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For two competing species, intraspecific competition must exceed interspecific competition for coexistence. To generalize this well-known criterion to multiple competing species, one must take into account both the distribution of interaction strengths and community structure. Here we derive a multispecies generalization of the two-species rule in the context of symmetric Lotka-Volterra competition and obtain explicit stability conditions for random competitive communities. We then explore the influence of community structure on coexistence. Results show that both the most and least stabilized cases have striking global structures, with a nested pattern emerging in both cases. The distribution of intraspecific coefficients leading to the most and least stabilized communities also follows a predictable pattern that can be justified analytically. In addition, we show that the size of the parameter space allowing for feasible communities always increases with the strength of intraspecific effects in a characteristic way that is independent of the interspecific interaction structure. We conclude by discussing possible extensions of our results to nonsymmetric competition.

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as \"feature types\" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt's calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt's outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.

Communities that are very rich in species could persist thanks to the stabilizing role of higher-order interactions, in which the presence of a species influences the interaction between other species. High order interactions maintain species diversity How the tremendous biodiversity observed in nature is maintained is a central question in ecology. Simple models of interacting competitors fail to reproduce the stable persistence of very large ecological communities, while neutral models in which species do not interact and diversity is maintained by immigration and speciation yield unrealistically small fluctuations in population abundance. Using competitive network models, Stefano Allesina and colleagues show that higher order interactions, whereby the presence of one species influences the interaction between other species, allow highly diverse communities to persist in closed systems with a fixed number and identity of species and in more realistic open systems, which gain new species through immigration and speciation. Ecologists have long sought a way to explain how the remarkable biodiversity observed in nature is maintained. On the one hand, simple models of interacting competitors cannot produce the stable persistence of very large ecological communities 1 , 2 , 3 , 4 , 5 . On the other hand, neutral models 6 , 7 , 8 , 9 , in which species do not interact and diversity is maintained by immigration and speciation, yield unrealistically small fluctuations in population abundance 10 , and a strong positive correlation between a species’ abundance and its age 11 , contrary to empirical evidence. Models allowing for the robust persistence of large communities of interacting competitors are lacking. Here we show that very diverse communities could persist thanks to the stabilizing role of higher-order interactions 12 , 13 , in which the presence of a species influences the interaction between other species. Although higher-order interactions have been studied for decades 14 , 15 , 16 , their role in shaping ecological communities is still unclear 5 . The inclusion of higher-order interactions in competitive network models stabilizes dynamics, making species coexistence robust to the perturbation of both population abundance and parameter values. We show that higher-order interactions have strong effects in models of closed ecological communities, as well as of open communities in which new species are constantly introduced. In our framework, higher-order interactions are completely defined by pairwise interactions, facilitating empirical parameterization and validation of our models.

Defects in the RAS small G protein or its associated network of regulatory proteins that disrupt GTPase cycling are a major cause of cancer and developmental RASopathy disorders. Lack of robust functional assays has been a major hurdle in RAS pathway-targeted drug development. We used NMR to obtain detailed mechanistic data on RAS cycling defects conferred by oncogenic mutations, or full-length RASopathy-derived regulatory proteins. By monitoring the conformation of wild-type and oncogenic RAS in real-time, we show that opposing properties integrate with regulators to hyperactivate oncogenic RAS mutants. Q61L and G13D exhibited rapid nucleotide exchange and an unexpected susceptibility to GAP-mediated hydrolysis, in direct contrast with G12V, indicating different approaches must be taken to inhibit these oncoproteins. An NMR methodology was established to directly monitor RAS cycling by intact, multidomain proteins encoded by RASopathy genes in mammalian cell extracts. By measuring GAP activity from tumor cells, we demonstrate how loss of neurofibromatosis type 1 (NF1) increases RAS-GTP levels in NF1-derived cells. We further applied this methodology to profile Noonan Syndrome (NS)-derived SOS1 mutants. Combining NMR with cell-based assays allowed us to differentiate defects in catalysis, allosteric regulation, and membrane targeting of individual mutants, while revealing a membrane-dependent compensatory effect that attenuates dramatic increases in RAS activation shown by Y337C, L550P, and I252T. Our NMR method presents a precise and robust measure of RAS activity, providing mechanistic insights that facilitate discovery of therapeutics targeted against the RAS signaling network.

International collaboration is becoming increasingly important for the advancement of science. To gain a more precise understanding of how factors such as international collaboration influence publication success, we divide publication success into two categories: journal placement and citation performance. Analyzing all papers published between 1996 and 2012 in eight disciplines, we find that those with more countries in their affiliations performed better in both categories. Furthermore, specific countries vary in their effects both individually and in combination. Finally, we look at the relationship between national output (in papers published) and input (in citations received) over the 17 years, expanding upon prior depictions by also plotting an expected proportion of citations based on Journal Placement. Discrepancies between this expectation and the realized proportion of citations illuminate trends in performance, such as the decline of the Global North in response to rapidly developing countries, especially China. Yet, most countries' show little to no discrepancy, meaning that, in most cases, citation proportion can be predicted by Journal Placement alone. This reveals an extreme asymmetry between the opinions of a few reviewers and the degree to which paper acceptance and citation rates influence career advancement.

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures.

This essay examines the role of Anglo Protestant missions in the Persian Gulf in racializing “the Moslem world” for the emergent white world order at the beginning of the 20th century. More specifically, I consider the way Protestant missionaries extracted knowledge about Islam, racializing “the Moslem world” as a civilizational “unit” devoid of energetic life—and therefore incompatible with the modern world—even as they simultaneously mediated the rise of oil extraction along the Persian Gulf in that same period. Extraction was not only evident in the material relations of empire, but also in the way Protestant missionary discourse shaped “the Muslim world” into a racial unit in need of management and optimization. I consider two energetic grammars used by Protestant missionaries to signify the changes occurring in “the Moslem World”, namely, Samuel Zwemer’s use of “disintegration” and Basil Mathews use of “ferment”. I argue that it was in these material and discursive entanglements of oil extraction where knowledge about Islam became an important tool of European colonial governance, and where energetic grammars of religion became critical to the biopolitical production and management of racialized Muslim populations.

Black, Indigenous, and Persons of Color (BIPOC) autistic transition-aged youth (TAY) report lower rates of competitive employment compared to White autistic TAY and even greater deficits with social skills associated with positive job interviewing. A virtual job interviewing program was adapted to support and improve the job interviewing skills of autistic TAY. The current study evaluates the effectiveness of an efficacious virtual interview training program on the job interview skills, interview anxiety, and likeliness to be hired, for a subsample of 32 BIPOC autistic TAY, ages 17–26 years old from a previous randomized control trial of the program. Bivariate analyses were used to evaluate between-group differences at pre-test related to background characteristics, and whether Virtual Interview Training for Transition-Age Youth (VIT-TAY) was associated with changes between pre-test and post-test measures of job interview skills. Additionally, a Firth logistic regression was conducted to examine the relationship between VIT-TAY and competitive integrative employment at 6 months, covarying for fluid cognition, having ever had a job interview, and baseline employment status. Participants receiving pre-employment services (Pre-ETS) and virtual interview training had better job interview skills (F = 12.7, ρ < .01; η ρ 2  = .32), lower job interview anxiety (F = .3.96, ρ < .05; η ρ 2  = .12), and a higher likeliness of receiving employment (F = 4.34, ρ < .05; η ρ 2  = .13 at the 6-month follow up compared to participants that only had Pre-ETS. Findings from this study suggest that virtual interview training for TAY is effective for BIPOC autistic TAY in improving their interview skills to gain competitive employment and lower their job interview anxiety.