Ecological network complexity scales with area

Galiana, Núria; Lurgi, Miguel; Bastazini, Vinicius A. G.; Bosch, Jordi; Cagnolo, Luciano; Cazelles, Kévin; Claramunt‐López, Bernat; Emer, Carine; Fortin, Marie‐Josée; Graß, Ingo; Hernández-Castellano, Carlos; Jauker, Frank; Leroux, Shawn J.; McCann, Kevin S.; McLeod, Anne M.; Montoya, Daniel; Mulder, Christian; Osorio-Canadas, Sergio; Reverté, Sara; Rodrigo, Anselm; Steffan‐Dewenter, Ingolf; Traveset, Anna; Valverde, Sergi; Vázquez, Diego P.; Wood, Spencer A.; Gravel, Dominique; Roslin, Tomas; Thuiller, Wilfried; Montoya, José M.

doi:10.1038/s41559-021-01644-4

Cited by 54 publications

(41 citation statements)

References 63 publications

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“…networks (10,11). By considering all eight vertebrate, invertebrate, and plant taxa, we further encompass several ecological functions implicit within the system, such as seed dispersal, pollination, arthropod pest control, and herbivory, providing us with a holistic assessment of ecosystem responses to human-induced disturbances.…”

Section: Discussionmentioning

confidence: 99%

Emergent properties of species-habitat networks in an insular forest landscape

et al. 2022

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Deforestation and fragmentation are pervasive drivers of biodiversity loss, but how they scale up to entire landscapes remains poorly understood. Here, we apply species-habitat networks based on species co-occurrences to test the effects of insular fragmentation on multiple taxa—medium-large mammals, small nonvolant mammals, lizards, understory birds, frogs, dung beetles, orchid bees, and trees—across 22 forest islands and three continuous forest sites within a river-damming quasi-experimental landscape in Central Amazonia. Widespread, nonrandom local species extinctions were translated into highly nested networks of low connectance and modularity. Networks’ robustness considering the sequential removal of large-to-small sites was generally low; between 5% (dung beetles) and 50% (orchid bees) of species persisted when retaining only <10 ha of islands. In turn, larger sites and body size were the main attributes structuring the networks. Our results raise the prospects that insular forest fragmentation results in simplified species-habitat networks, with distinct taxa persistence to habitat loss.

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Section: Discussionmentioning

confidence: 99%

Emergent properties of species-habitat networks in an insular forest landscape

et al. 2022

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“…Power law, which is also known as the 80:20 rule or Pareto principle, exponential, and log‐normal distributions are addressed in the literature that tends to search for a specific statistical distribution to discover the characteristics of many natural and unnatural phenomena. The example phenomena reflecting these distributions are compiled and categorized from (Limpert et al, 2001; Milojević, 2010; Newman, 2004; White et al, 2008) or other resources with given references as follows: Natural phenomena fitting log‐normal : element concentration in the Earth's crust, latent periods (from infection to the first symptoms) of infectious disease [e.g., the incubation period of Coronavirus disease 2019 (COVID‐19), SARS (severe acute respiratory syndrome), and MERS (Middle East Respiratory Syndrome; Backer et al, 2020)], the abundance of bacteria on plants; Unnatural phenomena fitting log‐normal : number of letters per word, number of words per sentence, age of first marriage in Western; Natural phenomena fitting exponential : damage in nuclear power incidents and accidents before 1980 (Wheatley et al, 2017), moderate‐sized disasters (observed sea‐level variations, wind velocity, annual river floods) (Pisarenko & Rodkin, 2010), the arrival rate of cosmic ray alpha particles or Geiger counter tics (Tobias, 2012); Unnatural phenomena fitting exponential : time to failure patterns (also in natural phenomena) (Frank, 2009), modeling malware propagation delays (Wang & Murynets, 2013), frequency of Korean family names (power law in family names in the world), intervals between aircraft arrivals to major airports (Willemain et al, 2004), the inter‐arrival times of the 911 calls (Albert, 2011), the time between goals in World Cup football matches (Chu, 2003), the dispersion of U.S. incomes which was qualified as a kind of thermal equilibrium (Bartels, 2012); Natural phenomena fitting power law : island sizes, lake sizes, flood magnitudes, species body sizes, individual body sizes (White et al, 2008), basic community structure descriptors (number of species, links, and links per species) with the area (Galiana et al, 2022); and Unnatural phenomena fitting power law : author productivity, citations received by papers, scattering of scientific literature (Milojević, 2010). Component sizes in component‐based software development (Sharma & Pendharkar, 2022).…”

Section: Fitting Binary Feature Frequencies Into a Statistical Distri...mentioning

confidence: 99%

Gaining insights in datasets in the shade of “garbage in, garbage out” rationale: Feature space distribution fitting

Canbek

2022

WIREs Data Min & Knowl

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This article emphasizes comprehending the “Garbage In, Garbage Out” (GIGO) rationale and ensuring the dataset quality in Machine Learning (ML) applications to achieve high and generalizable performance. An initial step should be added in an ML workflow where researchers evaluate the insights gained by quantitative analysis of the datasets sample and feature spaces. This study contributes towards achieving such a goal by suggesting a technique to quantify datasets in terms of feature frequency distribution characteristics. Hence a unique insight is provided into how the features in the available dataset samples are frequent. The technique was demonstrated in 11 benign and malign (malware) Android application datasets belonging to six academic Android mobile malware classification studies. The permissions requested by applications such as CALL_PHONE compose a relatively high‐dimensional binary feature space. The results showed that the distributions fit well into two of the four long right‐tail statistical distributions: log‐normal, exponential, power law, and Poisson. Precisely, log‐normal was the most exhibited statistical distribution except the two malign datasets that were in exponential. This study also explores statistical distribution fit/unfit feature analysis that enhances the insights in feature space. Finally, the study compiles phenomena examples in the literature exhibiting these statistical distributions that should be considered for interpreting the fitted distributions. In conclusion, conducting well‐formed statistical methods provides a clear understanding of the datasets and intra‐class and inter‐class differences before proceeding with selecting features and building a classifier model. Feature distribution characteristics should be one to analyze beforehand. This article is categorized under: Technologies > Data Preprocessing Technologies > Classification Technologies > Machine Learning

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“…However, this rationale has proven less apt for generalization than originally thought and the trophic theory of island biogeography (TTIB; the interplay of food web ecology and island biogeography; Holt, 2009) perspective allows examination of the underlying mechanisms. Establishment dynamics and the resulting ES outcomes are modulated by food web complexity (# species, # links), which increases with island area following a power law (Galiana et al, 2022). Specialists perform well in simple food webs (Montoya et al, 2003;Holt, 2009), which should provide them with an advantage in small oceanic islands.…”

Section: Anticipating Functionalities Of Species-poor Communitiesmentioning

confidence: 99%

“…Similarly, interest in how altitudinal gradients shape species distribution patterns has recently been rekindled (Dangles et al, 2008;Peters et al, 2016;Moret et al, 2019), with work confirming the role of temperature as a core determinant in these processes (Peters et al, 2016;Steinbauer et al, 2018). Research in either setting has historically centered on richness metrics, often treating species as stand-alone entities rather than constituent components of dynamic, interlinked networks (Massol et al, 2017;Galiana et al, 2022). Only in recent years have topics such as niche theory, trophic ecology or ecosystem functioning found places within ETIB-inspired theorems or mountain ecosystem science (Allouche et al, 2012;Rasmann et al, 2014a;Warren et al, 2015;Peters et al, 2016;Massol et al, 2017;Holt et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Island and Mountain Ecosystems as Testbeds for Biological Control in the Anthropocene

et al. 2022

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For centuries, islands and mountains have incited the interest of naturalists, evolutionary biologists and ecologists. Islands have been the cradle for biogeography and speciation theories, while mountain ranges have informed how population adaptation to thermal floors shapes the distribution of species globally. Islands of varying size and mountains’ altitudinal ranges constitute unique “natural laboratories” where one can investigate the effects of species loss or global warming on ecosystem service delivery. Although invertebrate pollination or seed dispersal processes are steadily being examined, biological control research is lagging. While observations of a wider niche breadth among insect pollinators in small (i.e., species-poor) islands or at high (i.e., colder) altitudes likely also hold for biological control agents, such remains to be examined. In this Perspective piece, we draw on published datasets to show that island size alone does not explain biological control outcomes. Instead, one needs to account for species’ functional traits, habitat heterogeneity, host community make-up, phenology, site history or even anthropogenic forces. Meanwhile, data from mountain ranges show how parasitism rates of Noctuid moths and Tephritid fruit flies exhibit species- and context-dependent shifts with altitude. Nevertheless, future empirical work in mountain settings could clarify the thermal niche space of individual natural enemy taxa and overall thermal resilience of biological control. We further discuss how global databases can be screened, while ecological theories can be tested, and simulation models defined based upon observational or manipulative assays in either system. Doing so can yield unprecedented insights into the fate of biological control in the Anthropocene and inform ways to reinforce this vital ecosystem service under global environmental change scenarios.

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Ecological network complexity scales with area

Cited by 54 publications

References 63 publications

Emergent properties of species-habitat networks in an insular forest landscape

Emergent properties of species-habitat networks in an insular forest landscape

Gaining insights in datasets in the shade of “garbage in, garbage out” rationale: Feature space distribution fitting

Island and Mountain Ecosystems as Testbeds for Biological Control in the Anthropocene

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