The southern pine beetle, Dendroctonus frontalis Zimmermann (Coleoptera: Scolytidae), is an economically important pest of pine forests in the southern United States (Price et al. 1992). This native bark beetle is able to attack and kill living trees, typically loblolly (Pinus taeda L.) or shortleaf (Pinus echinata Mill.) pine, through a process of mass attack coordinated by pheromones emitted by the beetle (Payne 1980). During the attack process, thousands of beetles bore through the outer bark of the tree and begin constructing galleries in the phloem layer. Trees can respond to beetle attack by exuding resin from a network of ducts, but the large number of simultaneous attacks usually overcomes this defense, literally draining the resin from the tree. Oviposition and brood development then occur in the girdled (and ultimately dead) tree. Once a tree is fully colonized the attack process shifts to adjacent trees, often resulting in a cluster of freshly attacked trees, trees containing developing brood, and dead and vacated trees (Coulson 1980). These infestations can range in size from a single tree to tens of thousands, although the latter only occur in areas where no control methods are applied. Approximately six generations can be completed in a year in the southern United States (Ungerer et al. 1999). Like many other forest insect pests, D. frontalis populations are characterized by a considerable degree of fluctuation. The longest time series available are Texas Forest Service records of infestations in southeast Texas since 1958 (figure 5.la). These data suggest that the fluctuations have at least some periodic component, with major outbreaks occurring at intervals of 7-9 years (1968, 1976, 1985, and 1992). A variety of different analyses, including standard time series analysis and response surface methodology (Turchin 1990, Turchin and Taylor 1992), suggest that D.frontalis dynamics are indeed cyclic and appear governed by some kind of delayed negative feedback acting on population growth (see chapter 1). This effect can be seen by plotting the realized per-capita rate of growth (R-values) over a year against population density in the previous year (figure 5.1b).

Predator-prey interactions are intricately linked to ecological systems, from micro-organisms to large animals. Most predator-prey studies use simplified pairwise interactions, constraining our ability to identify general principles. Here, predator-prey choices are examined across scales and levels of environmental complexity. We review current knowledge and emphasize the diversity and complexity of predator-prey systems, point to challenges in integrating them, and propose a framework that could benefit predictive modeling for ecosystem functioning and resilience. To do so, we compare the tools, mechanisms, and strategies deployed by micro- and macro- predators and prey defenses to show that commonalities become identifiable, and suggest structural and functional links between micro- and macro-scales. This provides arguments for both descriptive, and mathematical models. We propose that the use of microbial predators like the Bdellovibrio and like organisms can greatly advance the integration of experimental and mathematical modeling research, as they can provide robust empirical observations of predator-prey interactions tested under multiple conditions and levels of complexity. This facilitates model development, in turn leading to new hypotheses. We conclude by showing examples of current developments, that predator-prey interaction-based knowledge has the potential to provide novel medical tools and to improve environmental and agricultural management. Predator and prey are thus intertwined by interactions with other members of their food web and various environmental factors, resulting in both experiencing regular or irregular cycles in size and density. This complexity has led both experimenters and theorists to conduct most studies on predator–prey pairs in isolation, and we note that the term “predator–prey” projects a pairwise arrangement, while in most ecosystems significant interplay occurs among multiple predators and multiple prey. They were discovered by serendipity and rapidly shown to have unique attributes (Stolp and Starr 1963 ) As described here, they continue to provide fascinating features to researchers. The BALO’s life cycle is divided into two main phases: a motile attack phase (AP), when the predator actively searches for prey, and a prey-dependent growth and replication phase (GP). Indeed, while animals—even unicellular microbes, among the protists—feed on numerous prey individuals to mature to replicative size, predators like BALOs use a single prey cell to complete their life cycle, i.e. grow and replicate ( Fig. S1 ). This point is further treated in the following modeling section. Figure 1 Strategies employed by predators to attack prey, at the microscopic (upper panel), and the macroscopic (lower panel) scales, showing parallelisms. In both panels, the left square pictures depict group predation; the right squares show pictures of single predators catching prey of various sizes, and different means of capture. Upper panel: I. This cross-disciplinary approach provides a blueprint for transitioning from purely descriptive observations of microbial life cycles to predictive, data-driven models. Mathematical frameworks for predator–prey dynamics are remarkably consistent across organismal scales. The Lotka–Volterra equations serve as the structural “skeleton” for modeling these interactions in both macro and micro ecosystems, as fundamental principles of exponential and logistic growth apply universally. For instance, similar functional responses can capture density-dependent predation regardless of the organisms' size (Wang et al. 2016 , Baker et al. 2017 ). Varon and Zeigler (Zath et al. In macro-organisms, the phenomenon of reduced birth rate resulting from predator-induced fear (Wang et al. 2016 ) is well documented. In micro-organisms, similar behavioral responses are either nonexistent or less complex. Furthermore, in models of macro-organisms, age structure is important because population dynamics are influenced by the life phases of individual members (Murray 2007 ). On the other hand, since microbial populations are more homogeneous and have shorter life cycles, scientists typically exclude explicit age structure. Microbial models may require additional compartments for predator–prey complexes (e.g. Periodic time-series of prey and predator (left) and phase-portrait (right) for solution of model (11) with initial conditions and parametric values . In right sub-figure, solution started near the equilibrium point (red dot) moves away from it and converge to stable periodic cycle. D. A motif for the food chain model (12), highlighting key processes: basal prey growing logistically, middle predator intake from basal prey, further consumed by the top predator, and predators mortality. Arrows indicate interactions; “+” and “−” mark net effects. Wolves usually prey on large ungulates; however, during summer, many of them prey primarily on juveniles or on small prey due to their availability and vulnerability (Palacios and Mech 2011 ). As the more abundant/preferred prey gets exhausted, the predator can shift its feeding to alternative prey, thus preventing starvation, with an additional stabilizing effect on the whole system (Blasius et al. 1999 ). This is well exemplified in the hare-lynx cycle, where, though changes in the abundance of both populations between years are erratic, the overall change in According to the life-dinner principle, prey bear larger fitness costs than predators (Dawkins and Krebs 1979 ); the selection pressure on the prey to avoid its predator is higher than that on the predator to successfully catch its prey because the former event mostly costs the life of the prey while the latter only results in loss of a “dinner” (Inman 2022 ). Therefore, in nature, prey may make significant investments in defense mechanisms to evade predators. Both in animals and micro-organisms, body morphology plays an important defensive role.

Chronobiology and agroecosystems. Influences of seasonal changes on the circadian system were observed in studies of feeding activity in four species of ducks and of feeding and locomotor activity in two species of mammals kept in captivity in the natural LD cycle. Feeding and locomotor oscillators had different sensitivities to exogenous synchronizers. Changes from a circadian to an ultradian feeding rhythm occurred as a result of courtship and breeding behavior in two duck species. Different phases of feeding rhythms in ducks were shown to have important ecological consequences for establishing the species in appropriate time niches in agricultural wetlands. Detailed study of annual, seasonal and daily rhythms of feeding and locomotion measured simultaneously in captivity and in free-ranging animals is necessary to elucidate the structure and function of food chains and webs. Activity rhythms of both producers and consumers (predators and prey) during daily and annual cycles is of special concern. This contribution of chronobiology will further our understanding of the niche of selected species in agricultural ecosystems.

Human disturbances are modifying animal behavior in ecosystems worldwide, with the potential to reshape species interactions. For instance, human-induced shifts in diel activity may disrupt the alignment of daily activity patterns between interacting species and destabilize temporal niche partitioning. To test this hypothesis, we leverage a global meta-analysis on the effects of human disturbance on diel activity and overlap of 480 mammalian predator-prey and intraguild predator dyads from 57 studies. We demonstrate that human disturbance has no overall effect on temporal overlap. Instead, the body mass ratios between dominant species and subordinate species shape the influence of human disturbance. When subordinates are larger than dominant species, humans compress the temporal niche (i.e., higher diel overlap), but when dominant species are larger than subordinate species, humans expand the temporal niche (i.e., lower diel overlap). These results suggest that larger bodied mammals "lose" the temporal predator-prey response race under human disturbance, with large predators experiencing less overlap with their prey, and large prey facing more overlap with their predators. As the human footprint expands globally, we can expect continued alterations to the animal temporal niche, with consequences for species interactions, population persistence, community structure, and evolutionary dynamics. When subordinates are larger than dominant species, humans compress the temporal niche (i.e., higher diel overlap), but when dominant species are larger than subordinate species, humans expand the temporal niche (i.e., lower diel overlap). These results suggest that larger bodied mammals “lose” the temporal predator–prey response race under human disturbance, with large predators experiencing less overlap with their prey, and large prey facing more overlap with their predators. As the human footprint expands globally, we can expect continued alterations to the animal temporal niche, with consequences for species interactions, population persistence, community structure, and evolutionary dynamics. Subject terms: Behavioural ecology, Community ecology Humans alter the daily timing of animal activity, potentially reshaping predator–prey interactions. This meta-analysis reveals that larger species tend to “lose” under human disturbance, with large predators overlapping less with their prey, and large prey overlapping more with their predators. Results Human disturbance has a limited effect on the overall temporal overlap Phylogenetic multilevel meta-analytic models revealed that, overall, dominant–subordinate dyads displayed similar degrees of temporal overlap in low and high human disturbance conditions ( β = 0.014 [95% CI = −0.27, 0.29], p = 0.92; Fig. 1B ; heterogeneity in methods). This result held when dyads were split into predator–prey ( β = 0.026 [95% CI = −0.30, 0.35], p = 0.88; Fig. 1C ) and intraguild predator dyads ( β = −0.01 [95% CI = −0.39, 0.37], p = 0.96). The effects of disturbance on temporal partitioning were mediated by the body mass of dominant and subordinate species: under human disturbance, whichever species is larger tends to “lose” the temporal “response race,” in which predators seek to maximize overlap with prey and prey seek to minimize overlap with predators 70 , 71 . When Continued systematic approaches to understanding the mechanisms underlying changing species interactions in the Anthropocene are therefore crucial to conserving life on Earth. Methods Literature searching and screening To identify relevant studies for our meta-analysis, we searched Web of Science Core Collection (WoS), Google Scholar, and Proquest (Thesis and Dissertations only) 91 with a string of search terms that captured the interactions between terrestrial mammalian predators and prey in space and time and a variety of human disturbances identified from initial literature searches and key works in the field 9 , 14 . To train the active learning model, we pre-selected 10 relevant and irrelevant articles from the second search. We also included data from one thesis that was known to the authors but not identified in the ProQuest Thesis and Dissertation search. Inclusion and exclusion criteria We screened titles and abstracts from bibliometric records to identify empirical studies on how human disturbance shapes temporal overlap between predator–prey or intraguild predator dyads. Studies were retained for the full-text screening stage if they mentioned temporal activity, overlap, or partitioning between predators or predator and prey and any kind of human disturbance in the abstract, or if they referred to any predator–prey or intraguild predator interaction in the title. The training data for this model included a large compilation of mammalian-predator-prey records from scientific literature and trait data from EltonTraits 82 . We used the same dataset to calculate a second interaction intensity metric using a simpler trait-based model in which interaction probability depends on the ratio of predator to prey body mass, which has been shown to shape predator–prey interactions across food webs 99 . The random effects included in our models did not account for all non-independence among sampling variances (i.e., correlations that emerge due to the same individual and populations being included in more than one effect size, for example, shared predators or prey across different dominant–subordinate dyads). To account for the remaining non-independence, we created a variance-covariance (VCV) matrix to add to our meta-analytic models. This matrix assumes that sampling variance of the same species from the same studies have a correlation r = 0.5 111 , 112 . Code generated in this study are provided in the Supplementary Information. Code used in this study are available in the Zenodo ( https://zenodo.org/records/17546952 ) and Github ( https://eamonn-wooster.github.io/Disturbed_predator_prey/ ).

factor in the induction of sex in protists. Several protists synchronize their life cycles (namely the formation or release of gametes) according to environmental A protist ( PROH-tist) or protoctist is any eukaryotic organism that is not an animal, land plant, or fungus. Protists do not form a natural group, or clade, but are a paraphyletic group encompassing the entire eukaryote tree of life, from which land plants, animals, and fungi evolved. They are primarily single-celled, exhibiting a wide range of forms such as amoebae, ciliates, thick-walled microa Fl… Although traditionally presumed to be asexual, protists are capable of sexual reproduction, and can exhibit diverse and complex life cycles with different generations and life stages. Protists are abundantly present in all ecosystems, including extreme habitats, as important components of the biogeochemical cycles and trophic webs. As producers, they are responsible for a large portion of global primary production and carbon fixation. As consumers and decomposers, they regulate fungal and bacterial populations, and release nutrients to other trophic levels. Some form mutualistic relationships with other protists or animals such as corals and termites. Others are important parasites. Because of this definition by exclusion, protists compose a paraphyletic group from which the ancestors of those three kingdoms evolved. As such, there is no unique trait that unifies all protists yet excludes non-protists. Still, together they exhibit a remarkable diversity of life cycles, trophic levels, modes of locomotion, and cellular structures that dwarfs those seen in "higher" eukaryotes. A less popular view is that protists are defined as exclusively single-celled eukaryotes, but this disregards the various transitions to multicellularity among protists. The distinction between protists and other kingdoms was blurry before genetic analysis. Flagellates are the most common protists, and very likely the most abundant eukaryotes on Earth. They move using one or more whip-like structures called flagella. Most are heterotrophic (known as zooflagellates), feeding on bacteria or other organisms, ranging from filter feeders like choanoflagellates to active predators like provorans. Many are photo- or mixotrophic (known as phytoflagellates) and are studied as algae, like the dinoflagellates. Amoebae are known for their often flexible shape and ability to form extensions of the cytoplasm known as pseudopodia. These extensions come in various forms, such as lobose (blunt, rounded, as in Amoeba), filose (thin, tapering, as in cercozoans), or reticulose (branching networks, as in foraminifers). Some, called axopodia, take the shape of radiating projections supported by microtubules, characteristic of heliozoa and radiolaria. Some amoebae can grow to sizes visible to the naked eye, reaching up to 20 cm. Amoeboflagellates can produce both pseudopodia and flagella within the same life cycle. Ciliates have larger cells with two types of nuclei and rows of small flagella, known as cilia. They are often at the top of the microbial food web. Symbiontid euglenozoans and select ciliates have sulfur-oxidizing bacteria living as epibionts on their surfaces. Similarly, breviates have hydrogen-oxidizing epibiotic bacteria. Metamonads, particularly parabasalids and oxymonads found in the hindgut of termites, typically host methanogenic archaea as epi- or endobionts. Some rare associations involve prokaryotes that defend the protist Haplo-diploid cycle (as in land plants): there are two alternating generations of individuals. One, the diploid agamont (or sporophyte), undergoes meiosis to generate haploid cells (called spores) that develop into the other generation, the haploid gamont (or gametophyte). The gamont then generates gametes by mitosis, which fuse to form the diploid zygote that develops into the agamont. This is the case for many foraminifera and many algae. Depending on the relative growth and lifespan of one generation compared to the other, life cycles may be haploid-dominant, diploid-dominant, or with equally dominant generations. Brown algae exhibit the full range of these modes. Many red algae have a three-generational cycle with a carposporophyte, whose spores germinate into a tetrasporophyte, whose spores develop into the gametophyte. ==== Factors inducing sexual cycles ==== Free-living protists tend to reproduce sexually under stressful conditions, such as starvation or heat shock. Oxidative stress, which leads to DNA damage, also appears to be an important factor in the induction of sex in protists. Several protists synchronize their life cycles (namely the formation or release of gametes) according to environmental factors such as nutrient or light levels, resulting in synchronization with the day-night cycle, the lunar cycle, or the seasons. The malaria agent Plasmodium falciparum synchronizes its life cycle with the host's levels of melatonin. === Cycles in pathogenic protists === Pathogenic protists tend to have extremely complex life cycles that involve multiple forms of the organism, some of which reproduce sexually and others asexually. The stages that feed and multiply inside the host are generally known as trophozoites (from Greek trophos 'nutrition' and zoia 'animals'), but the names of each stage vary depending on the protist group (e.g., sporozoites and merozoites in apicomplexans; primary and secondary zoospores in phytomyxeans). The advanced sterols of modern eukaryotes, although metabolically expensive, likely provided numerous advantages through increased membrane flexibility, such as resilience to osmotic shock during dessication-rehydration cycles, extreme temperatures, oxidative damage, and UV light exposure, allowing them to colonize diverse and harsh environments (e.g., mudflats, rivers, agitated shorelines and land). In contrast, stem eukaryotes remained in low-oxygen marine waters, although at higher abundances.

that the relative durations of the predator and prey life cycles can have important effects on dynamics … stable cycles (Brown, 1984). Time delays and more elaborate stage structure can give rise to cycles with … models, produces cycles only for a very particular set of assumptions. This lack of cycles holds true for

recip- rocal linkage between predator and prey; yet, both predator and prey interact with a host … occurs between ecologically similar species. In a number of instances, ecologically similar species … herbivores; and prey, hosts, or plants. For simplicity we often refer to "predators" and "prey,"