal therapy (day 7 and day 14), from the AMBITION-cm trial in Botswana and Malawi (2018–21). Eligible patients were aged 18 years or older and presenting with a first case of cryptococcal meningitis. Findings: When compared with quantitative cryptococcal culture as the reference, the sensitivity of the 28S rRNA was 98·2% (95% CI 95·1–99·5) and of the QSP1 assay was 90·4% (85·2–94·0) in CSF at day 0. Quantification of the fungal load with QSP1 and 28S rRNA qPCR correlated with quantitative cryptococcal culture (R2=0·73 and R2=0·78, respectively). Both Botswana and Malawi had a predominant C neoformans prevalence of 67% (95% CI 55–75) and 68% (57–73), respectively, and lower C gattii rates of 21% (14–31) and 8% (4–14), respectively. We identified ten patients that, after 14 days of treatment, harboured viable but non-culturable yeasts based on QSP1 RNA detection (without any positive CFU in CSF culture). Interpretation: QSP1 and 28S rRNA assays are useful in identifying Cryptococcus species. qPCR results correlate well with baseline quantitative cryptococcal culture and show a similar decline in fungal load during induction therapy. These assays could be a faster alternative to quantitative cryptococcal culture to determine fungal load clearance. The clinical implications of the possible detection of viable but non-culturable cells in CSF during induction therapy remain unclear. Funding: European and Developing Countries Clinical Trials Partnership; Swedish International Development Cooperation Agency; Wellcome Trust/UK Medical Research Council/UKAID Joint Global Health Trials; and UK National Institute for Health Research
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