DOI: 10.5061/dryad.t1g1jwtcw
title: Reproduction is driven by seasonal environmental variation in an equatorial mammal, the banded mongoose (Mungos mungo)

description: Reproduction is an energetically costly activity and so is often timed to
 occur when conditions are most favourable. However, human-induced changes
 in long-term, seasonal, and short-term climatic conditions have imposed
 negative consequences for reproduction across a range of mammals. Whilst
 the effect of climate change on reproduction in temperate species is well
 known, its effect on equatorial species is comparatively understudied. We
 used long-term ecological data (~20 years) to investigate the impact of
 changes in rainfall and temperature on reproduction in an equatorial
 mammal, the banded mongoose (Mungos mungo). After controlling for the
 effects of group-size, we found that more females were pregnant and gave
 birth following periods of high seasonal rainfall, pregnancies increased
 at higher seasonal temperatures, and births increased with long-term
 rainfall. This is likely beneficial as high rainfall is positively
 associated with pup growth and survival. Females cannot, however, carry
 and raise pups over the course of a single wet season, so females face a
 trade-off in reproductive timing between maximising resource availability
 during gestation or the early life of pups, but not both.  Since
 the duration of the wet seasons is predicted to increase with climate
 change, the optimum conditions for banded mongoose reproduction may be
 extended. However, the potential benefits of extended wet seasons may be
 counteracted by the negative impacts of high temperatures on pup growth
 and survival. Our results highlight the importance of seasonality in
 reproduction of tropical mammals and the complex impacts of anthropogenic
 climate change on recruitment in equatorial species.
description: # Reproduction is driven by seasonal environmental variation in an
 equatorial mammal, the banded mongoose (Mungos mungo)
 [https://doi.org/10.5061/dryad.t1g1jwtcw](https://doi.org/10.5061/dryad.t1g1jwtcw) ## Description of the data and file structure Datasets include the imputed average maximum temperature (℃) and average rainfall (mm) per month at the study site which can be converted into a time series and then used to run the decomposition outlined in the methods of the manuscript. Pregnancy, birth and foetus count datasets are also include to allow the replication of the analyses for the number of births, pregnancies and foetuses models in the main manuscript respectively. The pregnancy and birth datasets can also be used to replicate the proportion of pregnancies and births models respectively, these analyses are presented in the supplementary information. **Max temp imputted dataset contains the following columns** month: The month specified in the 'Date' column year: The year specified in the 'Date' column Date: The month and year that the mean maximum temperature (mean_max_temp) relates to. mean_max_temp: The mean maximum monthly temperature (℃) in Mweya, Uganda for the month specified in the 'Date' column.  **Rainfall imputted dataset contains the following columns** month: The month specified in the 'Date' column year: The year specified in the 'Date' column Date: The month and year that the mean monthly rainfall (mean_rain) relates to. mean_rain: The mean monthly rainfall (mm) in Mweya, Uganda for the month specified in the 'Date' column.  **The birth_models dataset contains the following columns** date: The month at which the births occurred. number_of_births: The number of females that gave birth in a specific social group in the month specified in the 'date' column. (The banded mongoose population at our study site consists ~250 individuals, living in 10-12 social groups). group_ID: The ID of the social group  group_size: The number of individuals within the social group over 6 months old at the time specified in the date column. seasonal_rain: The seasonal variation in average monthly rainfall (mm) at the month specified in the 'date' column. This variable was obtained from the decomposition using the decompose() function in r which calculates the average value for each month across all years, this variable is then centered on the long-term trend value. This represents consistent intra-annual change. long_term_rain: The long-term trends in average monthly rainfall (mm) calculated using moving averages. This is also calculated using the decompose() function in r. short_term_rain: Short-term environmental fluctuations in average monthly rainfall (mm) at the month specified in the 'date' column.  This variables represents irregular changes in the environment. The decompose() function calculates this as the residual variation left over from the time series once the long-term and seasonal components are removed. seasonal_max_temp: The seasonal variation in average monthly maximum temperature (℃) at the month specified in the 'date' column. This variable represents consistent intra-annual change. This variable was obtained from the decomposition using the decompose() function in r which calculates the average value for each month across all years, the function then centres this variable on the long-term trend value. long_term_max_temp: The long-term trends in average monthly maximum temperature (℃) at the month specified in the 'date' column. This is also calculated using the decompose() function in r which uses moving averages. short_term_max_temp: Short-term environmental fluctuations in average monthly maximum temperature (℃) at the month specified in the 'date' column. This variable represents irregular changes in the environment. The decompose() function calculates this as the residual variation left over from the time series once the long-term and seasonal components are removed. number_of_females_over 9_months: The number of females within the social group over 9 months old at the time specified in the date column. long_term_rain_one_month_lag: The value for long term rainfall a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of females giving birth. seasonal_rain_one_month_lag:The value for seasonal rainfall a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of females giving birth. short_term_rain_one_month_lag: The value for short term rainfall a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of females giving birth. long_term_max_temp_one_month_lag: The value for long term maximum temperature a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of females giving birth. seasonal_max_temp_one_month_lag:The value for seasonal maximum temperature a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of females giving birth. short_term_max_temp_one_month_lag: The value for short term maximum temperature a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of females giving birth. long_term_rain_two_month_lag: The value for long term rainfall two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of females giving birth. seasonal_rain_two_month_lag: The value for seasonal rainfall two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of females giving birth. short_term_rain_two_month_lag: The value for short term rainfall two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of females giving birth. long_term_max_temp_two_month_lag: The value for long term maximum temperature two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of females giving birth. seasonal_max_temp_two_month_lag:The value for seasonal maximum temperature two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of females giving birth. short_term_max_temp_two_month_lag: The value for short term maximum temperature two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of females giving birth. proportion_of_births: The proportion of females within a group that gave birth, calculated as the number of females that gave birth in the month specified in the date column divided by the total number of females over 9 months within the group at that point in time. We used the number of females over 9 months since this is the age at which they can start to reproduce. This was used for the supplementary analysis (proportion of births models). **The Pregnancy_models_dataset contains the following columns** date: The month at which the pregnancies were recorded. number_preg_females: The number of females recorded as being pregnant in a specific social group in the month specified in the 'date' column. (The banded mongoose population at our study site consists ~250 individuals, living in 10-12 social groups). group_ID: The ID of the social group  group_size: The number of individuals within the social group over 6 months old at the time specified in the date column. seasonal_rain: The seasonal variation in average monthly rainfall (mm) at the month specified in the 'date' column. This variable was obtained from the decomposition using the decompose() function in r which calculates the average value for each month across all years, this variable is then centered on the long-term trend value. This represents consistent intra-annual change. long_term_rain: The long-term trends in average monthly rainfall (mm) at the month specified in the 'date' column. This is calculated using the decompose() function in r using moving averages . short_term_rain: Short-term environmental fluctuations in average monthly rainfall (mm) at the month specified in the 'date' column. This variable represents irregular changes in the environment. The decompose() function calculates this as the residual variation left over from the time series once the long-term and seasonal components are removed. seasonal_max_temp: The seasonal variation in average monthly maximum temperature (℃) at the time point specified in the 'date' column. This represents consistent intra-annual change. This variable was obtained from the decomposition using the decompose() function in r which calculates the average value for each month across all years, the function then centres this variable on the long-term trend value. long_term_max_temp: The long-term trends in average monthly maximum temperature (℃) at the month specified in the 'date' column. This is also calculated using the decompose() function in r which uses moving averages. short_term_max_temp: Short-term environmental fluctuations in average monthly maximum temperature (℃) at the month specified in the 'date' column. This variable represents irregular changes in the environment. The decompose() function calculates this as the residual variation left over from the time series once the long-term and seasonal components are removed. number_of_females_over 9_months: The number of females within a social group over 9 months old at the time specified in the date column.. long_term_rain_one_month_lag: The value for long term rainfall a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of pregnant females. seasonal_rain_one_month_lag:The value for seasonal rainfall a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of pregnant females. short_term_rain_one_month_lag: The value for short term rainfall a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of pregnant females. long_term_max_temp_one_month_lag: The value for long term maximum temperature a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of pregnant females. seasonal_max_temp_one_month_lag:The value for seasonal maximum temperature a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of pregnant females.. short_term_max_temp_one_month_lag: The value for short term maximum temperature a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of pregnant females. long_term_rain_two_month_lag: The value for long term rainfall two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of pregnant females. seasonal_rain_two_month_lag: The value for seasonal rainfall two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of pregnant females. short_term_rain_two_month_lag: The value for short term rainfall two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of pregnant females. long_term_max_temp_two_month_lag: The value for long term maximum temperature two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of pregnant females. seasonal_max_temp_two_month_lag: The value for seasonal maximum temperature two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of pregnant females. short_term_max_temp_two_month_lag: The value for short term maximum temperature two months prior to the corresponding date. This is used to investigate how environmental conditions two months prior affects the number of pregnant females. proportion_pregnant_females: The proportion of females within a group that were recorded as pregnant in the month specified in the date column, calculated as the number of females that were recorded as pregnant divided by the total number of females over 9 months within the group at that point in time. We used the number of females over 9 months since this is the age at which they can start to reproduce. This was used for the supplementary analysis (proportion of pregnant females models). **The Foetus_number_analysis dataset contains the following columns** DATE: The date at which the ultrasound scan was conducted. INDIV: The ID of the pregnant female that the ultrasound scan was conducted on. number of foetuses: The number of foetuses carried by the pregnant females the scan was conducted on. group_ID: The ID of the social group that the pregnant female belongs to. group_size: The number of individuals within the social group over 6 months old at the time specified in the date column. seasonal_rain: The seasonal variation in average monthly rainfall (mm) at the month specified in the 'DATE' column. This variable was obtained from the decomposition using the decompose() function in r which calculates the average value for each month across all years, this variable is then centered on the long-term trend value. This represents consistent intra-annual change. long_term_rain: The long-term trends in average monthly rainfall (mm) for the month specified in the 'DATE' column. This variable was obtained from the decomposition using the decompose() function in r which calculates long-term trends using moving averages.  short_term_rain: Short-term environmental fluctuations in average monthly rainfall (mm) for the month specified in the 'DATE' column. This variable represents irregular changes in the environment. This variable was obtained from the decomposition using the decompose() function in r  which calculates short-term fluctuations as the residual variation left over from the time series once the long-term and seasonal components are removed. seasonal_max_temp: The seasonal variation in average monthly maximum temperature (℃) at the month specified in the 'DATE' column. This represents consistent intra-annual change. This variable was obtained from the decomposition using the decompose() function in r which calculates the average value for each month across all years, the function then centres this variable on the long-term trend value. long_term_max_temp: The long-term trends in average monthly maximum temperature (℃) for the month specified in the 'DATE' column. This variables was calculated using the decompose() function in r which uses moving averages. short_term_max_temp: Short-term environmental fluctuations in average monthly maximum temperature (℃) for the month specified in the 'DATE' column. This column represents irregular changes in the environment. The decompose() function calculates this as the residual variation left over from the time series once the long-term and seasonal components are removed. long_term_rain_one_month_lag: The value for long term rainfall a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of foetuses produced by pregnant females. seasonal_rain_one_month_lag: The value for seasonal rainfall a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of foetuses produced by pregnant females. short_term_rain_one_month_lag: The value for short term rainfall a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of foetuses produced by pregnant females. long_term_max_temp_one_month_lag: The value for long term maximum temperature a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of foetuses produced by pregnant females. seasonal_max_temp_one_month_lag: The value for seasonal maximum temperature a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of foetuses produced by pregnant females. short_term_max_temp_one_month_lag: The value for short term maximum temperature a month prior to the corresponding date. This is used to investigate how environmental conditions a month prior affects the number of foetuses produced by pregnant females. ## Code/software These datasets are in the format .CSV and so can be viewed in Excel. As described in the methods of my paper, the analyses were conducted in R programming software.