As far back as Clements [1] and Gleason [2], the historical schism between deterministic and stochastic perspectives has divided ecologists. Deterministic theories tend to emphasize niche-based processes such as environmental filtering and species interactions as the main drivers of species distribution in nature, while stochastic theories mainly focus on chance colonization, random extinctions and ecological drift [3]. Although the old days when ecologists were fighting fiercely over null models and their adequacy to capture niche-based processes is over [4], the ghost of that debate between deterministic and stochastic perspectives came back to haunt ecologists in the form of the ‘environment versus space’ debate with the development of metacommunity theory [5]. While interest in that question led to meaningful syntheses of metacommunity dynamics in natural systems [6], it also illustrated how context-dependant the answer was [7]. One of the next frontiers in metacommunity ecology is to identify the underlying drivers of this observed context-dependency in the relative importance of ecological processus [7, 8].
Reflecting on seminal work by Robert MacArthur emphasizing different processes at different spatial scales [9, 10] (the so-called ‘MacArthur paradox’), Chase and Myers proposed in 2011 that a key in solving the deterministic versus stochastic debate was probably to turn our attention to how the relative importance of local processes changes across spatial scales [3]. Scale-dependance is a well-acknowledged challenge in ecology, hampering empirical syntheses and comparisons between studies [11-14]. Embracing the scale-dependance of ecological processes would not only lead to stronger syntheses and consolidation of current knowledge, it could also help resolve many current debates or apparent contradictions [11, 15, 16].
The timely study by Siqueira et al. [17] fits well within this historical context by exploring the relative importance of ecological drift and selection across a gradient of community size (number of individuals in a given community). More specifically, they tested the hypothesis that small communities are more dissimilar among each other because of ecological drift compared to large communities, which are mainly structured by niche selection [17]. That smaller populations or communities should be more affected by drift is a mathematical given [18], but the main questions are i) for a given community size how important is ecological drift relative to other processes, and ii) how small does a community have to be before random assembly dominates? The authors answer these questions using an extensive stream dataset with a community size gradient sampled from 200 streams in two climatic regions (Brazil and Finland). Combining linear models with recent null model approaches to measure deviations from random expectations [19], they show that, as expected based on theory and recent experimental work, smaller communities tend to have higher β-diversity, and that those β-diversity patterns could not be distinguished from random assembly processes [17]. Spatial turnover among larger communities is mainly driven by niche-based processes related to species sorting or dispersal dynamics [17]. Given the current environmental context, with many anthropogenic perturbations leading to reduced community size, it is legitimate to wonder, as the authors do, whether we are moving toward a more stochastic and thus less predictable world with obvious implications for the conservation of biodiversity [17].
The real strength of the study by Siqueira et al. [17], in my opinion, is in the inclusion of stream data from boreal and tropical regions. Interestingly and most importantly, the largest communities in the tropical streams are as large as the smallest communities in the boreal streams. This is where the study should really have us reflect on the notions of context-dependency in observed patterns because the negative relationship between community size and β-diversity was only observed in the tropical streams, but not in the boreal streams [17]. This interesting nonlinearity in the response means that a study that would have investigated the drift versus niche-based question only in Finland would have found very different results from the same study in Brazil. Only by integrating such a large scale gradient of community sizes together could the authors show the actual shape of the relationship, which is the first step toward building a comprehensive synthesis on a debate that has challenged ecologists for almost a century.

References

[1] Clements, F. E. (1936). Nature and structure of the climax. Journal of ecology, 24(1), 252-284. doi: 10.2307/2256278
[2] Gleason, H. A. (1917). The structure and development of the plant association. Bulletin of the Torrey Botanical Club, 44(10), 463-481. doi: 10.2307/2479596
[3] Chase, J. M., and Myers, J. A. (2011). Disentangling the importance of ecological niches from stochastic processes across scales. Philosophical transactions of the Royal Society B: Biological sciences, 366(1576), 2351-2363. doi: 10.1098/rstb.2011.0063
[4] Diamond, J. M., and Gilpin, M. E. (1982). Examination of the “null” model of Connor and Simberloff for species co-occurrences on islands. Oecologia, 52(1), 64-74. doi: 10.1007/BF00349013
[5] Leibold M. A., et al. (2004). The metacommunity concept: a framework for multi‐scale community ecology. Ecology letters, 7(7), 601-613. doi: 10.1111/j.1461-0248.2004.00608.x
[6] Cottenie, K. (2005). Integrating environmental and spatial processes in ecological community dynamics. Ecology letters, 8(11), 1175-1182. doi: 10.1111/j.1461-0248.2005.00820.x
[7] Leibold, M. A. and Chase, J. M. (2018). Metacommunity Ecology. Monographs in Population Biology, vol. 59. Princeton University Press. [8] Vellend, M. (2010). Conceptual synthesis in community ecology. The Quarterly review of biology, 85(2), 183-206. doi: 10.1086/652373
[9] MacArthur, R. H., and Wilson, E. O. (1963). An equilibrium theory of insular zoogeography. Evolution, 17(4), 373-387. doi: 10.1111/j.1558-5646.1963.tb03295.x
[10] MacArthur, R. H., and Levins, R. (1967). The limiting similarity, convergence, and divergence of coexisting species. The American Naturalist, 101(921), 377-385. doi: 10.1086/282505
[11] Viana, D. S., and Chase, J. M. (2019). Spatial scale modulates the inference of metacommunity assembly processes. Ecology, 100(2), e02576. doi: 10.1002/ecy.2576
[12] Chave, J. (2013). The problem of pattern and scale in ecology: what have we learned in 20 years?. Ecology letters, 16, 4-16. doi: 10.1111/ele.12048
[13] Patrick, C. J., and Yuan, L. L. (2019). The challenges that spatial context present for synthesizing community ecology across scales. Oikos, 128(3), 297-308. doi: 10.1111/oik.05802
[14] Chase, J. M., and Knight, T. M. (2013). Scale‐dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. Ecology letters, 16, 17-26. doi: 10.1111/ele.12112
[15] Horváth, Z., Ptacnik, R., Vad, C. F., and Chase, J. M. (2019). Habitat loss over six decades accelerates regional and local biodiversity loss via changing landscape connectance. Ecology letters, 22(6), 1019-1027. doi: 10.1111/ele.13260
[16] Chase, J. M, Gooriah, L., May, F., Ryberg, W. A, Schuler, M. S, Craven, D., and Knight, T. M. (2019). A framework for disentangling ecological mechanisms underlying the island species–area relationship. Frontiers of Biogeography, 11(1). doi: 10.21425/F5FBG40844.
[17] Siqueira T., Saito V. S., Bini L. M., Melo A. S., Petsch D. K. , Landeiro V. L., Tolonen K. T., Jyrkänkallio-Mikkola J., Soininen J. and Heino J. (2019). Community size affects the signals of ecological drift and niche selection on biodiversity. bioRxiv 515098, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/515098
[18] Hastings A., Gross L. J. eds. (2012). Encyclopedia of theoretical ecology (University of California Press, Berkeley).
[19] Chase, J. M., Kraft, N. J., Smith, K. G., Vellend, M., and Inouye, B. D. (2011). Using null models to disentangle variation in community dissimilarity from variation in α‐diversity. Ecosphere, 2(2), 1-11. doi: 10.1890/ES10-00117.1

Ecosystems shelter a huge number of species, continuously interacting. Each species interact in various ways, with trophic interactions, but also non-trophic interactions, not mentioning the abiotic and anthropogenic interactions. In particular, pollination, competition, facilitation, parasitism and many other interaction types are simultaneously present at the same place in terrestrial ecosystems [1-2]. For this reason, we need today to improve our understanding of such complex interaction networks to later anticipate their responses. This program is a huge challenge facing ecologists and they today join their forces among experimentalists, theoreticians and modelers. While some of us struggle in theoretical and modeling dimensions [3-4], some others perform brilliant works to observe and/or experiment on the same ecological objects [5-6]. In this nice study [6], Magrach et al. succeed in studying relatively large plant-pollinator interaction networks in the field, in Mediterranean ecosystems. For the first time to my knowledge, they study community-wide interactions instead of traditional and easier accessible pairwise interactions. On the basis of a statistically relevant survey, they focus on plant reproductive success and on the role of pollinator interactions in such a success. A more reductionist approach based on simpler pairwise interactions between plants and pollinators would not be able to highlight the interaction network structure (the topology) possibly impacting its responses [1,5], among which the reproductive success of some (plant) species. Yet, such a network analysis requires a fine control of probable biases, as those linked to size or autocorrelation between data of various sites. Here, Magrach et al. did a nice work in capturing rigorously the structures and trends behind this community-wide functioning. To grasp possible relationships between plant and pollinator species is a first mandatory step, but the next critical step requires understanding processes hidden behind such relationships. Here, the authors succeed to reach this step too, by starting interpreting the processes at stake in their studied plant-pollinator networks [7]. In particular, the niche complementarity has been demonstrated to play a determinant role in the plant reproductive success, and has a positive impact on it [6]. When will we be able to detect a community-wise process? This is one of my team’s objectives, and we developed new kind of models with this aim. Also, authors focus here on plant-pollinator network, but the next step might be to gather every kind of interactions into a huge ecosystem network which we call the socio-ecosystemic graph [4]. Indeed, why to limit our view to certain interactions only? It will take time to grasp the whole interaction network an ecosystem is sheltering, but this should be our next challenge. And this paper of Magrach et al. [6] is a first fascinating step in this direction. **References** [1] Campbell, C., Yang, S., Albert, R., and Shea, K. (2011). A network model for plant–pollinator community assembly. Proceedings of the National Academy of Sciences, 108(1), 197-202. doi: [10.1073/pnas.1008204108](https://dx.doi.org/10.1073/pnas.1008204108) [2] Kéfi, S., Miele, V., Wieters, E. A., Navarrete, S. A., and Berlow, E. L. (2016). How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience. PLoS biology, 14(8), e1002527. doi: [10.1371/journal.pbio.1002527](https://dx.doi.org/10.1371/journal.pbio.1002527) [3] Gaucherel, C. (2019). The Languages of Nature. When nature writes to itself. Lulu editions, Paris, France. [4] Gaucherel, C., and Pommereau, F. Using discrete systems to exhaustively characterize the dynamics of an integrated ecosystem. Methods in Ecology and Evolution, 10(9), 1615-1627. doi: [10.1111/2041-210X.13242](https://dx.doi.org/10.1111/2041-210X.13242) [5] Bennett, J. M. et al. (2018). A review of European studies on pollination networks and pollen limitation, and a case study designed to fill in a gap. AoB Plants, 10(6), ply068. doi: [10.1093/aobpla/ply068](https://dx.doi.org/10.1093/aobpla/ply068) [6] Magrach, A., Molina, F. P., and Bartomeus, I. (2020). Niche complementarity among pollinators increases community-level plant reproductive success. bioRxiv, 629931, ver. 7 peer-reviewed and recommended by PCI Ecology. doi: [10.1101/629931](https://dx.doi.org/10.1101/629931) [7] Bastolla, U., Fortuna, M. A., Pascual-García, A., Ferrera, A., Luque, B., and Bascompte, J. (2009). The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature, 458(7241), 1018-1020. doi: [10.1038/nature07950](https://dx.doi.org/10.1038/nature07950)

Despite the repeated mantra that "correlation does not imply causation", ecological studies not amenable to experimental settings often rely on correlational patterns to infer the causes of observed patterns. In this context, it's of paramount importance to build a plausible hypothesis and take into account potential confounding factors. The paper by Aizen and collaborators (2023) is a beautiful example of how properly unveil the complexities of an intriguing pattern: The decline in yield of some crops over the last few decades. This is an outstanding question to solve given the need to feed a growing population without destroying the environment, for example by increasing the area under cultivation. Previous studies suggested that pollinator-dependent crops were more susceptible to suffering yield declines than non-pollinator-dependent crops (Garibaldi et al 2011). Given the actual population declines of some pollinators, especially in agricultural areas, this correlative evidence was quite appealing to be interpreted as a causal effect. However, as elegantly shown by Aizen and colleagues in this paper, this first analysis did not account for other alternative explanations, such as the effect of climate change on other plant life-history traits correlated with pollinator dependence. Plant life-history traits do not vary independently. For example, trees are more likely to be pollinator-dependent than herbs (Lanuza et al 2023), which can be an important confounding factor in the analysis. With an elegant analysis and an impressive global dataset, this paper shows that the declining trend in the yield of some crops is most likely associated with their life form than with their dependence on pollinators. This does not imply that pollinators are not important for crop yield, but that the decline in their populations is not leaving a clear imprint in the global yield production trends once accounted for the technological and agronomic improvements. All in all, this paper makes a key contribution to food security by elucidating the factors beyond declining yield trends, and is a brave example of how science can self-correct itself as new knowledge emerges.   

References

Aizen, M.A., Gleiser, G., Kitzberger T. and Milla R. 2023. Being A Tree Crop Increases the Odds of Experiencing Yield Declines Irrespective of Pollinator Dependence. bioRxiv, 2023.04.27.538617, ver 2, peer-reviewed and recommended by PCI Ecology. https://doi.org/10.1101/2023.04.27.538617

Lanuza, J.B., Rader, R., Stavert, J., Kendall, L.K., Saunders, M.E. and Bartomeus, I. 2023. Covariation among reproductive traits in flowering plants shapes their interactions with pollinators. Functional Ecology 37: 2072-2084. https://doi.org/10.1111/1365-2435.14340

Garibaldi, L.A., Aizen, M.A., Klein, A.M., Cunningham, S.A. and Harder, L.D. 2011. Global growth and stability of agricultural yield decrease with pollinator dependence. Proceedings of the National Academy of Sciences, 108: 5909-5914. https://doi.org/10.1073/pnas.1012431108

Are you an ecologist who uses a computer or know someone that does? Even if your research doesn’t rely heavily on advanced computational techniques, it likely hasn’t escaped your attention that computers are increasingly being used to analyse field data and make predictions about the consequences of environmental change. So before artificial intelligence and robots take over from scientists, now is great time to read about how experts think computers could make your life easier and lead to innovations in ecological research. In “Data-based, synthesis-driven: setting the agenda for computational ecology”, Poisot and colleagues [1] provide a brief history of computational ecology and offer their thoughts on how computational thinking can help to bridge different types of ecological knowledge. In this wide-ranging article, the authors share practical strategies for realising three main goals: (i) tighter integration of data and models to make predictions that motivate action by practitioners and policy-makers; (ii) closer interaction between data-collectors and data-users; and (iii) enthusiasm and aptitude for computational techniques in future generations of ecologists. The key, Poisot and colleagues argue, is for ecologists to “engage in meaningful dialogue across disciplines, and recognize the currencies of their collaborations.” Yes, this is easier said than done. However, the journey is much easier with a guide and when everyone involved serves to benefit not only from the eventual outcome, but also the process.

References

[1] Poisot, T., Labrie, R., Larson, E., & Rahlin, A. (2018). Data-based, synthesis-driven: setting the agenda for computational ecology. BioRxiv, 150128, ver. 4 recommended and peer-reviewed by PCI Ecology. doi: 10.1101/150128

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