Research Article |
Corresponding author: Luane Maria Melo Azeredo ( luaneazeredo@gmail.com ) Academic editor: Carolina Arruda Freire
© 2019 Luane Maria Melo Azeredo, Monique Silva Ximenes, Kleytone Alves Pereira, Maria Paula Aguiar Fracasso, Luiz Carlos Serramo Lopez.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Azeredo LMM, Silva Ximenes M, Alves Pereira K, Aguiar Fracasso MP, Serramo Lopez LC (2019) Body mass index and glucose variations around the night in Artibeus planirostris (Chiroptera) measured under natural conditions. Zoologia 36: 1-8. https://doi.org/10.3897/zoologia.36.e28027
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Body condition is an important measure to estimate the energy reserve of an organism. Scientists frequently use body condition indices (BCIs) with morphometric measures but direct measurements, such as blood glucose, seem to be more reliable. We observed oscillations in the body condition and glucose indexes of individuals of Artibeus planirostris (Spix, 1823) during 13 nights in the field. We assume that if glucose levels are proportional to feeding state and body condition is a measure of energy reserve, blood glucose and BCI should be positively correlated and both are expected to increase during the night as the bats leave their diurnal roost to feed. To test this, we examined the relationship between blood glucose levels, BCI and reproductive phase of free flying male bats (n = 70) for 12 hours after sunset for 13 nights. Bats were captured in Reserva Biológica de Guaribas (Paraíba, Brazil) using mist nets. Blood glucose was analyzed with a portable glucometer. Supporting our assumptions, the number of hours after sunset and BCI presented significant positive correlations with glucose levels in A. planirostris. Reproductive phase did not present a significant correlation with any other variables. As we predicted, glucose level can be used as proxy for morphometric BCI and it can be measured with a simple portable glucometer. The increase both in glucose and BCI around the night can be explained by the efficient assimilation of nutrients present in fruits ingested by bats and the quick metabolism that increases the levels of glucose (an other nutrients) in blood, increasing the body mass.
Body condition, blood metabolites, foraging
Body condition is an important ecological attribute that provides an estimate of the energy reserve of an organism (
However, these BCI’s need to be carefully analyzed and associated with other methods of estimating body condition, to avoid misinterpretations (
Blood glucose is a metabolite that is directly correlated to feeding state (
Frugivorous bats consume carbohydrate and lipid-rich fruits and the digestion occurs immediately in an efficient way (
The studied species was the flat-faced fruit-eating bat, Artibeus planirostris (Spix, 1823). This species is relatively large, compared with other neotropical bats such as Carollia perspicillata (Linnaeus, 1758) and Sturnira liIium (Geoffroy, 1810) with forearm length ranging from 62 to 73 mm and body mass ranging from 40 to 69 g (
This study was conducted at Reserva Biológica de Guaribas (Rebio Guaribas), a protected area spanning part of the municipalities of Mamanguape and Rio Tinto, in state of Paraíba, Brazil (6°44'02"S, 35°10'32"W and 6°40'53"S, 35°09'59"W) The climate in Rebio Guaribas is warm and humid with an average annual temperature of 26 °C (
Bats were caught with nine mist nets of 12 x 3 m in two week interval expeditions of three days each from June to November of 2012, totaling 13 days. Adult animals were identified based on the absence of the epiphyseal cartilage of the 4th finger, at the metacarpal – phalangeal junction (
We collected blood samples from the femoral vein using disposable calibrated lancets GTech® inserted into a lancing device GTech®. The concentrations of blood glucose were measured using reagent test strips (Freestyle®) and a glucometer Optium™ Xceed® medsense in a similar procedure of
We calculated the body condition index (BCI) to assess the body condition of all individuals of A. planirostris. We evaluated the index using Le Cren’s relative condition (Kn) (
The sampling effort was 37,908h*m2. A total of 70 male bats were captured. The model that best fitted the relationship between our collected variables was that one that included glucose, BCI and hours after sunset (AIC = 205.51). Reproductive phase and the seasons (rainy and dry) were excluded by stepwise model selection and were considered non-significant. Glucose levels ranged from 1.11mmol/L to 20.36 mmol/L, (mean 7.50 ± SD 4.85), body mass ranged from 31.88 to 52.51 g (mean 43.28 ± 4.52) and forearm length ranged from 55.4 to 63.0 mm (mean 59.41 ± 1.81). The linear model showed that BCI and the hours after sunset influenced positively and significantly the levels of blood glucose (F-statistic: 16.03 on 2 and 67 DF, p-value: 2.051e-06). (see Table
Summary of linear model results, showing the relationship between glucose concentration and the variables selected by AIC stepwise model selection, for Artibeus planirostris in Rebio Guaribas, PB.
Coefficients | Estimate | Std Error | t-value | p-value |
Intercept | -4.08 | 4.89 | -1.98 | 0.03 |
Hours after sunset | 0.57 | 0.16 | 3.86 | <0.001 |
BCI | 5.29 | 1.98 | 2.67 | <0.01 |
Correlation matrix between glucose, BCI and hours after sunset for Artibeus planirostris in Rebio Guaribas, PB.
Variables | Glucose | Hours after sunset |
Hours after sunset | 0.51*** | |
BCI | 0.41** | 0.33* |
Data collected to evaluate the body condition of individuals of Artibeus planirostris from Jun to November of 2012 in Reserva Biológica de Guaribas, PB.
Rep. Phase | Body mass | Forearm length | Glucose (mmol/L) | Month | Season | BCI | Hours after sunset |
---|---|---|---|---|---|---|---|
Rep. Phase | Body mass | Forearm length | Glucose (mmol/L) | Month | Season | BCI | Hours after sunset |
NR | 42.98 | 60.5 | 1.6095 | Jun | rainy | 0.979179 | 1 |
R | 38.47 | 61.2 | 4.884 | Jun | rainy | 0.865873 | 2 |
NR | 46.36 | 58 | 2.2755 | Jun | rainy | 1.104203 | 2 |
NR | 43.13 | 57.2 | 1.11 | Jun | rainy | 1.042414 | 4 |
R | 50.31 | 61.1 | 6.771 | Jun | rainy | 1.134317 | 5 |
NR | 45.71 | 56.8 | 14.763 | Jun | rainy | 1.112969 | 9 |
R | 49.96 | 61.1 | 7.3815 | Jun | rainy | 1.126426 | 10 |
R | 45.7 | 57.4 | 4.4955 | Jun | rainy | 1.100474 | 1 |
NR | 44.53 | 55.4 | 8.0475 | Jun | rainy | 1.113125 | 1 |
NR | 45.01 | 61.4 | 5.661 | Jun | rainy | 1.009597 | 2 |
NR | 39.54 | 58.4 | 1.11 | Jun | rainy | 0.934969 | 3 |
NR | 43.38 | 60.9 | 7.9365 | Jun | rainy | 0.981454 | 4 |
NR | 48.01 | 59.7 | 13.431 | Jun | rainy | 1.109222 | 5 |
NR | 43.99 | 58.2 | 5.328 | Jun | rainy | 1.043961 | 6 |
R | 42.02 | 58.2 | 3.8295 | Jun | rainy | 0.997209 | 7 |
NR | 44.27 | 60 | 4.329 | Jun | rainy | 1.017425 | 8 |
NR | 47.09 | 58.3 | 12.1545 | Jun | rainy | 1.11551 | 10 |
NR | 37.06 | 55.6 | 1.11 | Jun | rainy | 0.922886 | 3 |
NR | 41.85 | 61.8 | 2.997 | Jun | rainy | 0.932316 | 4 |
NR | 38.12 | 59 | 1.11 | Jun | rainy | 0.891736 | 5 |
R | 43.5 | 60.2 | 4.1625 | Jun | rainy | 0.99623 | 3 |
R | 47.6 | 60.5 | 7.548 | Jun | rainy | 1.084433 | 4 |
NR | 42.8 | 61.1 | 5.7165 | Jun | rainy | 0.964993 | 4 |
R | 38.87 | 56.9 | 4.1625 | Jun | rainy | 0.944673 | 6 |
NR | 49.75 | 60.5 | 9.324 | Jun | rainy | 1.133415 | 8 |
R | 50.61 | 60.1 | 9.4905 | Jun | rainy | 1.161094 | 10 |
NR | 42.69 | 59 | 16.8165 | Jun | rainy | 0.998642 | 11 |
NR | 39 | 59.3 | 1.11 | Jul | rainy | 0.90746 | 2 |
NR | 40.98 | 57.2 | 1.11 | Jul | rainy | 0.99045 | 7 |
NR | 44.18 | 58.4 | 3.108 | Jul | rainy | 1.044687 | 9 |
R | 41.23 | 58 | 9.879 | Aug | rainy | 0.982017 | 4 |
R | 43.56 | 61 | 9.2685 | Aug | rainy | 0.983824 | 5 |
R | 49.82 | 61.9 | 17.538 | Aug | rainy | 1.107979 | 6 |
NR | 47.99 | 62.3 | 14.43 | Aug | rainy | 1.060062 | 8 |
R | 41.75 | 58.7 | 11.8215 | Aug | rainy | 0.981912 | 10 |
NR | 41.3 | 61.2 | 5.6055 | Aug | rainy | 0.92957 | 2 |
R | 48.7 | 61.2 | 10.3785 | Aug | rainy | 1.096127 | 3 |
NR | 37.68 | 59.8 | 12.21 | Aug | rainy | 0.869024 | 5 |
R | 49.15 | 61.4 | 2.2755 | Aug | rainy | 1.102459 | 6 |
NR | 45.42 | 57.3 | 13.4865 | Aug | rainy | 1.095743 | 6 |
R | 41.69 | 58.3 | 6.9375 | Aug | rainy | 0.98759 | 8 |
R | 40.62 | 57.3 | 10.656 | Aug | rainy | 0.979944 | 9 |
R | 48.86 | 58.7 | 20.3685 | Aug | rainy | 1.149131 | 9 |
R | 45.05 | 59.2 | 4.8285 | Aug | rainy | 1.050098 | 9 |
NR | 33.46 | 56.7 | 13.5975 | Aug | rainy | 0.816214 | 6 |
NR | 40.05 | 58.1 | 12.0435 | Aug | rainy | 0.952182 | 12 |
NR | 39.73 | 61.3 | 13.875 | Aug | rainy | 0.892696 | 8 |
NR | 41.89 | 59.9 | 8.9355 | Aug | rainy | 0.964421 | 9 |
NR | 44.94 | 59.4 | 10.0455 | Aug | rainy | 1.043818 | 10 |
R | 37.7 | 59.3 | 2.997 | Sep | dry | 0.877211 | 2 |
R | 38.86 | 61.4 | 1.11 | Sep | dry | 0.871649 | 2 |
R | 35.45 | 57.7 | 4.218 | Sep | dry | 0.848974 | 5 |
R | 35.25 | 58.7 | 8.103 | Sep | dry | 0.82904 | 5 |
R | 38.4 | 58.6 | 1.11 | Sep | dry | 0.904748 | 7 |
R | 47.14 | 61.4 | 9.213 | Sep | dry | 1.057374 | 7 |
R | 40.71 | 59.8 | 7.659 | Sep | dry | 0.938906 | 9 |
NR | 38.31 | 58.6 | 1.776 | Sep | dry | 0.902627 | 10 |
NR | 42.97 | 58.9 | 9.2685 | Sep | dry | 1.00699 | 10 |
R | 44.74 | 60.3 | 10.656 | Sep | dry | 1.022838 | 11 |
R | 48.26 | 61.3 | 3.8295 | Sep | dry | 1.084357 | 11 |
R | 31.88 | 56 | 1.11 | Sep | dry | 0.787917 | 1 |
R | 39.41 | 57.9 | 1.11 | Oct | dry | 0.940376 | 1 |
R | 45.38 | 60.2 | 7.437 | Oct | dry | 1.039285 | 2 |
R | 45.71 | 61.4 | 5.0505 | Oct | dry | 1.025298 | 8 |
R | 44.08 | 60.7 | 14.1525 | Oct | dry | 1.000754 | 9 |
NR | 49.09 | 61.5 | 14.0415 | Oct | dry | 1.099227 | 10 |
R | 40.62 | 63 | 6.549 | Nov | dry | 0.886764 | 4 |
R | 40.62 | 63 | 6.549 | Nov | dry | 0.886764 | 4 |
NR | 52.51 | 57 | 14.0415 | Nov | dry | 1.273812 | 9 |
NR | 52.51 | 57 | 14.0415 | Nov | dry | 1.273812 | 9 |
As predicted, glucose level was positively correlated with BCI and both variables increased their values along the night. The levels of blood glucose increased steadily after sunset, even with the bats flying and consequently expending energy. Our results were similar to
Some studies pointed that high levels of glucose can have detrimental effects on the health of mammals (
Although more direct measures are more reliable to predict body condition, many scientists still use morphometric BCIs, because the other measures can be expensive or difficult to perform under field conditions (
In Brazil, some studies have used morphometric BCIs for bats as an attempt to assess the seasonality of their body condition, or used it to compare traits among populations of different habitats (
It is important to emphasize that we did not test the accuracy of our portable glucometer by comparing its reading with other laboratory methods. Some studies demonstrated that portable glucometers do not have the same accuracy of laboratory methods when used for animals (
The present study showed a positive relationship between blood glucose level and body mass index that indicates that body mass is probably associated with the nutritional state of A. planirostris. Increase in glucose levels along the night can be explained by the efficient assimilation of nutrients presents in fruits ingested by the bats and the quick metabolism that increases the levels of glucose in blood. Some studies pointed that high active flight is the key to regulate homeostasis of bats. We cannot confirm that the gain of weight for A. planirostris is due to storing energy reserves or just the water present in fruits. Thus, we suggest that in further studies, it would be interesting to observe the action of other metabolites besides glucose, in order to better explore the body condition of bats
We thank SISBIO – ICMBio for the license that allowed this study to be conducted. We also thank Jorge Nascimento (Julião) and Getulio Freitas managers of the Reserva Biológica de Guaribas, PB, for their logistical support. In addition we are thankful for Genildo, our forest guide, and all people who participated in the data collection: Paloma, Rumenigg, Karlla, Hannah, Mayara, Caio, Anderson, Eduardo, Júlia, Mariana, Letícia and Derick. This research was undertaken during the master’s dissertation of MSX and postdoctoral research of MPAF. Lastly, we thank the Programa de Pós-graduação em Ecologia e Monitoramento ambiental (PPGEMA) from Universidade Federal da Paraíba, and Capes and CNPq for scholarships.