Greater bee diversity is needed to maintain crop pollination over time.

The current biodiversity crisis underscores the need to understand how biodiversity loss affects ecosystem function in real-world ecosystems. At any one place and time, a few highly abundant species often provide the majority of function, suggesting that function could be maintained with relatively little biodiversity. However, biodiversity may be critical to ecosystem function at longer timescales if different species are needed to provide function at different times. Here we show that the number of wild bee species needed to maintain a threshold level of crop pollination increased steeply with the timescale examined: two to three times as many bee species were needed over a growing season compared to on a single day and twice as many species were needed over six years compared to during a single year. Our results demonstrate the importance of pollinator biodiversity to maintaining pollination services across time and thus to stable agricultural output.

The sensitivity of a honeybee colony to worker mortality depends on season and resource availability.

BACKGROUND: Honeybees have extraordinary phenotypic plasticity in their senescence rate, making them a fascinating model system for the evolution of aging. Seasonal variation in senescence and extrinsic mortality results in a tenfold increase in worker life expectancy in winter as compared to summer. To understand the evolution of this remarkable pattern of aging, we must understand how individual longevity scales up to effects on the entire colony. In addition, threats to the health of honey bees and other social insects are typically measured at the individual level. To predict the effects of environmental change on social insect populations, we must understand how individual effects impact colony performance. We develop a matrix model of colony demographics to ask how worker age-dependent and age-independent mortality affect colony fitness and how these effects differ by seasonal conditions. RESULTS: We find that there are seasonal differences in honeybee colony elasticity to both senescent and extrinsic worker mortality. Colonies are most elastic to extrinsic (age-independent) nurse and forager mortality during periods of higher extrinsic mortality and resource availability but most elastic to age-dependent mortality during periods of lower extrinsic mortality and lower resource availability. CONCLUSIONS: These results suggest that seasonal changes in the strength of selection on worker senescence partly explain the observed pattern of seasonal differences in worker aging in honey bees. More broadly, these results extend our understanding of the role of extrinsic mortality in the evolution of senescence to social animals and improve our ability to model the effects of environmental change on social insect populations of economic or conservation concern.

Individual learning phenotypes drive collective behavior.

Individual differences in learning can influence how animals respond to and communicate about their environment, which may nonlinearly shape how a social group accomplishes a collective task. There are few empirical examples of how differences in collective dynamics emerge from variation among individuals in cognition. Here, we use a naturally variable and heritable learning behavior called latent inhibition (LI) to show that interactions among individuals that differ in this cognitive ability drive collective foraging behavior in honey bee colonies. We artificially selected two distinct phenotypes: high-LI bees that ignore previously familiar stimuli in favor of novel ones and low-LI bees that learn familiar and novel stimuli equally well. We then provided colonies differentially composed of different ratios of these phenotypes with a choice between familiar and novel feeders. Colonies of predominantly high-LI individuals preferred to visit familiar food locations, while low-LI colonies visited novel and familiar food locations equally. Interestingly, in colonies of mixed learning phenotypes, the low-LI individuals showed a preference to visiting familiar feeders, which contrasts with their behavior when in a uniform low-LI group. We show that the shift in feeder preference of low-LI bees is driven by foragers of the high-LI phenotype dancing more intensely and attracting more followers. Our results reveal that cognitive abilities of individuals and their social interactions, which we argue relate to differences in attention, drive emergent collective outcomes.

Coordination among neighbors improves the efficacy of Zika control despite economic costs.

Emerging mosquito-borne viruses like Zika, dengue, and chikungunya pose a major threat to public health, especially in low-income regions of Central and South America, southeast Asia, and the Caribbean. Outbreaks of these diseases are likely to have long-term social and economic consequences due to Zika-induced congenital microcephaly and other complications. Larval control of the container-inhabiting mosquitoes that transmit these infections is an important tool for mitigating outbreaks. However, metapopulation theory suggests that spatiotemporally uneven larvicide treatment can impede control effectiveness, as recolonization compensates for mortality within patches. Coordinating the timing of treatment among patches could therefore substantially improve epidemic control, but we must also consider economic constraints, since coordination may have costs that divert resources from treatment. To inform practical disease management strategies, we ask how coordination among neighbors in the timing of mosquito control efforts influences the size of a mosquito-borne infectious disease outbreak under the realistic assumption that coordination has costs. Using an SIR (Susceptible-Infectious-Recovered)/metapopulation model of mosquito and disease dynamics, we examine whether sharing surveillance information and coordinating larvicide treatment among neighboring patches reduces human infections when incorporating coordination costs. We examine how different types of coordination costs and different surveillance methods jointly influence the effectiveness of larval control. We find that the effect of coordination depends on both costs and the type of surveillance used to inform treatment. With epidemiological surveillance, coordination improves disease outcomes, even when costly. With demographic surveillance, coordination either improves or hampers disease control, depending on the type of costs and surveillance sensitivity. Our results suggest coordination among neighbors can improve management of mosquito-borne epidemics under many, but not all, assumptions about costs. Therefore, estimating coordination costs is an important step for most effectively applying metapopulation theory to strategies for managing outbreaks of mosquito-borne viral infections.

A Multiscale Review of Behavioral Variation in Collective Foraging Behavior in Honey Bees.

The emergence of collective behavior from local interactions is a widespread phenomenon in social groups. Previous models of collective behavior have largely overlooked the impact of variation among individuals within the group on collective dynamics. Honey bees (Apis mellifera) provide an excellent model system for exploring the role of individual differences in collective behavior due to their high levels of individual variation and experimental tractability. In this review, we explore the causes and consequences of individual variation in behavior for honey bee foraging across multiple scales of organization. We summarize what is currently known about the genetic, developmental, and neurophysiological causes of individual differences in learning and memory among honey bees, as well as the consequences of this variation for collective foraging behavior and colony fitness. We conclude with suggesting promising future directions for exploration of the genetic and physiological underpinnings of individual differences in behavior in this model system.

How Life History Shapes Optimal Patterns of Senescence: Implications from Individuals to Societies.

One evolutionary view of aging, the disposable soma theory, suggests that an organism's rate of senescence depends on the amount of energy invested in somatic maintenance. Since organisms have limited energy to allocate among growth, maintenance, and reproduction, the optimal amount of energy to invest in maintenance is influenced by the probability of death from extrinsic causes and the effect of somatic investment on survival. In eusocial animals, the disposable soma theory can be used to explain colonies' energy investment in the longevity of workers, who act as the somatic elements of a superorganism. There have been few theoretical considerations of how changes in the costliness of worker maintenance or in the effect of individual life span on group fitness influence a colony's investment in worker longevity. We develop a decision theory model to evaluate how changing the marginal costs and benefits of longevity and extrinsic mortality influence optimal worker life span in a social insect colony. Our model predicts that higher extrinsic mortality favors shorter life span. However, increased life span is favored when marginal benefits are an increasing function of longevity. In honeybees, this explains how greater somatic investment is sometimes favored despite high mortality. Our approach expands the disposable soma theory to make quantitative predictions about the selective pressures shaping senescence in social systems.

Coordination Between the Sexes Constrains the Optimization of Reproductive Timing in Honey Bee Colonies.

Honeybees are an excellent model system for examining how trade-offs shape reproductive timing in organisms with seasonal environments. Honeybee colonies reproduce two ways: producing swarms comprising a queen and thousands of workers or producing males (drones). There is an energetic trade-off between producing workers, which contribute to colony growth, and drones, which contribute only to reproduction. The timing of drone production therefore determines both the drones' likelihood of mating and when colonies reach sufficient size to swarm. Using a linear programming model, we ask when a colony should produce drones and swarms to maximize reproductive success. We find the optimal behavior for each colony is to produce all drones prior to swarming, an impossible solution on a population scale because queens and drones would never co-occur. Reproductive timing is therefore not solely determined by energetic trade-offs but by the game theoretic problem of coordinating the production of reproductives among colonies.