98%
921
2 minutes
20
Conservation translocations, anthropogenic movements of species to prevent their extinction, have increased substantially over the last few decades. Although multiple species are frequently moved to the same location, current translocation guidelines consider species in isolation. This practice ignores important interspecific interactions and thereby risks translocation failure. We model three different two-species systems to illustrate the inherent complexity of multispecies translocations and to assess the influence of different interaction types (consumer-resource, mutualism, and competition) on translocation strategies. We focus on how these different interaction types influence the optimal founder population sizes for successful translocations and the order in which the species are moved (simultaneous or sequential). Further, we assess the effect of interaction strength in simultaneous translocations and the time delay between translocations when moving two species sequentially. Our results show that translocation decisions need to reflect the type of interaction. While all translocations of interacting species require a minimum founder population size, which is demarked by an extinction boundary, consumer-resource translocations also have a maximum founder population limit. Above the minimum founder size, increasing the number of translocated individuals leads to a substantial increase in the extinction boundary of competitors and consumers, but not of mutualists. Competitive and consumer-resource systems benefit from sequential translocations, but the order of translocations does not change the outcomes for mutualistic interaction partners noticeably. Interspecific interactions are important processes that shape population dynamics and should therefore be incorporated into the quantitative planning of multispecies translocations. Our findings apply whenever interacting species are moved, for example, in reintroductions, conservation introductions, biological control, or ecosystem restoration.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1890/15-0409 | DOI Listing |
Proc Natl Acad Sci U S A
September 2025
Molecular Imaging Program at Stanford, Department of Radiology, School of Medicine, Stanford University, Palo Alto, CA 94304.
The biophysical properties of single cells are crucial for understanding cellular function and behavior in biology and medicine. However, precise manipulation of cells in 3-D microfluidic environments remains challenging, particularly for heterogeneous populations. Here, we present "Electro-LEV," a unique platform integrating electromagnetic and magnetic levitation principles for dynamic 3-D control of cell position during separation.
View Article and Find Full Text PDFPLOS Digit Health
September 2025
Department of Dermatology, Stanford University, Stanford, California, United States of America.
Large Language Models (LLMs) are increasingly deployed in clinical settings for tasks ranging from patient communication to decision support. While these models demonstrate race-based and binary gender biases, anti-LGBTQIA+ bias remains understudied despite documented healthcare disparities affecting these populations. In this work, we evaluated the potential of LLMs to propagate anti-LGBTQIA+ medical bias and misinformation.
View Article and Find Full Text PDFG3 (Bethesda)
September 2025
Department of Biology, Stanford University, Stanford, CA 94305, USA.
The ψ directionality index was introduced by Peter & Slatkin (Evolution 67: 3274-3289, 2013) to infer the direction of range expansions from single-nucleotide polymorphism variation. Computed from the joint site frequency spectrum for two populations, ψ uses shared genetic variants to measure the difference in the amount of genetic drift experienced by the populations, associating excess drift with greater distance from the origin of the range expansion. Although ψ has been successfully applied in natural populations, its statistical properties have not been well understood.
View Article and Find Full Text PDFDelirium Commun
January 2025
Department of Nursing, Nursing Research Division, Mayo Clinic.
Background: Family participation in the delivery of nonpharmacological measures has shown in past studies to prevent 17-75% of incident delirium. A scalable and sustainable method to partner with family (i.e.
View Article and Find Full Text PDFNAR Cancer
September 2025
Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
Personalized treatment selection is crucial for cancer patients due to the high variability in drug response. While actionable mutations can increasingly inform treatment decisions, most therapies still rely on population-based approaches. Here, we introduce neural interaction explainable AI (NeurixAI), an explainable and highly scalable deep learning framework that models drug-gene interactions and identifies transcriptomic patterns linked with drug response.
View Article and Find Full Text PDF