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Id | Title * | Authors * ▲ | Abstract * | Picture * | Thematic fields * | Recommender | Reviewers | Submission date | |
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01 Mar 2022
![]() Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiringTimothée Poisot https://doi.org/10.32942/osf.io/gxhu2How to evaluate and interpret the contribution of species turnover and interaction rewiring when comparing ecological networks?Recommended by François MunozA network includes a set of vertices or nodes (e.g., species in an interaction network), and a set of edges or links (e.g., interactions between species). Whether and how networks vary in space and/or time are questions often addressed in ecological research. Two ecological networks can differ in several extents: in that species are different in the two networks and establish new interactions (species turnover), or in that species that are present in both networks establish different interactions in the two networks (rewiring). The ecological meaning of changes in network structure is quite different according to whether species turnover or interaction rewiring plays a greater role. Therefore, much attention has been devoted in recent years on quantifying and interpreting the relative changes in network structure due to species turnover and/or rewiring. Poisot et al. (2012) proposed to partition the global variation in structure between networks, \( \beta_{WN} \) (WN = Whole Network) into two terms: \( \beta_{OS} \) (OS = Only Shared species) and \( \beta_{ST} \) (ST = Species Turnover), such as \( \beta_{WN} = \beta_{OS} + \beta_{ST} \). The calculation lays on enumerating the interactions between species that are common or not to two networks, as illustrated on Figure 1 for a simple case. Specifically, Poisot et al. (2012) proposed to use a Sorensen type measure of network dissimilarity, i.e., \( \beta_{WN} = \frac{a+b+c}{(2a+b+c)/2} -1=\frac{b+c}{2a+b+c} \) , where \( a \) is the number of interactions shared between the networks, while \( b \) and \( c \) are interaction numbers unique to one and the other network, respectively. \( \beta_{OS} \) is calculated based on the same formula, but only for the subnetworks including the species common to the two networks, in the form \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a_{OS}+b_{OS}+c_{OS}} \) (e.g., Fig. 1). \( \beta_{ST} \) is deduced by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and represents in essence a "dissimilarity in interaction structure introduced by dissimilarity in species composition" (Poisot et al. 2012). Figure 1. Ecological networks exemplified in Fründ (2021) and discussed in Poisot (2022). a is the number of shared links (continuous lines in right figures), while b+c is the number of edges unique to one or the other network (dashed lines in right figures). Alternatively, Fründ (2021) proposed to define \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a+b+c} \) and \( \beta_{ST} = \frac{b_{ST}+c_{ST}}{2a+b+c} \), where \( b_{ST}=b-b_{OS} \) and \( c_{ST}=c-c_{OS} \) , so that the components \( \beta_{OS} \) and \( \beta_{ST} \) have the same denominator. In this way, Fründ (2021) partitioned the count of unique \( b+c=b_{OS}+b_{ST}+c_{ST} \) interactions, so that \( \beta_{OS} \) and \( \beta_{ST} \) sums to \( \frac{b_{OS}+c_{OS}+b_{ST}+c_{ST}}{2a+b+c} = \frac{b+c}{2a+b+c} = \beta_{WN} \). Fründ (2021) advocated that this partition allows a more sensible comparison of \( \beta_{OS} \) and \( \beta_{ST} \), in terms of the number of links that contribute to each component. For instance, let us consider the networks 1 and 2 in Figure 1 (left panel) such as \( a_{OS}=2 \) (continuous lines in right panel), \( b_{ST} + c_{ST} = 1 \) and \( b_{OS} + c_{OS} = 1 \) (dashed lines in right panel), and thereby \( a = 2 \), \( b+c=2 \), \( \beta_{WN} = 1/3 \). Fründ (2021) measured \( \beta_{OS}=\beta_{ST}=1/6 \) and argued that it is appropriate insofar as it reflects that the number of unique links in the OS and ST components contributing to network dissimilarity (dashed lines) are actually equal. Conversely, the formula of Poisot et al. (2012) yields \( \beta_{OS}=1/5 \), hence \( \beta_{ST} = \frac{1}{3}-\frac{1}{5}=\frac{2}{15}<\beta_{OS} \). Fründ (2021) thus argued that the method of Poisot tends to underestimate the contribution of species turnover. To clarify and avoid misinterpretation of the calculation of \( \beta_{OS} \) and \( \beta_{ST} \) in Poisot et al. (2012), Poisot (2022) provides a new, in-depth mathematical analysis of the decomposition of \( \beta_{WN} \). Poisot et al. (2012) quantify in \( \beta_{OS} \) the actual contribution of rewiring in network structure for the subweb of common species. Poisot (2022) thus argues that \( \beta_{OS} \) relates only to the probability of rewiring in the subweb, while the definition of \( \beta_{OS} \) by Fründ (2021) is relative to the count of interactions in the global network (considered in denominator), and is thereby dependent on both rewiring probability and species turnover. Poisot (2022) further clarifies the interpretation of \( \beta_{ST} \). \( \beta_{ST} \) is obtained by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and thus represents the influence of species turnover in terms of the relative architectures of the global networks and of the subwebs of shared species. Coming back to the example of Fig.1., the Poisot et al. (2012) formula posits that \( \frac{\beta_{ST}}{\beta_{WN}}=\frac{2/15}{1/3}=2/5 \), meaning that species turnover contributes two-fifths of change in network structure, while rewiring in the subweb of common species contributed three fifths. Conversely, the approach of Fründ (2021) does not compare the architectures of global networks and of the subwebs of shared species, but considers the relative contribution of unique links to network dissimilarity in terms of species turnover and rewiring. Poisot (2022) concludes that the partition proposed in Fründ (2021) does not allow unambiguous ecological interpretation of rewiring. He provides guidelines for proper interpretation of the decomposition proposed in Poisot et al. (2012). References Fründ J (2021) Dissimilarity of species interaction networks: how to partition rewiring and species turnover components. Ecosphere, 12, e03653. https://doi.org/10.1002/ecs2.3653 Poisot T, Canard E, Mouillot D, Mouquet N, Gravel D (2012) The dissimilarity of species interaction networks. Ecology Letters, 15, 1353–1361. https://doi.org/10.1111/ele.12002 Poisot T (2022) Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring. EcoEvoRxiv Preprints, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/osf.io/gxhu2 | Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring | Timothée Poisot | <p style="text-align: justify;">Despite having established its usefulness in the last ten years, the decomposition of ecological networks in components allowing to measure their β-diversity retains some methodological ambiguities. Notably, how to ... | ![]() | Biodiversity, Interaction networks, Theoretical ecology | François Munoz | 2021-07-31 00:18:41 | View | |
01 Jun 2018
![]() Data-based, synthesis-driven: setting the agenda for computational ecologyTimothée Poisot, Richard Labrie, Erin Larson, Anastasia Rahlin https://doi.org/10.1101/150128Some thoughts on computational ecology from people who I’m sure use different passwords for each of their accountsRecommended by Phillip P.A. Staniczenko based on reviews by Matthieu Barbier and 1 anonymous reviewerAre 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 | Data-based, synthesis-driven: setting the agenda for computational ecology | Timothée Poisot, Richard Labrie, Erin Larson, Anastasia Rahlin | <p>Computational ecology, defined as the application of computational thinking to ecological problems, has the potential to transform the way ecologists think about the integration of data and models. As the practice is gaining prominence as a way... | ![]() | Meta-analyses, Statistical ecology, Theoretical ecology | Phillip P.A. Staniczenko | 2018-02-05 20:51:41 | View | |
22 May 2019
![]() Sex makes them sleepy: host reproductive status induces diapause in a parasitoid population experiencing harsh wintersTougeron K., Brodeur J., van Baaren J., Renault D. and Le Lann C. https://doi.org/10.1101/371385The response of interacting species to biotic seasonal cuesRecommended by Adele Mennerat and Enric Frago based on reviews by Anne Duplouy and 1 anonymous reviewerIn temperate regions, food abundance and quality vary greatly throughout the year, and the ability of organisms to synchronise their phenology to these changes is a key determinant of their reproductive success. Successful synchronisation requires that cues are perceived prior to change, leaving time for physiological adjustments. References [1] Tauber, M. J., Tauber, C. A., and Masaki, S. (1986). Seasonal Adaptations of Insects. Oxford, New York: Oxford University Press. | Sex makes them sleepy: host reproductive status induces diapause in a parasitoid population experiencing harsh winters | Tougeron K., Brodeur J., van Baaren J., Renault D. and Le Lann C. | <p>When organisms coevolve, any change in one species can induce phenotypic changes in traits and ecology of the other species. The role such interactions play in ecosystems is central, but their mechanistic bases remain underexplored. Upper troph... | ![]() | Coexistence, Evolutionary ecology, Experimental ecology, Host-parasite interactions, Physiology | Adele Mennerat | 2018-07-18 18:51:03 | View | |
29 May 2023
![]() Using integrated multispecies occupancy models to map co-occurrence between bottlenose dolphins and fisheries in the Gulf of Lion, French Mediterranean SeaValentin Lauret, Hélène Labach, Léa David, Matthieu Authier, Olivier Gimenez https://doi.org/10.32942/osf.io/npd6uMapping co-occurence of human activities and wildlife from multiple data sourcesRecommended by Paul Caplat based on reviews by Mason Fidino and 1 anonymous reviewerTwo fields of research have grown considerably over the past twenty years: the investigation of human-wildlife conflicts (e.g. see Treves & Santiago-Ávila 2020), and multispecies occupancy modelling (Devarajan et al. 2020). In their recent study, Lauret et al. (2023) combined both in an elegant methodological framework, applied to the study of the co-occurrence of fishing activities and bottlenose dolphins in the French Mediterranean. A common issue with human-wildlife conflicts (and, in particular, fishery by-catch) is that data is often only available from those conflicts or interactions, limiting the validity of the predictions (Kuiper et al. 2022). Lauret et al. use independent data sources informing the occurrence of fishing vessels and dolphins, combined in a Bayesian multispecies occupancy model where vessels are "the other species". I particularly enjoyed that approach, as integration of human activities in ecological models can be extremely complex, but can also translate in phenomena that can be captured as one would of individuals of a species, as long as the assumptions are made clearly. Here, the model is made more interesting by accounting for environmental factors (seabed depth) borrowing an approach from Generalized Additive Models in the Bayesian framework. While not pretending to provide (yet) practical recommendations to help conserve bottlenose dolphins (and other wildlife conflicts), this study and the associated code are a promising step in that direction. REFERENCES Devarajan, K., Morelli, T.L. & Tenan, S. (2020), Multi-species occupancy models: review, roadmap, and recommendations. Ecography, 43: 1612-1624. https://doi.org/10.1111/ecog.04957 Kuiper, T., Loveridge, A.J. and Macdonald, D.W. (2022), Robust mapping of human–wildlife conflict: controlling for livestock distribution in carnivore depredation models. Anim. Conserv., 25: 195-207. https://doi.org/10.1111/acv.12730 Lauret V, Labach H, David L, Authier M, & Gimenez O (2023) Using integrated multispecies occupancy models to map co-occurrence between bottlenose dolphins and fisheries in the Gulf of Lion, French Mediterranean Sea. Ecoevoarxiv, ver. 2 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.32942/osf.io/npd6u Treves, A. & Santiago-Ávila, F.J. (2020). Myths and assumptions about human-wildlife conflict and coexistence. Conserv. Biol. 34, 811–818. https://doi.org/10.1111/cobi.13472 | Using integrated multispecies occupancy models to map co-occurrence between bottlenose dolphins and fisheries in the Gulf of Lion, French Mediterranean Sea | Valentin Lauret, Hélène Labach, Léa David, Matthieu Authier, Olivier Gimenez | <p style="text-align: justify;">In the Mediterranean Sea, interactions between marine species and human activities are prevalent. The coastal distribution of bottlenose dolphins (<em>Tursiops truncatus</em>) and the predation pressure they put on ... | ![]() | Marine ecology, Population ecology, Species distributions | Paul Caplat | 2022-10-21 11:13:36 | View | |
22 Apr 2021
![]() The hidden side of the Allee effect: correlated demographic traits and extinction risk in experimental populationsVercken Elodie, Groussier Géraldine, Lamy Laurent, Mailleret Ludovic https://hal.archives-ouvertes.fr/hal-02570868Allee effects under the magnifying glassRecommended by David AlonsoFor decades, the effect of population density on individual performance has been studied by ecologists using both theoretical, observational, and experimental approaches. The generally accepted definition of the Allee effect is a positive correlation between population density and average individual fitness that occurs at low population densities, while individual fitness is typically decreased through intraspecific competition for resources at high population densities. Allee effects are very relevant in conservation biology because species at low population densities would then be subjected to much higher extinction risks. However, due to all kinds of stochasticity, low population numbers are always more vulnerable to extinction than larger population sizes. This effect by itself cannot be necessarily ascribed to lower individual performance at low densities, i.e, Allee effects. Vercken and colleagues (2021) address this challenging question and measure the extent to which average individual fitness is affected by population density analyzing 30 experimental populations. As a model system, they use populations of parasitoid wasps of the genus Trichogramma. They report Allee effect in 8 out 30 experimental populations. Vercken and colleagues's work has several strengths. First of all, it is nice to see that they put theory at work. This is a very productive way of using theory in ecology. As a starting point, they look at what simple theoretical population models say about Allee effects (Lewis and Kareiva 1993; Amarasekare 1998; Boukal and Berec 2002). These models invariably predict a one-humped relation between population-density and per-capita growth rate. It is important to remark that pure logistic growth, the paradigm of density-dependence, would never predict such qualitative behavior. It is only when there is a depression of per-capita growth rates at low densities that true Allee effects arise. Second, these authors manage to not only experimentally test this main prediction but also report additional demographic traits that are consistently affected by population density. In these wasps, individual performance can be measured in terms of the average number of individuals every adult is able to put into the next generation ---the lambda parameter in their analysis. The first panel in figure 3 shows that the per-capita growth rates are lower in populations presenting Allee effects, the ones showing a one-humped behavior in the relation between per-capita growth rates and population densities (see figure 2). Also other population traits, such maximum population size and exitinction probability, change in a correlated and consistent manner. In sum, Vercken and colleagues's results are experimentally solid and based on theory expectations. However, they are very intriguing. They find the signature of Allee effects in only 8 out 30 populations, all from the same genus Trichogramma, and some populations belonging to the same species (from different sampling sites) do not show consistently Allee effects. Where does this population variability comes from? What are the reasons underlying this within- and between-species variability? What are the individual mechanisms driving Allee effects in these populations? Good enough, this piece of work generates more intriguing questions than the question is able to clearly answer. Science is not a collection of final answers but instead good questions are the ones that make science progress. References Amarasekare P (1998) Allee Effects in Metapopulation Dynamics. The American Naturalist, 152, 298–302. https://doi.org/10.1086/286169 Boukal DS, Berec L (2002) Single-species Models of the Allee Effect: Extinction Boundaries, Sex Ratios and Mate Encounters. Journal of Theoretical Biology, 218, 375–394. https://doi.org/10.1006/jtbi.2002.3084 Lewis MA, Kareiva P (1993) Allee Dynamics and the Spread of Invading Organisms. Theoretical Population Biology, 43, 141–158. https://doi.org/10.1006/tpbi.1993.1007 Vercken E, Groussier G, Lamy L, Mailleret L (2021) The hidden side of the Allee effect: correlated demographic traits and extinction risk in experimental populations. HAL, hal-02570868, ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://hal.archives-ouvertes.fr/hal-02570868 | The hidden side of the Allee effect: correlated demographic traits and extinction risk in experimental populations | Vercken Elodie, Groussier Géraldine, Lamy Laurent, Mailleret Ludovic | <p style="text-align: justify;">Because Allee effects (i.e., the presence of positive density-dependence at low population size or density) have major impacts on the dynamics of small populations, they are routinely included in demographic models ... | ![]() | Demography, Experimental ecology, Population ecology | David Alonso | 2020-09-30 16:38:29 | View | |
05 Apr 2019
![]() Using a large-scale biodiversity monitoring dataset to test the effectiveness of protected areas at conserving North-American breeding birdsVictor Cazalis, Soumaya Belghali, Ana S.L. Rodrigues https://doi.org/10.1101/433037Protected Areas effects on biodiversity: a test using bird data that hopefully will give ideas for much more studies to comeRecommended by Paul Caplat based on reviews by Willson Gaul and 1 anonymous reviewerIn the face of worldwide declines in biodiversity, evaluating the effectiveness of conservation practices is an absolute necessity. Protected Areas (PA) are a key tool for conservation, and the question “Are PA effective” has been on many a research agenda, as the introduction to this preprint will no doubt convince you. A challenge we face is that, until now, few studies have been explicitly designed to evaluate PA, and despite the rise of meta-analyses on the topic, our capacity to quantify their effect on biodiversity remains limited. References [1] Cazalis, V., Belghali, S., & Rodrigues, A. S. (2019). Using a large-scale biodiversity monitoring dataset to test the effectiveness of protected areas at conserving North-American breeding birds. bioRxiv, 433037, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/433037 | Using a large-scale biodiversity monitoring dataset to test the effectiveness of protected areas at conserving North-American breeding birds | Victor Cazalis, Soumaya Belghali, Ana S.L. Rodrigues | <p>Protected areas currently cover about 15% of the global land area, and constitute one of the main tools in biodiversity conservation. Quantifying their effectiveness at protecting species from local decline or extinction involves comparing prot... | ![]() | Biodiversity, Conservation biology, Human impact, Landscape ecology, Macroecology | Paul Caplat | 2018-10-04 08:43:34 | View | |
02 Oct 2018
![]() How optimal foragers should respond to habitat changes? On the consequences of habitat conversion.Vincent Calcagno, Frederic Hamelin, Ludovic Mailleret, Frederic Grognard 10.1101/273557Optimal foraging in a changing world: old questions, new perspectivesRecommended by Francois-Xavier Dechaume-MoncharmontMarginal value theorem (MVT) is an archetypal model discussed in every behavioural ecology textbook. Its popularity is largely explained but the fact that it is possible to solve it graphically (at least in its simplest form) with the minimal amount of equations, which is a sensible strategy for an introductory course in behavioural ecology [1]. Apart from this heuristic value, one may be tempted to disregard it as a naive toy model. After a burst of interest in the 70's and the 80's, the once vivid literature about optimal foraging theory (OFT) has lost its momentum [2]. Yet, OFT and MVT have remained an active field of research in the parasitoidologists community, mostly because the sampling strategy of a parasitoid in patches of hosts and its resulting fitness gain are straightforward to evaluate, which eases both experimental and theoretical investigations [3]. References [1] Fawcett, T. W. & Higginson, A. D. 2012 Heavy use of equations impedes communication among biologists. Proc. Natl. Acad. Sci. 109, 11735–11739. doi: 10.1073/pnas.1205259109 | How optimal foragers should respond to habitat changes? On the consequences of habitat conversion. | Vincent Calcagno, Frederic Hamelin, Ludovic Mailleret, Frederic Grognard | The Marginal Value Theorem (MVT) provides a framework to predict how habitat modifications related to the distribution of resources over patches should impact the realized fitness of individuals and their optimal rate of movement (or patch residen... | ![]() | Behaviour & Ethology, Dispersal & Migration, Foraging, Landscape ecology, Spatial ecology, Metacommunities & Metapopulations, Theoretical ecology | Francois-Xavier Dechaume-Moncharmont | 2018-03-05 10:42:11 | View | |
14 Dec 2018
![]() Recommendations to address uncertainties in environmental risk assessment using toxicokinetics-toxicodynamics modelsVirgile Baudrot and Sandrine Charles https://doi.org/10.1101/356469Addressing uncertainty in Environmental Risk Assessment using mechanistic toxicological models coupled with Bayesian inferenceRecommended by Luis Schiesari based on reviews by Andreas Focks and 2 anonymous reviewersEnvironmental Risk Assessment (ERA) is a strategic conceptual framework to characterize the nature and magnitude of risks, to humans and biodiversity, of the release of chemical contaminants in the environment. Several measures have been suggested to enhance the science and application of ERA, including the identification and acknowledgment of uncertainties that potentially influence the outcome of risk assessments, and the appropriate consideration of temporal scale and its linkage to assessment endpoints [1]. References [1] Dale, V. H., Biddinger, G. R., Newman, M. C., Oris, J. T., Suter, G. W., Thompson, T., ... & Chapman, P. M. (2008). Enhancing the ecological risk assessment process. Integrated environmental assessment and management, 4(3), 306-313. doi: 10.1897/IEAM_2007-066.1 | Recommendations to address uncertainties in environmental risk assessment using toxicokinetics-toxicodynamics models | Virgile Baudrot and Sandrine Charles | <p>Providing reliable environmental quality standards (EQS) is a challenging issue for environmental risk assessment (ERA). These EQS are derived from toxicity endpoints estimated from dose-response models to identify and characterize the environm... | ![]() | Chemical ecology, Ecotoxicology, Experimental ecology, Statistical ecology | Luis Schiesari | 2018-06-27 21:33:30 | View | |
27 Nov 2023
Modeling Tick Populations: An Ecological Test Case for Gradient Boosted TreesWilliam Manley, Tam Tran, Melissa Prusinski, Dustin Brisson https://doi.org/10.1101/2023.03.13.532443Gradient Boosted Trees can deliver more than accurate ecological predictionsRecommended by Timothée PoisotTick-borne diseases are an important burden on public health all over the globe, making accurate forecasts of tick population a key ingredient in a successful public health strategy. Over long time scales, tick populations can undergo complex dynamics, as they are sensitive to many non-linear effects due to the complex relationships between ticks and the relevant (numerical) features of their environment. But luckily, capturing complex non-linear responses is a task that machine learning thrives on. In this contribution, Manley et al. (2023) explore the use of Gradient Boosted Trees to predict the distribution (presence/absence) and abundance of ticks across New York state. This is an interesting modelling challenge in and of itself, as it looks at the same ecological question as an instance of a classification problem (presence/absence) or of a regression problem (abundance). In using the same family of algorithm for both, Manley et al. (2023) provide an interesting showcase of the versatility of these techniques. But their article goes one step further, by setting up a multi-class categorical model that estimates jointly the presence and abundance of a population. I found this part of the article particularly elegant, as it provides an intermediate modelling strategy, in between having two disconnected models for distribution and abundance, and having nested models where abundance is only predicted for the present class (see e.g. Boulangeat et al., 2012, for a great description of the later). One thing that Manley et al. (2023) should be commended for is their focus on opening up the black box of machine learning techniques. I have never believed that ML models are more inherently opaque than other families of models, but the focus in this article on explainable machine learning shows how these models might, in fact, bring us closer to a phenomenological understanding of the mechanisms underpinning our observations. There is also an interesting discussion in this article, on the rate of false negatives in the different models that are being benchmarked. Although model selection often comes down to optimizing the overall quality of the confusion matrix (for distribution models, anyway), depending on the type of information we seek to extract from the model, not all types of errors are created equal. If the purpose of the model is to guide actions to control vectors of human pathogens, a false negative (predicting that the vector is absent at a site where it is actually present) is a potentially more damaging outcome, as it can lead to the vector population (and therefore, potentially, transmission) increasing unchecked. References
Boulangeat I, Gravel D, Thuiller W. Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances: The role of dispersal and biotic interactions in explaining species distributions and abundances. Ecol Lett. 2012;15: 584-593. Manley W, Tran T, Prusinski M, Brisson D. (2023) Modeling tick populations: An ecological test case for gradient boosted trees. bioRxiv, 2023.03.13.532443, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.03.13.532443 | Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees | William Manley, Tam Tran, Melissa Prusinski, Dustin Brisson | <p style="text-align: justify;">General linear models have been the foundational statistical framework used to discover the ecological processes that explain the distribution and abundance of natural populations. Analyses of the rapidly expanding ... | Parasitology, Species distributions, Statistical ecology | Timothée Poisot | Anonymous, Anonymous | 2023-03-23 23:41:17 | View | |
12 May 2022
![]() Riparian forest restoration as sources of biodiversity and ecosystem functions in anthropogenic landscapesYasmine Antonini, Marina Vale Beirao, Fernanda Vieira Costa, Cristiano Schetini Azevedo, Maria Wojakowski, Alessandra Kozovits, Maria Rita Silverio Pires, Hildeberto Caldas Sousa, Maria Cristina Teixeira Braga Messias, Maria Augusta Goncalves Fujaco, Mariangela Garcia Praca Leite, Joice Paiva Vidigal Martins, Graziella Franca Monteiro, Rodolfo Dirzo https://doi.org/10.1101/2021.09.08.459375Complex but positive diversity - ecosystem functioning relationships in Riparian tropical forestsRecommended by Werner UlrichMany ecological drivers can impact ecosystem functionality and multifunctionality, with the latter describing the joint impact of different functions on ecosystem performance and services. It is now generally accepted that taxonomically richer ecosystems are better able to sustain high aggregate functionality measures, like energy transfer, productivity or carbon storage (Buzhdygan 2020, Naeem et al. 2009), and different ecosystem services (Marselle et al. 2021) than those that are less rich. Antonini et al. (2022) analysed an impressive dataset on animal and plant richness of tropical riparian forests and abundances, together with data on key soil parameters. Their work highlights the importance of biodiversity on functioning, while accounting for a manifold of potentially covarying drivers. Although the key result might not come as a surprise, it is a useful contribution to the diversity - ecosystem functioning topic, because it is underpinned with data from tropical habitats. To date, most analyses have focused on temperate habitats, using data often obtained from controlled experiments. The paper also highlights that diversity–functioning relationships are complicated. Drivers of functionality vary from site to site and each measure of functioning, including parameters as demonstrated here, can be influenced by very different sets of predictors, often associated with taxonomic and trait diversity. Single correlative comparisons of certain aspects of diversity and functionality might therefore return very different results. Antonini et al. (2022) show that, in general, using 22 predictors of functional diversity, varying predictor subsets were positively associated with soil functioning. Correlational analyses alone cannot resolve the question of causal link. Future studies should therefore focus on inferring precise mechanisms behind the observed relationships, and the environmental constraints on predictor subset composition and strength. References Antonini Y, Beirão MV, Costa FV, Azevedo CS, Wojakowski MM, Kozovits AR, Pires MRS, Sousa HC de, Messias MCTB, Fujaco MA, Leite MGP, Vidigal JP, Monteiro GF, Dirzo R (2022) Riparian forest restoration as sources of biodiversity and ecosystem functions in anthropogenic landscapes. bioRxiv, 2021.09.08.459375, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2021.09.08.459375 Buzhdygan OY, Meyer ST, Weisser WW, Eisenhauer N, Ebeling A, Borrett SR, Buchmann N, Cortois R, De Deyn GB, de Kroon H, Gleixner G, Hertzog LR, Hines J, Lange M, Mommer L, Ravenek J, Scherber C, Scherer-Lorenzen M, Scheu S, Schmid B, Steinauer K, Strecker T, Tietjen B, Vogel A, Weigelt A, Petermann JS (2020) Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands. Nature Ecology & Evolution, 4, 393–405. https://doi.org/10.1038/s41559-020-1123-8 Marselle MR, Hartig T, Cox DTC, de Bell S, Knapp S, Lindley S, Triguero-Mas M, Böhning-Gaese K, Braubach M, Cook PA, de Vries S, Heintz-Buschart A, Hofmann M, Irvine KN, Kabisch N, Kolek F, Kraemer R, Markevych I, Martens D, Müller R, Nieuwenhuijsen M, Potts JM, Stadler J, Walton S, Warber SL, Bonn A (2021) Pathways linking biodiversity to human health: A conceptual framework. Environment International, 150, 106420. https://doi.org/10.1016/j.envint.2021.106420 Naeem S, Bunker DE, Hector A, Loreau M, Perrings C (Eds.) (2009) Biodiversity, Ecosystem Functioning, and Human Wellbeing: An Ecological and Economic Perspective. Oxford University Press, Oxford. https://doi.org/10.1093/acprof:oso/9780199547951.001.0001 | Riparian forest restoration as sources of biodiversity and ecosystem functions in anthropogenic landscapes | Yasmine Antonini, Marina Vale Beirao, Fernanda Vieira Costa, Cristiano Schetini Azevedo, Maria Wojakowski, Alessandra Kozovits, Maria Rita Silverio Pires, Hildeberto Caldas Sousa, Maria Cristina Teixeira Braga Messias, Maria Augusta Goncalves Fuja... | <ol> <li style="text-align: justify;">Restoration of tropical riparian forests is challenging, since these ecosystems are the most diverse, dynamic, and complex physical and biological terrestrial habitats. This study tested whether biodiversity ... | ![]() | Biodiversity, Community ecology, Ecological successions, Ecosystem functioning, Terrestrial ecology | Werner Ulrich | 2021-09-10 10:51:23 | View |
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