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1. Environmental “knees” and “wiggles” as strong stabilizers of species’ range limits set by interspecific competition

   https://doi.org/10.1371/journal.pcbi.1014336  

   Shirani, Farshad; Freeman, Benjamin G (June 2026, PLOS Computational Biology)

   Blyuss, Konstantin B (Ed.)

   Whether interspecific competition is a major contributing factor to setting species’ range limits has been debated for a long time. Theoretical studies have proposed that the interactions between interspecific competition and disruptive gene flow along an environmental gradient can halt range expansion of ecologically similar species where they meet. However, the stability of such range limits has not been well addressed. We use a deterministic mathematical model of adaptive range evolution over a continuous habitat to show that the range limits set by interspecific competition are unlikely to be evolutionarily stable if the environmental optima for fitness-related traits vary (almost) linearly in space. That is, in a linear environment without a dispersal barrier or a third (or more) species, the range borders formed between two competing species constantly move towards the weaker species. We demonstrate that environmental nonlinearities such as “knees” and “wiggles”—wherein an isolated sharp change or a step-like change occurs in the steepness of a trait optimum—can strongly stabilize competitively formed range limits. The stabilization mechanism relies on the contrast that such nonlinearities create in the level of disruptive gene flow to the peripheral population of each species, and succeeds when an additional process, such as Allee effects, prevents the establishment of an infinitesimal population in the presence of an abundant competitor. We show that the stability of the range limits at these nonlinearities is robust against moderate environmental disturbances. Whether strong disturbances such as rapid high-amplitude climate changes can destabilize such range limits depends on how the competitive dominance of the species changes across the nonlinearity. Therefore, our findings underscore the importance of assessing species’ competitive ability when predicting responses to climate change, and identify geographic regions where established range limits are likely to persist as well as regions where shifting limits may eventually stabilize. 

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2. Disentangling nonrandom structure from random placement when estimating β‐diversity through space or time

   https://doi.org/10.1002/ecs2.70061  

   McGlinn, Daniel J; Blowes, Shane A; Dornelas, Maria; Engel, Thore; Martins, Inês S; Shimadzu, Hideyasu; Gotelli, Nicholas J; Magurran, Anne; McGill, Brian J; Chase, Jonathan M (March 2025, Ecosphere)

   Abstract There is considerable interest in understanding patterns of β‐diversity that measure the amount of change in species composition through space or time. Most hypotheses for β‐diversity evoke nonrandom processes that generate spatial and temporal within‐species aggregation; however, β‐diversity can also be driven by random sampling processes. Here, we describe a framework based on rarefaction curves that quantifies the nonrandom contribution of species compositional differences across samples to β‐diversity. We isolate the effect of within‐species spatial or temporal aggregation on beta‐diversity using a coverage standardized metric of β‐diversity (β_C). We demonstrate the utility of our framework using simulations and an empirical case study examining variation in avian species composition through space and time in engineered versus natural riparian areas. The primary strengths of our approach are that it provides an intuitive visual null model for expected patterns of biodiversity under random sampling that allows integrating analyses across α‐, γ‐, and β‐scales. Importantly, the method can accommodate comparisons between communities with different species pool sizes, and it can be used to examine species turnover both within and between meta‐communities. 

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3. Improving Taxonomic Practices and Enhancing Its Extensibility—An Example from Araneology

   https://doi.org/10.3390/d14010005  

   Bond, Jason E.; Godwin, Rebecca L.; Colby, Jordan D.; Newton, Lacie G.; Zahnle, Xavier J.; Agnarsson, Ingi; Hamilton, Chris A.; Kuntner, Matjaž (January 2022, Diversity)

   Planetary extinction of biodiversity underscores the need for taxonomy. Here, we scrutinize spider taxonomy over the last decade (2008–2018), compiling 2083 published accounts of newly described species. We evaluated what type of data were used to delineate species, whether data were made freely available, whether an explicit species hypothesis was stated, what types of media were used, the sample sizes, and the degree to which species constructs were integrative. The findings we report reveal that taxonomy remains largely descriptive, not integrative, and provides no explicit conceptual framework. Less than 4% of accounts explicitly stated a species concept and over one-third of all new species described were based on 1–2 specimens or only one sex. Only ~5% of studies made data freely available, and only ~14% of all newly described species employed more than one line of evidence, with molecular data used in ~6% of the studies. These same trends have been discovered in other animal groups, and therefore we find it logical that taxonomists face an uphill challenge when justifying the scientific rigor of their field and securing the needed resources. To move taxonomy forward, we make recommendations that, if implemented, will enhance its rigor, repeatability, and scientific standards. 

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4. First-passage processes on a filamentous track in a dense traffic: optimizing diffusive search for a target in crowding conditions

   https://doi.org/https://doi.org/10.1088/1742-5468/aaf31d  

   Ghosh, Soumendu; Mishra, Bhavya; Kolomeisky, Anatoly B; Chowdhury, Debashish (December 2018, Journal of statistical mechanics)

   Several important biological processes are initiated by the binding of a protein to a specific site on the DNA. The strategy adopted by a protein, called transcription factor (TF), for searching its specific binding site on the DNA has been investigated over several decades. In recent times the effects obstacles, like DNA-binding proteins, on the search by TF has begun to receive attention. RNA polymerase (RNAP) motors collectively move along a segment of the DNA during a genomic process called transcription. This RNAP trac is bound to affect the diffusive scanning of the same segment of the DNA by a TF searching for its binding site. Motivated by this phenomenon, here we develop a kinetic model where a ‘particle’, that represents a TF, searches for a specific site on a one-dimensional lattice. On the same lattice another species of particles, each representing a RNAP, hop from left to right exactly as in a totally asymmetric simple exclusion process (TASEP) which forbids simultaneous occupation of any site by more than one particle, irrespective of their identities. Although the TF is allowed to attach to or detach from any lattice site, the RNAPs can attach only to the first site at the left edge and detach from only the last site on the right edge of the lattice. We formulate the search as a first-passage process; the time taken to reach the target site for the first time, starting from a well defined initial state, is the search time. By approximate analytical calculations and Monte Carlo (MC) computer simulations, we calculate the mean search time. We show that RNAP traffic rectifies the diffusive motion of TF to that of a Brownian ratchet, and the mean time of successful search can be even shorter than that required in the absence of RNAP traffic. Moreover, we show that there is an optimal rate of detachment that corresponds to the shortest mean search time. 

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5. ALAN: adaptive learning for multi-agent navigation

   https://doi.org/10.1007/s10514-018-9719-4  

   Godoy, Julio; Chen, Tiannan; Guy, Stephen J.; Karamouzas, Ioannis; Gini, Maria (February 2018, Autonomous Robots)

   In multi-agent navigation, agents need to move towards their goal locations while avoiding collisions with other agents and obstacles, often without communication. Existing methods compute motions that are locally optimal but do not account for the aggregated motions of all agents, producing inefficient global behavior especially when agents move in a crowded space. In this work, we develop a method that allows agents to dynamically adapt their behavior to their local conditions. We formulate the multi-agent navigation problem as an action-selection problem and propose an approach, ALAN, that allows agents to compute time-efficient and collision-free motions. ALAN is highly scalable because each agent makes its own decisions on how to move, using a set of velocities optimized for a variety of navigation tasks. Experimental results show that agents using ALAN, in general, reach their destinations faster than using ORCA, a state-of-the-art collision avoidance framework, and two other navigation models. 

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