Animal behavior, like other kinds of phenotypic plasticity, is crucial for survival and reproduction in environments that vary over space and time. Behaviors themselves may need to be flexible when the distributions of environmental conditions themselves change; for example, short-term weather patterns and long-term climate conditions are changing due to human activity, and behavioral flexibility will likely be key to some population persisting during these changes and others going extinct. Thus, measuring this flexibility is key to understanding which species may be resilient to climate change.

Measuring behavioral flexibility is tricky as different studies may define and measure it differently and yet other studies may measure similar kinds of flexibility yet call them different things. This so-called "jingle-jangle" issue suggests that studies can more robustly measure a behavioral trait when they use multiple behavioral tests. An additional issue is that measuring behavioral traits that vary between individuals due to genetic or development effects, often referred to as "personality" traits, requires that those trait differences be repeatable across time and between individuals. 

With these issues in mind, McCune et al. (2019) presented a preregistration for experiments using a population of great-tailed grackles to investigate how behavioral flexibility relates to other important traits that are known to vary across individuals including exploratory behavior, boldness, and persistence and motor diversity in accessing a new food source. Behavioral flexibility is measured by the rate of learning a color associated with reward after first learning an association with a different color. That preregistration was recommended by PCI Ecology (Van Cleve, 2019). Now, McCune et al. (2025) present results from this study and find that only exploration of a novel environment and persistence were statistically repeatable behaviors. Both behaviors were not significantly correlated with behavioral flexibility. However, grackles that were trained to perform better in the color reversal learning task were more exploratory. This association between behavioral flexibility and exploratory tendencies may have evolved in grackles to help them survive in new environments, which they have proven very capable of doing as they have expanded their range in North American over the last 140 years (Wehtje, 2003).

There are a few key features of McCune et al. (2025) to merit recommendation. First, the authors are intent on demonstrating careful behavioral research practices including, as evidenced above, preregistering their hypotheses and predictions and making available both the preregistered and final analysis code. Second, the study demonstrates a thorough attempt to address two aspects that bedevil behavioral research, namely the "jingle-jangle" issue and repeatability of traits across individuals. Even after measuring multiple features of boldness, exploratory, persistence, McCune et al. (2025) find that only a subset of the measured behaviors are repeatable and only a subset of those are associated with behavioral flexibility. This suggests that only thorough studies like McCune et al. (2025) can start to probe difficult to measure behavioral associations that may be key to understanding how species will respond to our changing world.

References

McCune K, Lukas D,  MacPherson M, Logan CJ (2025) Behavioral flexibility is related to exploration, but not boldness, persistence or motor diversity. EcoEvoRxiv, ver.2 peer-reviewed and recommended by PCI Ecology https://doi.org/10.32942/X2H33F

Van Cleve, J. (2019) Probing behaviors correlated with behavioral flexibility. PCI Ecology, 100020. https://doi.org/10.24072/pci.ecology.100020

McCune K, Rowney C, Bergeron L, Logan CJ. (2019) Is behavioral flexibility linked with exploration, but not boldness, persistence, or motor diversity? (http://corinalogan.com/Preregistrations/g_exploration.html) In principle acceptance by PCI Ecology of the version on 27 Mar 2019

Wehtje, W. (2003) The range expansion of the great-tailed grackle (Quiscalus mexicanus Gmelin) in North America since 1880. Journal of Biogeography 30:1593–1607 https://doi.org/10.1046/j.1365-2699.2003.00970.x.

As mankind develops increasingly efficient and productive methods of agriculture and food production, we have reached a point where intensive agriculture threatens several aspects of life on Earth, negatively affecting biodiversity, carbon, nitrogen and phosphorus cycles and water reservoirs, while producing considerable amounts of greenhouse gases (Krebs and Bach, 2018). There was a need to develop farming methods that were friendly to both nature and people, producing good quality, healthy food without destroying the environment. The idea of permaculture, a concept of sustainable agriculture based on methods learned directly from nature, originated in the 1960s, invented and developed by Bruce Charles Mollison and David Holmgren (Mollison and Holmgren 1979, Mollison et al. 1991, Holmgren 2002). Although the idea of permaculture has attracted scientific interest, the representation in published studies is unbalanced in favour of positive ecological and sociological effects, with much less presence of rigorous experimental testing (Ferguson and Lovell 2014, Reiff et al. 2024a).

Reiff et al. (2024b) provided the first large-scale empirical evidence of permaculture production outcomes for Central Europe. Based on results from 11 commercial permaculture sites, situated mostly in Germany but also in Switzerland and Luxembourg, the authors found that food production from permaculture sites was on average comparable to that from conventional and organic agriculture. The authors were very thorough in pointing out the issues that could potentially affect their results and which need further testing.

Among these, the authors highlight the considerable variability between the 11 sites studied, which may suggest that different permacultures should differ in details according to their specificity - an interesting issue that definitely requires further study. The other factor that the authors point out that could have influenced the results and led to an underestimation of the real potential is the age of the permaculture sites. The sites from the study were relatively young, and their potential can be expected to increase with time.

It is important to note that the results are mostly applicable to vegetables, as vegetable production accounted for 94% of production in the permaculture sites (followed by tree crops, 6%, and soft fruit production, 0.5%). There is therefore a need to include other types of crops produced in further studies of this type.

To date, the results informing permaculture food production are urgently needed and should cover the potentially wide range of geographical regions and crops produced. The results of Reiff et al. (2025) show that rigorous testing of this issue is demanding, but the authors provide a very sound "road map" of further steps.      

Literature:

Ferguson R. S. and Lovell S. T. 2014. Permaculture for agroecology: design, movement, practice, and worldview. A review. Agronomy for Sustainable Development 34, 251-274. https://doi.org/10.1007/s13593-013-0181-6

Holmgren D. 2002. Permaculture: Principles & Pathways Beyond Sustainability. Holmgren Design Services, pp. 320.

Krebs J. and Bach S. 2018. Permaculture – scientific evidence of principles for the agroecological design of farming systems. Sustainability 10, 3218, https://doi.org/10.3390/su10093218

Mollison B. C. and Holmgren D. 1979. Permaculture One: A Perennial Agricultural System for Human Settlements. Tagari Publications, pp. 136.

Mollison B. C., Slay, R. M. and Jeeves A. 1991. Introduction to permaculture. Tagari Publications, pp. 198.

Reiff J., Jungkunst H. F., Mauser K. M., Kampel S., Regending S., Rösch V., Zaller J. G. and Entling M. H. 2024a. Permaculture enhances carbon stocks, soil quality and biodiversity in Central Europe. Communications Earth & Environment 5, 305. https://doi.org/10.1038/s43247-024-01405-8

Reiff J., Jungkunst H. F., Antes N. and Entling M. H. 2024b. Crop productivity of Central European Permaculture is within the range of organic and conventional agriculture. bioRxiv, ver.2 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.1101/2024.09.09.611985

Recently, microclimate has gained significant momentum [1], as evidenced by the increasing number of studies and the emergence of a dedicated scientific community coordinating research efforts [2]. Several factors underpin this trend, including advances in technology that have made microclimate monitoring [3] and ecological contextualization [4] more accessible, as well as improvements in computational methods that facilitate modeling at unprecedented scales [5]. But the growing emphasis on microclimate is primarily driven by their ecological relevance, as microclimate represent the actual climate conditions experienced by organisms [1]. This makes them more suitable than macroclimate data for understanding and predicting biodiversity responses to climate change [6]. While macroclimate data remain a common tool in ecology, they often represent generalized climatic conditions over large spatial scales. These data are typically derived from statistical models calibrated on observations collected at meteorological stations [7], which are usually located at 2 meters above the ground in open areas and at elevations compatible with human activities. Such characteristics limit the applicability of macroclimate data for understanding biodiversity responses, particularly at finer spatial scales.

 This is especially true in forest ecosystems, where microclimate results from the filtering of macroclimate conditions by forest habitats [8]. A simple walk in a forest during summer highlights this filtering, with the cooling effect of canopy shading and tree packing being clearly perceptible. If humans can sense these variations, they likely influence forest biodiversity. In fact, microclimates are crucial for defining the thermal niches of understory plant species [9] and understanding plant community reshuffling in response to climate warming [10]. In mountainous areas, topography adds further complexity to microclimates. The drop in temperature with elevation, known as the elevation-temperature lapse rate, is familiar, but topography also drives fine-scale variations [11]. Solar radiation hitting forest varies with aspect and hillshade, creating localized temperature differences. For example, equator-facing slopes receive more sunlight, while west-facing slopes are sunlit during the warmest part of the day. Consequently, in the northern hemisphere, southwest-facing slopes generally exhibit warmer temperatures, longer growing seasons, and shorter snow cover durations [12]. Thus, both topography and forest canopy shape the understory microclimate experienced by organisms in temperate mountainous forests.

 Is biodiversity more influenced by topography- or canopy-induced temperature buffering? While this question may not seem particularly interesting at first glance, understanding the underlying mechanisms of microclimate is crucial for guiding biodiversity conservation decisions in the face of climate change [13]. Poleward-facing slopes, valley bottoms, and dense canopies buffer warm episodes by creating cooler, more humid habitats that can serve as refugia for biodiversity [12]. Both buffering processes are valuable for conservation, but topography-induced buffering is generally more stable over the long term [14]. In contrast, canopy buffering is more vulnerable to human management, disturbances, and the ongoing acceleration of climate change, which is expected to drive tree mortality and lead to canopy opening [15]. Identifying the dominant buffering process in a given area is essential for mapping biodiversity refugia and fully integrating microclimate into conservation strategies. This approach can improve decision-making and actions aimed at promoting biodiversity sustainability in a warming world.

 The work of Borderieux and colleagues [16] offers new insights into this question through an innovative approach. They focus on temperate forests in a watershed in the Vosges Mountains, where they monitor understory temperature and inventory forest plant communities in separate samplings. Aiming to disentangle the effects of topography and forest canopy on understory temperature and its impact on plant communities, the authors deployed a network of temperature sensors using stratified sampling, balanced according to topography (elevation, aspect, and slope) and canopy cover. They then correlated mean annual temperatures (daily mean and maximum) with topographic factors and canopy cover, considering their potential interactions in a linear model. The contribution of each microclimate component was computed, and their effects on temperatures were mapped. These predictions were then confronted to floristic inventories to test whether topography- and canopy-induced temperature variations explained plant diversity and assemblages.

 First, the authors demonstrated that local topographic variations, which determine the amount of solar radiation reaching forests in mountainous areas, outweigh the contribution of canopy shading to understory temperatures. This result is surprising, as many previous studies have emphasized the importance of canopy buffering in shaping forest microclimate conditions [8]. However, these studies mostly focused on lowland areas or large scales, where terrain roughness has less influence. It is also unexpected because the authors observed that canopy cover varies at a smaller scale than aspect or topographic position in their study area, creating habitat heterogeneity that could reasonably drive local temperature variations. Nevertheless, the authors found that aspect, heat load, and topographic position induced more variation in microclimate than canopy filtering, significantly allowing deviations from the expected elevation-temperature lapse rate. Second, the topographic effect on understory temperature propagated to biodiversity. The authors found that topography-induced temperature offset explained plant diversity and composition, while canopy-induced temperature offset did not. Specifically, cold topoclimates harbored 30% more species than the average species richness across the inventoried plots. This increase in species richness was primarily due to an increase in cold-adapted species, highlighting the role of cold topoclimates as refugia.

 It is difficult to assess the extent to which these results are influenced by the specific forest context of the study area chosen by the authors, as there is no clear consensus in previous research regarding the role of topoclimate. For example, Macek et al. (2019) [17] highlighted the predominance of topography in controlling temperature and, consequently, forest community structure in the Czech Republic, while Vandewiele et al. (2023) [18] demonstrated the dominance of canopy control in the German Alps. The forest conditions investigated by Borderieux et al. (2025) were narrow, as they focused mainly on closed forests (more than 80% of the study area and sampling sites exhibiting canopy cover greater than 79%). Given that the canopy buffering effect on temperature increases with canopy cover until plateauing at around 80% [19], this may explain why the authors did not find a strong contribution from the canopy. Nevertheless, the methodology and case presented in their study are both innovative and applicable to other mountainous regions. The work of Borderieux et al. (2025) deserves attention for highlighting a frequently overlooked component of forest microclimate, as canopy filtering is typically regarded as the dominant driver. Topoclimate is a critical factor to consider when protecting cold-adapted forest species in the context of global warming, especially since topographic features are less subject to change than canopy cover. Future research should aim to test this hypothesis across a broader range of forest and topography conditions to identify general patterns, as well as assess the long-term effectiveness of these topographic refugia for biodiversity. It remains unclear whether the cooling effect provided by topoclimate will be sufficient to stabilize climate conditions despite the expected acceleration of climate warming in the coming decades, and whether it will be able to preserve cold-adapted species, which are among the most unique but threatened components of mountain biodiversity.

References

[1] Kemppinen, J. et al. Microclimate, an important part of ecology and biogeography. Global Ecology and Biogeography 33, e13834 (2024). https://doi.org/10.1111/geb.13834

[2] Lembrechts, J. J. et al. SoilTemp: A global database of near-surface temperature. Global Change Biology 26, 6616–6629 (2020). https://doi.org/10.1111/gcb.15123

[3] Wild, J. et al. Climate at ecologically relevant scales: A new temperature and soil moisture logger for long-term microclimate measurement. Agricultural and Forest Meteorology 268, 40–47 (2019). https://doi.org/10.1016/j.agrformet.2018.12.018

[4] Zellweger, F., Frenne, P. D., Lenoir, J., Rocchini, D. & Coomes, D. Advances in Microclimate Ecology Arising from Remote Sensing. Trends in Ecology & Evolution 34, 327–341 (2019). https://doi.org/10.1016/j.tree.2018.12.012

[5] Haesen, S. et al. ForestTemp – Sub-canopy microclimate temperatures of European forests. Global Change Biology 27, 6307–6319 (2021). https://doi.org/10.1111/gcb.15892

[6] Lembrechts, J. J. et al. Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography 28, 1578–1596 (2019). https://doi.org/10.1111/geb.12974

[7] Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37, 4302–4315 (2017). https://doi.org/10.1002/joc.5086

[8] De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat Ecol Evol 3, 744–749 (2019). https://doi.org/10.1038/s41559-019-0842-1

[9] Haesen, S. et al. Microclimate reveals the true thermal niche of forest plant species. Ecology Letters 26, 2043–2055 (2023). https://doi.org/10.1111/ele.14312

[10] Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020). https://doi.org/10.1126/science.aba6880

[11] Rolland, C. Spatial and Seasonal Variations of Air Temperature Lapse Rates in Alpine Regions. Journal of climate, 16(7), 1032-1046 (2003). https://doi.org/10.1175/1520-0442(2003)016%3C1032:SASVOA%3E2.0.CO;2

[12] Rita, A. et al. Topography modulates near-ground microclimate in the Mediterranean Fagus sylvatica treeline. Sci Rep 11, 1–14 (2021). https://doi.org/10.1038/s41598-021-87661-6

[13] Bertrand, R., Aubret, F., Grenouillet, G., Ribéron, A. & Blanchet, S. Comment on “Forest microclimate dynamics drive plant responses to warming”. Science 370, eabd3850 (2020). https://doi.org/10.1126/science.abd3850

[14] Hylander, K., Greiser, C., Christiansen, D. M. & Koelemeijer, I. A. Climate adaptation of biodiversity conservation in managed forest landscapes. Conservation Biology 36, e13847 (2022). https://doi.org/10.1111/cobi.13847

[15] McDowell, N. G. & Allen, C. D. Darcy’s law predicts widespread forest mortality under climate warming. Nature Clim Change 5, 669–672 (2015). https://doi.org/10.1038/nclimate2641

[16] Borderieux, J. et al. Cool topoclimates promote cold-adapted plant diversity in temperate mountain forests. Ecoevorxiv, ver. 3( 2024). Peer-reviewed and recommended by PCI Ecology https://doi.org/10.32942/X2XC8T

[17] Macek, M., Kopecký, M. & Wild, J. Maximum air temperature controlled by landscape topography affects plant species composition in temperate forests. Landscape Ecol 34, 2541–2556 (2019). https://doi.org/10.1007/s10980-019-00903-x

[18] Vandewiele, M. et al. Mapping spatial microclimate patterns in mountain forests from LiDAR. Agricultural and Forest Meteorology 341, 109662 (2023). https://doi.org/10.1016/j.agrformet.2023.109662

[19] Zellweger, F. et al. Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe. Global Ecology and Biogeography 28, 1774–1786 (2019). https://doi.org/10.1111/geb.12991

Community structure is determined by the regional species pool – which for simplicity can be assumed to be filtered through dispersal limitations, abiotic conditions, and species coexistence mechanisms (Cornell & Harrison 2014). This filtering involves macroecological constraints, such as energy and space availability, and assembly rules that determine species composition (Diamond 1975; Weiher & Keddy 1995; Guisan & Rahbek 2011; Hortal et al. 2012). But also by a series of processes that determine species distributions across scales, including biogeographical and stochastic processes (e.g., large-scale dispersal and occupancy dynamics within the landscape) and deterministic niche-based responses to abiotic and biotic conditions, which interact across scales (Soberón 2010; Hortal et al. 2010; Brousseau et al. 2018). These processes collectively determine the persistence of species assemblages within communities. It follows that, to understand the processes determining the structure of these communities it is necessary to combine methods analyse the effects of drivers acting on both species distributions and community responses.

Van de Walle et al. (2025) take this integrative approach. The final revised version of their work combines multivariate techniques (in this case a RDA) and Joint SDMs to model the small-scale distribution and structure of the invertebrate communities inhabiting a series of coastal dunes in Southern England, France, Belgium and the Netherlands. The paper builds upon well-designed stratified field surveys, which allow them to identify variations at different scales, from geographical to local. These high-quality field data, together with the combination of different modelling techniques, allows them to identify both a clear biogeographical zonation in the structure of these communities, and the existence of a series of neat responses of species to the spatial structure and vigour of the tussocks created by the marram grass fixing the sand dunes. Their models also include the body size, feeding guild and phylogenetic relationships between co-occurring species, although their effects are smaller compared to those of biogeographical differences –which, arguably, are determined by differences in the species pool of each dune system, and species responses to the microhabitat conditions created by the tussocks. They can however identify a trade-off between generalist and specialist species within each community.

Note that here I'm using model in the sense of tools for understanding and explaining complex ecological systems, as advocated by Levins (1966). Which is precisely what Van de Walle et al. (2025) do here. By combining techniques tailored to model species distributions and community-level responses, they (we) gain a much improved understanding of how both species pools and the spatial structure of habitats determine the composition of ecological communities. Importantly, Van de Walle et al. (2025) use this knowledge to obtain key insights about how to manage and restore these endangered habitats, thereby proving the value of this kind of integrative approaches.

References

Brousseau, P.-M., Gravel, D., & Handa, I. T. (2018). On the development of a predictive functional trait approach for studying terrestrial arthropods. Journal of Animal Ecology, 87(5), 1209–1220. https://doi.org/10.1111/1365-2656.12834

Cornell, H. V., & Harrison, S. P. (2014). What are species pools and when are they important? Annual Review of Ecology, Evolution, and Systematics, 45(1), 45–67. http://dx.doi.org/10.1146/annurev-ecolsys-120213-091759

Diamond, J. M. (1975). Assembly of species communities. In M. L. Cody & J. M. Diamond (Eds.), Ecology and Evolution of Communities (pp. 342–444). Harvard University Press.

Guisan, A., & Rahbek, C. (2011). SESAM – a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. Journal of Biogeography, 38(8), 1433–1444. https://doi.org/10.1111/j.1365-2699.2011.02550.x

Hortal, J., Roura-Pascual, N., Sanders, N. J., & Rahbek, C. (2010). Understanding (insect) species distributions across spatial scales. Ecography, 33(1). https://doi.org/10.1111/j.1600-0587.2009.06428.x

Hortal, J., de Marco, P., Santos, A. M. C., & Diniz-Filho, J. A. F. (2012). Integrating biogeographical processes and local community assembly. Journal of Biogeography, 39(4). https://doi.org/10.1111/j.1365-2699.2012.02684.x

Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54, 421–431.

Soberón, J. (2010). Niche and area of distribution modeling: A population ecology perspective. Ecography, 33(1), 159–167. https://doi.org/10.1111/j.1600-0587.2009.06074.x 

van de Walle, R., Dahirel, M., Langeraert, W., Benoit, D., Vantieghem, P., Vandegehuchte, M. L., Massol, F., & Bonte, D. (2025). Drivers of plant-associated invertebrate community structure in West-European coastal dunes. BioRxiv, 2024.06.24.600350, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2024.06.24.600350

Weiher, E., & Keddy, P. A. (1995). Assembly rules, null models, and trait dispersion: New questions from old patterns. Oikos, 74(1), 159–164. https://doi.org/10.2307/3545686

The functional response, describing the relation between predator intake rate and prey density, is a pivotal concept to understand foraging behaviour and its consequences for community dynamics. Holling (1959a) introduced three types of functional responses according to their shapes, labelled I, II and III.  The type II, also known as the disc equation (Holling 1959b), has become popular among empiricists and theoreticians alike, due to its ability to describe predator intake saturation. The type III is often used to represent predator switching to other prey species when main prey density is low. 

Although theoretical works identify the linear functional response used in Lotka-Volterra models as a type I, Holling (1959a)’s type I model actually envisioned that at some threshold prey density, the linear increase in predator intake with prey density would give way to an upper predator intake limit, so that Holling’s type I has a rectilinear shape, with an angle joining straight lines. Ecology students can actually see this rectilinear shape reproduced in some texbooks, although not in textbook dynamical models, as they usually transition from Lotka-Volterra models to models with type II response. 

To many, the rectilinear shape of the original type I looks like a historical curiosity: the type II functional response accounts for intake rate saturation with a more convenient smooth function. 

Novak et al. (2025) turn this preconception on its head by first pedagogically showing that Holling’s original type I model can be obtained as a limit case of a variant of the celebrated type II model. The derivation follows up earlier work by Sjöberg (1980), which might be unfamiliar to readers outside aquatic ecology. The often untold assumption of the type II functional response model is that searching and handling prey are two exclusive behavioural processes, with predators that can only handle one prey item at a time. Allowing for several prey items to be handled at once while searching, until the predator reaches n prey items, the original type I functional response emerges as a limit case of the « multiprey » functional response as n goes to infinity. Interestingly, the multiprey response looks a lot like the original type I for large yet doable n. 

Novak et al. (2025) then proceed to look for the prevalence of such multiprey functional response shapes in a large database of functional responses (Uiterwaal et al. 2022). Combining linear type I and multiprey models (the asymptote may not always be visible), they find support for this revised type I hypothesis in about one-third of the cases. Although the type II and III models are still well supported by data, the results do suggest that linearity at low prey density may well be more frequent than one thinks. They complement this analysis by showing that larger predators relative to their prey tend to have larger n in the multiprey response. It is consistent with the hypothesis that the bigger you are relative to your prey, the more prey items you can handle at once.

Finally, Novak et al. (2025) investigate the consequences of the multiprey model for community dynamics. They find overall a richer dynamical behaviour than the Lotka-Volterra type I and common parameterizations of the type II, suggesting that observed linearity in some range of prey density does not necessarily translate in simpler dynamical behaviour. 

Novak et al. (2025) provide here a convincing and pedagogical study showing how seemingly benign behavioural assumptions can in fact profoundly alter the perceived relevance of community dynamics models. As they conclude, their analyses have lessons for future empirical functional response work, which should not necessarily dismiss the type I model and consider perhaps variants to the classical type II and III, as well as for future theoretical analyses, which could generalize this model to multiple prey species, or relax other behavioural assumptions.

References

Holling, C. S. (1959a). The components of predation as revealed by a study of small-mammal predation of the European Pine Sawfly. The Canadian Entomologist, 91(5), 293-320. https://doi.org/10.4039/Ent91293-5 

Holling, C. S. (1959b). Some characteristics of simple types of predation and parasitism. The Canadian Entomologist, 91(7), 385-398. https://doi.org/10.4039/Ent91385-7  

Novak, M., Coblentz, K. E., & DeLong, J. P (2025). In defense of the original Type I functional response: The frequency and population-dynamic effects of feeding on multiple prey at a time. bioRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2024.05.14.594210

Sjöberg, S. (1980). Zooplankton feeding and queueing theory. Ecological Modelling, 10(3-4), 215-225. https://doi.org/10.1016/0304-3800(80)90060-5 

Uiterwaal, S. F., Lagerstrom, I. T., Lyon, S. R., & DeLong, J. P. (2022). FoRAGE database: A compilation of functional responses for consumers and parasitoids. Ecology, 103(7), e3706. https://doi.org/10.1002/ecy.3706