Rhesus macaques build new social connections after a natural disaster

Abstract

Climate change is increasing the frequency and intensity of weather-related disasters such as hurricanes, wildfires, floods, and droughts. Understanding resilience and vulnerability to these intense stressors and their aftermath could reveal adaptations to extreme environmental change. In 2017, Puerto Rico suffered its worst natural disaster, Hurricane Maria, which left 3,000 dead and provoked a mental health crisis. Cayo Santiago island, home to a population of rhesus macaques (Macaca mulatta), was devastated by the same storm. We compared social networks of two groups of macaques before and after the hurricane and found an increase in affiliative social connections, driven largely by monkeys most socially isolated before Hurricane Maria. Further analysis revealed monkeys invested in building new relationships rather than strengthening existing ones. Social adaptations to environmental instability might predispose rhesus macaques to success in rapidly changing anthropogenic environments.

Keywords: Cayo Santiago; Hurricane Maria; Macaca mulatta; Puerto Rico; Rhesus macaques; natural disaster; social network; social support.

Figure 1.. Hurricane Maria’s effect on vegetation and mortality

(A) Foliage cover from Cayo Santiago Island, as measured by greenness, decreased by 63% after Maria (t test, p = 3.7 × 10⁻²⁵). Images are Digital Globe aerial photos of Cayo Santiago island, Puerto Rico, before Hurricane Maria (left) and after (right). See Figure S1 for details on the damage to each group’s home range. (B) The death rate per 100 adults per month, from 1998 to 2018. We plot adult death rates because the exact date of death of infants and juveniles had an estimated error margin of up to 8 months due to the difficulty in individually recognizing and tracking young animals who had not yet received their unique ID tattoos. Color code: 2017 post-hurricane in dark orange and 2018 in yellow. Grey lines are years 1998 to 2017 pre-hurricane. The October 2017 death rate was more than triple the rate expected based on October months in previous years (> upper bound for 99.99% CI, p < 0.0001). The peak in March corresponds to a Shigella outbreak in 2010. The red vertical lines indicate the date when Hurricane Maria made landfall on Cayo Santiago. See Data S1A for comparison of mortality rates after natural disasters in previous studies.

Figure 2.. Rhesus macaques showed higher probabilities of affiliative behaviors after Hurricane Maria

(A and B) Distribution of the probability of being in proximity (A) and grooming (B) pre- (red) and post-hurricane (blue) for study groups V and KK. Pre-hurricane violin plots summarize multiple years of data collection (2015–2017). 2017 data only includes observations up to Hurricane Maria (September 20, 2017). Stars indicate statistical significance (95% CIs of model estimates do not include the null value, i.e., p < 0.05) (Table S1).

Figure 3.. Pre-disaster social integration, but not loss of a partner, predicted changes in the probability of engaging in grooming after Hurricane Maria

(A) Distribution of pre- to post-hurricane changes in an individual’s probability of grooming (for one sub-sampling iteration, see Figure S2 for all 500 subsampling iterations). (B–D) Pre- to post-hurricane change in an individual’s probability of grooming (based on one sub-sampling iteration) as a function of: (B) pre-disaster levels of individual social integration (measured by time spent grooming); (C) standardized strength of relationship to lost partners (measured by pre-disaster time spent grooming lost partners); (D) the change in an individual’s probability of being in proximity to others after the hurricane compared with before. Red lines in (B), (C), and (D) are regression lines using ggplot geom_smooth in R. correlation coefficients (r) and p values are computed by using cor.test in R. n.s., non-significant. See Table S2 for GLMM results across all subsampling iterations.

Figure 4.. Grooming networks were denser after Hurricane Maria

(A–D) Example grooming networks based on one sub-sampling iteration for group KK before the hurricane in 2017 (A) and after in 2018 (B); group V in 2017 (C) and in 2018 (D). 2017 networks include data up to Hurricane Maria (Sept 20, 2017). See Figure S3 for all groups and years. Note: network plots have average values of connectedness and are representative of other sub-sampling iterations. Each node is an individual. Color coding is as follows: males, green; females, purple. Edges indicate a grooming relationship, and arrows indicate the direction of grooming. Edge thickness indicates relationship strength based on proportion of grooming (number of scans a pair was observed grooming to total number of scans featuring animals from that pair). Node size scales with the number of unique partners. Network layout was held constant for pre- and post-hurricane periods to make the comparison clearer. Note, we estimated the precision of our pre-hurricane grooming networks based on Whitehead (see STAR Methods for details). This method estimates the correlation between the observed and true interactions probabilities between dyads within a network. Correlations >0.4 are generally considered to indicate useful representations of the underlying social structure. In our networks, correlation estimates for all groups and years range between 0.714 and 0.862 (Data S1C). See Tables S3 and S4 for hurricane effect on network density and relationship strength respectively in groups V and KK.

Figure 5.. Monkeys groomed different types of partners after Hurricane Maria

Violin plots summarize changes in proportion of grooming directed from one type of partner to another pre- to post-hurricane for individuals in group KK (orange) and group V (blue). Dotted red lines mark the “no change” limit. Stars indicate a significant change (95% CI does not include 0; p < 0.05) in proportion of grooming from before the hurricane to after (Table S5). “Male→Female” indicates grooming from males to females. Abbreviations are as follows: HighR, high-ranking; LowR, low-ranking. Note: the bi- or tri-modal shape of the violin plots reflect the pre-hurricane year used for comparison (2 for KK and 3 for V). We plotted all years together to facilitate presentation of results. Only differences robust to the pre-hurricane year used for comparison are ultimately detected as statistically significant.

Figure 6.. Reciprocity and closure of triads, but not probability of being in proximity, increased the likelihood of grooming between dyads after the hurricane

Violin plots summarizing the distribution of TERGM formation model log-odds for group KK (left) and V (right). Labels from top to bottom: proximity, reciprocity, triadic closure and network density (a control term). We plot the full distribution of log-odds over all 500 modeling iterations. Stars indicate a significant effect on relationship formation (95% CI of parameter estimate does not include 0; p < 0.05; see Table S5 for statistics). Positive log-odds are interpreted as an increased likelihood of relationship formation and negative log-odds as a decreased likelihood of relationship formation. The red dotted line marks the “coefficient = 0” limit.