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Cover page of (Re)conceptualizing Neighborhood Ecology in Social Disorganization Theory: From a Variable-Centered Approach to a Neighborhood-Centered Approach

(Re)conceptualizing Neighborhood Ecology in Social Disorganization Theory: From a Variable-Centered Approach to a Neighborhood-Centered Approach


Shaw and McKay advanced social disorganization theory in the 1930s, kick-starting a large body of research on communities and crime. Studies emphasize individual impacts of poverty, residential instability, and racial/ethnic heterogeneity by examining their independent effects on crime, adopting a variable-centered approach. We use a “neighborhood-centered” approach that considers how structural forces combine into unique constellations that vary across communities, with consequences for crime. Examining neighborhoods in Southern California we: (1) identify neighborhood typologies based on levels of poverty, instability, and heterogeneity; (2) explore how these typologies fit within a disorganization framework and are spatially distributed across the region; and (3) examine how these typologies are differentially associated with crime. Results reveal nine neighborhood types with varying relationships to crime.

Cover page of Immigrant Organizations and Neighborhood Crime

Immigrant Organizations and Neighborhood Crime


We examine the impact of immigrant-serving organizations on neighborhood crime in the Los Angeles Metropolitan area, while accounting for other community correlates of crime as well as potential endogeneity. We estimate longitudinal negative binomial regression models that test for the main, mediating, and moderating effects of immigrant-serving organizations. We found that immigrant-serving organizations generally have crime-reducing effects for all types of crime. We also find that high immigrant concentration is associated with lower levels of crime in general, and this effect is moderated by the number of organizations, which underlines the importance of accounting for these organizations when studying the nexus of immigrant concentration and neighborhood crime.

Cover page of The Network of Neighborhoods and Geographic Space: Implications for Joblessness While on Parole

The Network of Neighborhoods and Geographic Space: Implications for Joblessness While on Parole


Objectives: Few studies have examined the consequences of neighborhoods for job prospects for people on parole. Specifically, networks between neighborhoods in where people commute to work and their spatial distributions may provide insight into patterns of joblessness because they represent the economic structure between neighborhoods. We argue that the network of neighborhoods provides insight into the competition people on parole face in the labor market, their spatial mismatch from jobs, as well as their structural support. Methods: We use data from people on parole released in Texas from 2006 to 2010 and create a network between all census tracts in Texas based on commuting ties from home to work. We estimate a series of multilevel models examining how network structures are related to joblessness. Results: The findings indicate that the structural position of neighborhoods has consequences for people on parole’s joblessness. Higher outdegree, reflecting neighborhoods with more outgoing ties to other neighborhoods, was consistently associated with less joblessness, while higher indegree, reflecting neighborhoods with more incoming ties into the neighborhood, was associated with more joblessness, particularly for Black and Latino people on parole. There was also some evidence of differences depending on geographic scale. Conclusions: Structural neighborhood-to-neighborhood networks are another component to understanding joblessness while people are on parole. The most consistent support was shown for the competition and structural support mechanisms, rather than spatial mismatch.

Cover page of Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments

Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments


Objectives: Despite theoretical interest in how dimensions of the built environment can help explain the location of crime in micro−geographic units, measuring this is difficult. Methods: This study adopts a strategy that first scrapes images from Google Street View every 20 meters in every street segment in the city of Santa Ana, CA, and then uses machine learning to detect features of the environment. We capture eleven different features across four main dimensions, and demonstrate that their relative presence across street segments considerably increases the explanatory power of models of five different Part 1 crimes. Results: The presence of more persons in the environment is associated with higher levels of crime. The auto−oriented measures—vehicles and pavement—were positively associated with crime rates. For the defensible space measures, the presence of walls has a slowing negative relationship with most crime types, whereas fences did not. And for our two greenspace measures, although terrain was positively associated with crime rates, vegetation exhibited an inverted−U relationship with two crime types. Conclusions: The results demonstrate the efficacy of this approach for measuring the built environment.

Cover page of Improving or declining: What are the consequences for changes in local crime?*

Improving or declining: What are the consequences for changes in local crime?*


Whereas existing ecology of crime research frequently uses a cross-sectional design, an open question is whether theories underlying such studies will operate similarly in longitudinal research. Using latent trajectory models and longitudinal data in half-mile egohoods from the Southern California region over a 10-year period (2000–2010), we explore this question and assess whether the changes in key measures of social disorganization theory are related to changes in violent or property crime through three possible relationships: 1) a monotonic relationship, 2) an asymmetric relationship, and 3) a perturbation relationship in which any change increases crime. We find evidence that measures can exhibit any of these three possible relationships, highlighting the importance of not assuming monotonic relationships. Most frequently observed are asymmetric relationships, which we posit are simultaneously capturing more than one theoretical process of neighborhoods and crime. Specific findings include asymmetric relationships between change in concentrated disadvantage, racial/ethnic minority composition, or population and violent crime, as well as relationships between change in Asian composition or population and property crime. We consider how this strategy opens a needed area of future research assessing how measures for other theories operate as environments change.

Cover page of Association between genetic and socioenvironmental risk for schizophrenia during upbringing in a UK longitudinal cohort.

Association between genetic and socioenvironmental risk for schizophrenia during upbringing in a UK longitudinal cohort.



Associations of socioenvironmental features like urbanicity and neighborhood deprivation with psychosis are well-established. An enduring question, however, is whether these associations are causal. Genetic confounding could occur due to downward mobility of individuals at high genetic risk for psychiatric problems into disadvantaged environments.


We examined correlations of five indices of genetic risk [polygenic risk scores (PRS) for schizophrenia and depression, maternal psychotic symptoms, family psychiatric history, and zygosity-based latent genetic risk] with multiple area-, neighborhood-, and family-level risks during upbringing. Data were from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative cohort of 2232 British twins born in 1994-1995 and followed to age 18 (93% retention). Socioenvironmental risks included urbanicity, air pollution, neighborhood deprivation, neighborhood crime, neighborhood disorder, social cohesion, residential mobility, family poverty, and a cumulative environmental risk scale. At age 18, participants were privately interviewed about psychotic experiences.


Higher genetic risk on all indices was associated with riskier environments during upbringing. For example, participants with higher schizophrenia PRS (OR = 1.19, 95% CI = 1.06-1.33), depression PRS (OR = 1.20, 95% CI = 1.08-1.34), family history (OR = 1.25, 95% CI = 1.11-1.40), and latent genetic risk (OR = 1.21, 95% CI = 1.07-1.38) had accumulated more socioenvironmental risks for schizophrenia by age 18. However, associations between socioenvironmental risks and psychotic experiences mostly remained significant after covariate adjustment for genetic risk.


Genetic risk is correlated with socioenvironmental risk for schizophrenia during upbringing, but the associations between socioenvironmental risk and adolescent psychotic experiences appear, at present, to exist above and beyond this gene-environment correlation.

Microbes, memory and moisture: Predicting microbial moisture responses and their impact on carbon cycling


Soil moisture is a major driver of microbial activity and thus, of the release of carbon (C) into the Earth's atmosphere. Yet, there is no consensus on the relationship between soil moisture and microbial respiration, and as a result, moisture response functions are a poorly constrained aspect of C models. In addition, models assume that the response of microbial respiration to moisture is the same for all ecosystems, regardless of climate history, an assumption that many empirical studies have challenged. These gaps in understanding of the microbial respiration response to moisture contribute to uncertainty in model predictions. We review our understanding of what drives microbial moisture response, highlighting evidence that historical precipitation can influence both responses to moisture and sensitivity to drought. We present two hypotheses, the ‘climate history hypothesis’, where we predict that baseline moisture response functions change as a function of precipitation history, and the ‘drought legacy hypothesis’, in which we suggest that the intensity and frequency of historical drought have shaped microbial communities in ways that will control moisture responses to contemporary drought. Underlying mechanisms include biological selection and filtering of the microbial community by rainfall regimes, which result in microbial traits and trade-offs that shape function. We present an integrated modelling and empirical approach for understanding microbial moisture responses and improving models. Standardized measures of moisture response (respiration rate across a range of moistures) and accompanying microbial properties are needed across sites. These data can be incorporated into trait-based models to produce generalized moisture response functions, which can then be validated and incorporated into conventional and microbially explicit ecosystem models of soil C cycling. Future studies should strive to analyse realistic moisture conditions and consider the role of environmental factors and soil structure in microbial response. Microbes are the engines that drive C storage and are sensitive to changes in rainfall. A greater understanding of the factors that govern this sensitivity could be a key part of improving predictions of soil C dynamics, climate change and C-climate feedbacks. Read the free Plain Language Summary for this article on the Journal blog.

Climate-Driven Legacies in Simulated Microbial Communities Alter Litter Decomposition Rates


The mechanisms underlying diversity-functioning relationships have been a consistent area of inquiry in biogeochemistry since the 1950s. Though these mechanisms remain unresolved in soil microbiomes, many approaches at varying scales have pointed to the same notion—composition matters. Confronting the methodological challenge arising from the complexity of microbiomes, this study used the model DEMENTpy, a trait-based modeling framework, to explore trait-based drivers of microbiome-dependent litter decomposition. We parameterized DEMENTpy for five sites along a climate gradient in Southern California, United States, and conducted reciprocal transplant simulations analogous to a prior empirical study. The simulations demonstrated climate-dependent legacy effects of microbial communities on plant litter decomposition across the gradient. This result is consistent with the previous empirical study across the same gradient. An analysis of community-level traits further suggests that a 3-way tradeoff among resource acquisition, stress tolerance, and yield strategies influences community assembly. Simulated litter decomposition was predictable with two community traits (indicative of two of the three strategies) plus local environment, regardless of the system state (transient vs. equilibrium). Although more empirical confirmation is still needed, community traits plus local environmental factors (e.g., environment and litter chemistry) may robustly predict litter decomposition across spatial-temporal scales. In conclusion, this study offers a potential trait-based explanation for climate-dependent community effects on litter decomposition with implications for improved understanding of whole-ecosystem functioning across scales.

Cover page of Finding the perfect match: Fingerprint expertise facilitates statistical learning and visual comparison decision-making.

Finding the perfect match: Fingerprint expertise facilitates statistical learning and visual comparison decision-making.


Forensic feature-comparison examiners compare-or "match"-evidence samples (e.g., fingerprints) to provide judgments about the source of the evidence. Research demonstrates that examiners in select disciplines possess expertise in this task by outperforming novices-yet the psychological mechanisms underpinning this expertise are unclear. This article investigates one implicated mechanism: statistical learning, the ability to learn how often things occur in the environment. This ability is likely important in forensic decision-making as samples sharing rarer statistical information are more likely to come from the same source than those sharing more common information. We investigated 46 fingerprint examiners' and 52 novices' statistical learning of fingerprint categories and application of this knowledge in a source-likelihood judgment task. Participants completed four measures of their statistical learning (frequency discrimination judgments, bounded and unbounded frequency estimates, and source-likelihood judgments) before and after familiarization to the "ground-truth" category frequencies. Compared to novices, fingerprint examiners had superior domain-specific statistical learning across all measures-both before and after familiarization. This suggests that fingerprint expertise facilitates domain-specific statistical learning-something that has important theoretical and applied implications for the development of training programs and statistical databases in forensic science. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Cover page of Differential Response of Bacterial Microdiversity to Simulated Global Change.

Differential Response of Bacterial Microdiversity to Simulated Global Change.


Global change experiments often observe shifts in bacterial community composition based on 16S rRNA gene sequences. However, this genetic region can mask a large amount of genetic and phenotypic variation among bacterial strains sharing even identical 16S regions. As such, it remains largely unknown whether variation at the sub-16S level, sometimes termed microdiversity, responds to environmental perturbations and whether such changes are relevant to ecosystem processes. Here, we investigated microdiversity within Curtobacterium, the dominant bacterium found in the leaf litter layer of soil, to simulated drought and nitrogen addition in a field experiment. We first developed and validated Curtobacterium-specific primers of the groEL gene to assess microdiversity within this lineage. We then tracked the response of this microdiversity to simulated global change in two adjacent plant communities, grassland and coastal sage scrub (CSS). Curtobacterium microdiversity responded to drought but not nitrogen addition, indicating variation within the genus of drought tolerance but not nitrogen response. Further, the response of microdiversity to drought depended on the ecosystem, suggesting that litter substrate selects for a distinct composition of microdiversity that is constrained in its response, perhaps related to tradeoffs in resource acquisition traits. Supporting this interpretation, a metagenomic analysis revealed that the composition of Curtobacterium-encoded carbohydrate-active enzymes (CAZymes) varied distinctly across the two ecosystems. Identifying the degree to which relevant traits are phylogenetically conserved may help to predict when the aggregated response of a 16S-defined taxon masks differential responses of finer-scale bacterial diversity to global change. IMPORTANCE Microbial communities play an integral role in global biogeochemical cycling, but our understanding of how global change will affect microbial community structure and functioning remains limited. Microbiome analyses typically aggregate large amounts of genetic diversity which may obscure finer variation in traits. This study found that fine-scale diversity (or microdiversity) within the bacterial genus Curtobacterium was affected by simulated global changes. However, the degree to which this was true depended on the type of global change, as the composition of Curtobacterium microdiversity was affected by drought, but not by nitrogen addition. Further, these changes were associated with variation in carbon degradation traits. Future work might improve predictions of microbial community responses to global change by considering microdiversity.