ASU Scholarship Showcase

The evolution of cooperation is a fundamental problem in biology, especially for non-relatives, where indirect fitness benefits cannot counter within-group inequalities. Multilevel selection models show how cooperation can evolve if it generates a group-level advantage, even when cooperators are disadvantaged within their group. This allows the possibility of group selection, but few examples have been described in nature. Here we show that group selection can explain the evolution of cooperative nest founding in the harvester ant Pogonomyrmex californicus. Through most of this species’ range, colonies are founded by single queens, but in some populations nests are instead founded by cooperative groups of unrelated queens. In mixed groups of cooperative and single-founding queens, we found that aggressive individuals had a survival advantage within their nest, but foundress groups with such non-cooperators died out more often than those with only cooperative members. An agent-based model shows that the between-group advantage of the cooperative phenotype drives it to fixation, despite its within-group disadvantage, but only when population density is high enough to make between-group competition intense. Field data show higher nest density in a population where cooperative founding is common, consistent with greater density driving the evolution of cooperative foundation through group selection.

Human societies are unique in the level of cooperation among non-kin. Evolutionary models explaining this behavior typically assume pure strategies of cooperation and defection. Behavioral experiments, however, demonstrate that humans are typically conditional co-operators who have other-regarding preferences. Building on existing models on the evolution of cooperation and costly punishment, we use a utilitarian formulation of agent decision making to explore conditions that support the emergence of cooperative behavior. Our results indicate that cooperation levels are significantly lower for larger groups in contrast to the original pure strategy model. Here, defection behavior not only diminishes the public good, but also affects the expectations of group members leading conditional co-operators to change their strategies. Hence defection has a more damaging effect when decisions are based on expectations and not only pure strategies.

We use an agent-based model to analyze the effects of spatial heterogeneity and agents’ mobility on social-ecological outcomes. Our model is a stylized representation of a dynamic population of agents moving and harvesting a renewable resource. Cooperators (agents who harvest an amount close to the maximum sustainable yield) and selfish agents (those who harvest an amount greater than the sustainable yield) are simulated in the model. Three indicators of the outcomes of the system are analyzed: the number of settlements, the resource level, and the proportion of cooperators in the population. Our paper adds a more realistic approach to previous studies on the evolution of cooperation by considering a social-ecological system in which agents move in a landscape to harvest a renewable resource. Our results conclude that resource dynamics play an important role when studying levels of cooperation and resource use. Our simulations show that the agents’ mobility significantly affects the outcomes of the system. This response is nonlinear and very sensible to the type of spatial distribution of the resource richness. In our simulations, better outcomes of long-term sustainability of the resource are obtained with moderate agent mobility and cooperation is enhanced in harsh environments with low resource level in which cooperative groups have natural boundaries fostered by agents’ low mobility.

During the last 40 years evidence from systematic case study analysis and behavioral experiments have provided a comprehensive perspective on how communities can manage common resources in a sustainable way. The conventional theory based on selfish rational actors cannot explain empirical observations. A more comprehensive theoretical framework of human behavior is emerging that include concepts such as trust, conditional cooperation, other-regarding preferences, social norms, and reputation. The new behavioral perspective also demonstrates that behavioral responses depend on social and biophysical context.

Recently, there has been an increased interest in using behavioral experiments to study hypotheses on the governance of social-ecological systems. A diversity of software tools are used to implement such experiments. We evaluated various publicly available platforms that could be used in research and education on the governance of social-ecological systems. The aims of the various platforms are distinct, and this is noticeable in the differences in their user-friendliness and their adaptability to novel research questions. The more easily accessible platforms are useful for prototyping experiments and for educational purposes to illustrate theoretical concepts. To advance novel research aims, more elaborate programming experience is required to either implement an experiment from scratch or adjust existing experimental software. There is no ideal platform best suited for all possible use cases, but we have provided a menu of options and their associated trade-offs.

Allowing resource users to communicate in behavioural experiments on commons dilemmas increases the level of cooperation. In actual common pool resource dilemmas in the real world, communication is costly, which is an important detail missing from most typical experiments. We conducted experiments where participants must give up harvesting opportunities to communicate. The constrained communication treatment is compared with the effect of limited information about the state of the resource and the actions of the other participants. We find that despite making communication costly, performance of groups improves in all treatments with communication. We also find that constraining communication has a more significant effect than limiting information on the performance of groups.

Online communities are becoming increasingly important as platforms for large-scale human cooperation. These communities allow users seeking and sharing professional skills to solve problems collaboratively. To investigate how users cooperate to complete a large number of knowledge-producing tasks, we analyze Stack Exchange, one of the largest question and answer systems in the world. We construct attention networks to model the growth of 110 communities in the Stack Exchange system and quantify individual answering strategies using the linking dynamics on attention networks. We identify two answering strategies. Strategy A aims at performing maintenance by doing simple tasks, whereas strategy B aims at investing time in doing challenging tasks. Both strategies are important: empirical evidence shows that strategy A decreases the median waiting time for answers and strategy B increases the acceptance rate of answers. In investigating the strategic persistence of users, we find that users tends to stick on the same strategy over time in a community, but switch from one strategy to the other across communities. This finding reveals the different sets of knowledge and skills between users. A balance between the population of users taking A and B strategies that approximates 2:1, is found to be optimal to the sustainable growth of communities.

We sought to evaluate the reproducibility of a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based approach to measure the stable-isotope enrichment of in vivo-labeled muscle ATP synthase β subunit (β-F1-ATPase), a protein most directly involved in ATP production, and whose abundance is reduced under a variety of circumstances. Muscle was obtained from a rat infused with stable-isotope-labeled leucine. The muscle was homogenized, β-F1-ATPase immunoprecipitated, and the protein was resolved using 1D-SDS PAGE. Following trypsin digestion of the isolated protein, the resultant peptide mixtures were subjected to analysis by HPLC-ESI-MS/MS, which resulted in the detection of multiple β-F1-ATPase peptides. There were three β-F1-ATPase unique peptides with a leucine residue in the amino acid sequence, and which were detected with high intensity relative to other peptides and assigned with >95% probability to β-F1-ATPase. These peptides were specifically targeted for fragmentation to access their stable-isotope enrichment based on MS/MS peak areas calculated from extracted ion chromatographs for selected labeled and unlabeled fragment ions. Results showed best linearity (R[superscript 2] = 0.99) in the detection of MS/MS peak areas for both labeled and unlabeled fragment ions, over a wide range of amounts of injected protein, specifically for the β-F1-ATPase[subscript 134-143] peptide. Measured stable-isotope enrichment was highly reproducible for the β-F1-ATPase[subscript 134-143] peptide (CV = 2.9%). Further, using mixtures of synthetic labeled and unlabeled peptides we determined that there is an excellent linear relationship (R[superscript 2] = 0.99) between measured and predicted enrichment for percent enrichments ranging between 0.009% and 8.185% for the β-F1-ATPase[subscript 134-143] peptide. The described approach provides a reliable approach to measure the stable-isotope enrichment of in-vivo-labeled muscle β-F1-ATPase based on the determination of the enrichment of the β-F1-ATPase[subscript 134-143] peptide.

Background: Healthy individuals on the lower end of the insulin sensitivity spectrum also have a reduced gene expression response to exercise for specific genes. The goal of this study was to determine the relationship between insulin sensitivity and exercise-induced gene expression in an unbiased, global manner.

Methods and Findings: Euglycemic clamps were used to measure insulin sensitivity and muscle biopsies were done at rest and 30 minutes after a single acute exercise bout in 14 healthy participants. Changes in mRNA expression were assessed using microarrays, and miRNA analysis was performed in a subset of 6 of the participants using sequencing techniques. Following exercise, 215 mRNAs were changed at the probe level (Bonferroni-corrected P<0.00000115). Pathway and Gene Ontology analysis showed enrichment in MAP kinase signaling, transcriptional regulation and DNA binding. Changes in several transcription factor mRNAs were correlated with insulin sensitivity, including MYC, r=0.71; SNF1LK, r=0.69; and ATF3, r= 0.61 (5 corrected for false discovery rate). Enrichment in the 5’-UTRs of exercise-responsive genes suggested regulation by common transcription factors, especially EGR1. miRNA species of interest that changed after exercise included miR-378, which is located in an intron of the PPARGC1B gene.

Conclusions: These results indicate that transcription factor gene expression responses to exercise depend highly on insulin sensitivity in healthy people. The overall pattern suggests a coordinated cycle by which exercise and insulin sensitivity regulate gene expression in muscle.

Although insulin resistance in skeletal muscle is well-characterized, the role of circulating whole blood in the metabolic syndrome phenotype is not well understood. We set out to test the hypothesis that genes involved in inflammation, insulin signaling and mitochondrial function would be altered in expression in the whole blood of individuals with metabolic syndrome. We further wanted to examine whether similar relationships that we have found previously in skeletal muscle exist in peripheral whole blood cells. All subjects (n=184) were Latino descent from the Arizona Insulin Resistance registry. Subjects were classified based on the metabolic syndrome phenotype according to the National Cholesterol Education Program’s Adult Treatment Panel III. Of the 184 Latino subjects in the study, 74 were classified with the metabolic syndrome and 110 were without. Whole blood gene expression profiling was performed using the Agilent 4x44K Whole Human Genome Microarray. Whole blood microarray analysis identified 1,432 probes that were altered in expression ≥1.2 fold and P<0.05 after Benjamini-Hochberg in the metabolic syndrome subjects. KEGG pathway analysis revealed significant enrichment for pathways including ribosome, oxidative phosphorylation and MAPK signaling (all Benjamini-Hochberg P<0.05). Whole blood mRNA expression changes observed in the microarray data were confirmed by quantitative RT-PCR. Transcription factor binding motif enrichment analysis revealed E2F1, ELK1, NF-kappaB, STAT1 and STAT3 significantly enriched after Bonferroni correction (all P<0.05). The results of the present study demonstrate that whole blood is a useful tissue for studying the metabolic syndrome and its underlying insulin resistance although the relationship between blood and skeletal muscle differs.