Santa Fe Institute

Understanding the extent to which the species composition of a community can be explained by pairwise interactions is a long-standing question in ecology. Recent observations have revealed that stable microbial communities contain a high number of species that cannot coexist in pairs, providing new empirical elements to explore this question from a fresh perspective. Here, using species-rich models of ecological communities with pairwise interactions alone, we show that emergent coexistence arises naturally in an extent that is consistent with empirical observations. Interestingly, this phenomenon does not require additional mechanisms like intransitive or higher-order interactions; rather, coexistence can arise from the dense networks of indirect effects. As diversity increases, we show that indirect effects can become so intricate that pairwise interactions decouple from community composition, revealing a fundamental limit to reductionist explanations of coexistence. Our findings provide the theoretical foundations to understand how simple pairwise interactions can lead to emergent coexistence patterns in ecological communities.

Short tandem repeats (STRs) are low-entropy regions in the genome, consisting of a short (1-6 bp) unit that is consecutively repeated multiple times. They are known for high mutational instability, due to so-called stutter-mutations, in which the number of units in the run increases or descreases. In particular, STRs with repeat unit length of 1-2 bp are prone to mutate even within several cell divisions. The extremely rapid accumulation of variation makes them interesting phylogenetic markers for retrospective single-cell lineage reconstruction. Here we model their mutational dynamics at the level of individual repeat unit type and then aggregate length variations over many STR loci with the aim of obtaining a very fast ``molecular clock''. We calibrate our model based on several datasets with known lineage structure prepared from cultured cells. We find that the mutational dynamics of STRs are reasonably consistent for a given cell line, but vary among different ones. This suggests that the dynamics are not entirely explained by mutations in caretaker genes, rather, various other factors play a role -- possibly tissue origin and differentiation state. Further data and research is necessary to asses their relative effects.

In 2013, Bollobás, Mitsche, and Prałat gave upper and lower bounds for the likely metric dimension of random Erdős-Rényi graphs $G(n,p)$ for a large range of expected degrees. However, their results only apply when $d=pn=\omega(\log^5 n)$, leaving open sparser random graphs with $d=O(\log^5 n)$ or $d=o(\log^5n)$. Here we provide upper and lower bounds on the likely metric dimension of $G(n,p)$ in a range of $d$ starting just above the connectivity transition, i.e., where $d=c \log n$ for some constant $c > 1$, up to $d=O(\log^5 n)$. Our lower bound technique is based on an entropic argument which is weaker but more general than the use of Suen's inequality by Bollobás, Mitsche, and Prałat, whereas our upper bound is similar to theirs.

Many studies of pretrial rearrest aggregate charges of all severities together, from high-level felonies to petty misdemeanors. Using a dataset of over 15,000 felony defendants who were released pretrial between July 2017 and July 2021 in New Mexico, we disaggregated rearrest outcomes by offense type and severity and explored how these factors related to the risk scores generated by the Public Safety Assessment (PSA). We found that the rates of rearrest for serious crimes during pretrial release were lower than the overall rearrest rates suggest. In particular, across all PSA score categories, approximately one-third of rearrests involved misdemeanors or petty misdemeanors. Of the two-thirds of rearrests for felony charges, most were for fourth-degree offenses. Rearrest for first- or second-degree felonies was rare, occurring in less than 0.1% and 1% of cases, respectively, even among defendants whose initial charge was severe and who received high risk scores from the PSA.

Abstract Rural communities in developing regions are increasingly vulnerable to economic and social shocks, particularly where institutional support is limited. Through a focused case study of Tribal Textiles in rural Zambia during the COVID-19 pandemic, we adopt a deductive thematic approach to examine how social enterprises can strengthen community resilience during crises. We draw on (1) interviews with entrepreneurs and leadership, (2) field observations, and (3) relevant documentation; we find that effective community resilience emerges through the interaction of two mechanisms—embedded organizational values and adaptive creativity. These operate across four dimensions: resource mobilization, mental outlook, community networks, and economic investment, representing the elements of the community resilience framework that were most strongly evidenced in our data. Using these insights, we develop a framework that demonstrates how social enterprises can bridge institutional gaps while preserving cultural heritage in rural communities. This study advances theory by challenging the view that rural constraints are merely barriers to overcome. Instead, we reveal how deeply embedded social enterprises can transform apparent limitations into sources of community resilience. Our framework offers practical guidance for policymakers and development practitioners seeking to strengthen rural economic ecosystems through culturally authentic, socially embedded enterprise development.

The paradigm of scientific medicine is among the most influential epistemic shifts in the past century, wherein randomized controlled trials (RCTs) represent the impartial arbiter of medical claims. Nevertheless, not all RCTs agree, and systematic reviews are invoked to reconcile them. We theorize how tacitness-beliefs, implicit assumptions, expectations, heuristics, unrecorded routines-accumulates within 'socio-epistemic bubbles', continuous regions of social density that decrease diversity and increase unwarranted certainty about healthcare studied by RCTs. To assess our theory, we analyze 20,117 meta-analyses extracted from 1,962 Cochrane systematic reviews. We find that being closer within 'social space' inscribed by scientific collaboration increases agreement across RCTs. Our analysis suggests that this amplified certainty can drive premature convergence affecting medical practice and population health. By increasing researchers' ability to perform an experiment in the way required to achieve an expected result, socio-epistemic bubbles represent the dark matter of the experimental process often obscured. Our findings imply hidden limitations associated with unmeasured social influence across the policy sciences, through which conflicting claims perpetuate and highlight the necessity of accounting for them to improve collective certainty.

Neuron processes-axons and dendrites-have distinct branching patterns related to their biological function in the brain and body. Other non-neuronal cells in the nervous system, glia, also have characteristic branching morphologies. Our previous work has used biological scaling theory to connect branching patterns in neurons to biophysical function such as energy or conduction time minimization and material constrants in a compact, unifying mathematical model. Here, we use functionally relevant structural parameters related to asymmetric branching patterns extracted from our model as features in machine-learning classification methods to highlight differences between different types of neurons and glia as well as between healthy and diseased cells. Notably, we find that parameters related to information flow vary with position in the cell-that is, relative proximity of each branching junction to the soma (cell body) or synapses. We find that for some neuronal and glial cell type comparisons, such as comparisons between medium spiny neuron (MSN) dendrites, incorporating relative branching junction location significantly improves the performance of machine-learning classification methods. Our results imply that differences in information flow across cells drive specific morphological changes that correspond to localized regions of neuronal and glial cells. The promise of our methods and results lay foundation for future studies classifying neuronal and glial cells based on pathology, using our asymmetric scale factors and relative branching junction location as potential biomarkers to identify particular diseases based on both structural differences and the underlying differences in function.

Cognition is often associated with complex brains, yet many forms of learning─such as habituation, sensitization, and even spacing effects─have been observed in single cells and aneural organisms. These simple cognitive abilities, despite their cost, offer evolutionary advantages by allowing organisms to reduce environmental uncertainty and improve survival. Recent studies have confirmed early claims of learning-like behavior in protists and slime molds, pointing to the presence of basal cognitive functions long before the emergence of nervous systems. In this work, we adopt a synthetic biology approach to explore how minimal genetic circuits can implement nonassociative learning in unicellular systems. Building on theoretical models and using well-characterized regulatory elements, we design and simulate synthetic circuits capable of reproducing habituation, sensitization, and the massed-spaced learning effect. Our designs incorporate activators, repressors, fluorescent reporters, and quorum-sensing molecules, offering a platform for experimental validation. By examining the structural and dynamical constraints of these circuits, we highlight the distinct temporal dynamics of gene-based learning systems compared to neural counterparts and provide insights into the evolutionary and engineering challenges of building synthetic cognitive behavior at the cellular level.

The association between gut microbiota community composition and parasitic infections can result in complex interactions that impact host health. Determining whether microbiota composition impacts a host's susceptibility to infections or parasitic infections that alter the microbiota will have important implications for mitigation strategies. In this study, we characterized and compared the microbiota of eight wild Oregon Juncos (Junco hyemalis) infected with Plasmodium relictum GRW04 and eight non-infected individuals. Our results found that alpha diversity was significantly lower in infected birds. Based on beta diversity metrics, compositional turnover was primarily among rare bacterial taxa. The taxa Muribaculaceae, Acidobacteriales, Pseudomonas and Escherichia-Shingella were depleted in infected birds. When comparing co-occurrence networks of bacterial communities, the microbiota of infected birds had fewer connections and hub taxa were missing compared to non-infected birds. These results show stronger evidence for parasite-mediated microbiota composition and may correspond with characteristics associated with dysbiosis. Through future studies, it may be possible to determine whether these observed changes to microbial communities correlate with alterations to gut microbiota functions or detrimentally impact host health.

Understanding social relationships is critical to succeeding in primate societies. In species with complex social networks (including humans), correctly predicting the strength of one's social relationships or bonds helps individuals better navigate future interactions. Social contingency-behaviour that depends on and directly responds to another's actions-is a key feature of interaction that provides opportunities to learn about these relationships. We propose that playful teasing in great apes represents a specialized form of social contingency that enables relationship assessment in a relatively safe context. Playful teasing involves one individual pestering, harassing or provoking another in a playful manner. An ape can learn about their bond by observing how a social partner responds to mild provocation-seeing how far they can push the other before receiving an explicitly negative response. Since responses range from mild aversion to benign tolerance to reciprocal play, the teaser can gain valuable information about relationship quality through monitoring the socially contingent responses to teasing actions. This form of learning through contingency may have evolved as a relatively low-risk method to assess social relationships through direct feedback, though several alternative explanations exist. We examine multiple evolutionary hypotheses for playful teasing and offer suggestions for future empirical testing. This article is part of the theme issue 'Mechanisms of learning from social interaction'.