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03 Jan 2024
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Efficient sampling designs to assess biodiversity spatial autocorrelation : should we go fractal?

Spatial patterns and autocorrelation challenges in ecological conservation

Recommended by ORCID_LOGO based on reviews by Nigel Yoccoz and Charles J Marsh

Pattern, like beauty, is to some extent in the eye of the beholder” (Grant 1977 in Wiens, 1989)

Ecologists are immersed in unraveling the complex spatial patterns that govern species diversity, driven by both practical and theoretical imperatives (Rahbek, 2005; Wang et al., 2019). This dual focus necessitates a practical imperative for strategic biodiversity conservation, requiring a nuanced understanding of locations with peak species richness and dynamic shifts in species assemblages (Chase et al., 2020). Simultaneously, there is a theoretical interest in using diversity patterns as empirical testing grounds for theories explaining factors influencing diversity disparities and the associated increase in species turnover correlated with inter-site distance (Condit et al., 2002).
 
McGill (2010), in his paper "Matters of Scale", highlights the scale-dependent nature of ecology, aligning with the recognition that spatial autocorrelation is inherent in biogeographical data and often correlated with sample size (Rahbek, 2005). Spatial autocorrelation, often underestimated in ecological studies (Dormann, 2007), occurs when proximate locations exhibit similarities in ecological attributes (Tobler, 1970; Getis, 2010), introducing a latent bias that compromises the robustness of ecological findings (Dormann, 2007; Dormann et al., 2007). This phenomenon serves as both an asset, providing valuable information for inferring processes from patterns (Palma et al. 1999), and a challenge, imposing limitations on hypothesis testing and prediction (Dormann et al., 2007 and references therein). Various factors contribute to spatial autocorrelation, with three primary contributors (Dormann et al., 2007; Legendre, 1993; Legendre and Fortin, 1989; Legendre and Legendre, 2012): (i) distance-related effects in biological processes, (ii) misrepresentation of non-linear relationships between the environment and species as linear and (iii) the oversight of a crucial spatially structured environmental determinant in the statistical model, leading to spatial structuring in the response (Dormann et al., 2007).
 
Recognising the pivotal role of spatial heterogeneity in ecological theories (Wang et al., 2019), it becomes imperative to discern and address the limitations introduced by spatial autocorrelation (Legendre, 1993). McGill (2011) emphasises that the ultimate goal of biodiversity pattern studies should be to develop a quantitative predictive theory useful for conservation. The spatial dimension's importance in study planning, determining the system's scale, appropriate quadrat size, and spacing between sampling stations, is paramount (Fortin, 1999a,b). Responses to these considerations are intricately linked with study objectives and insights from pre-sampling campaigns, underscoring the need for a nuanced and rigorous approach (Delmelle, 2021).
 
Understanding statistical techniques and nested sampling designs is crucial to answering fundamental ecological questions (Dormann et al., 2007; McDonald, 2012). In addressing spatial autocorrelation challenges, ecologists must recognize the limitations of many standard statistical methods in ecological studies (Dale and Fortin, 2002; Legendre and Fortin, 1989; Steel et al., 2013). In the initial phases of description or hypothesis generation, ecologists should proactively acknowledge the spatial structure in their data and conduct tests for spatial autocorrelation (for a comprehensive description, see Legendre and Fortin, 1989): various tools, including correlograms, spectral analysis, the Mantel test, and clustering methods, facilitate the assessment and description of spatial structures. The partial Mantel test enables the study of causal models with space as an explanatory variable. Techniques for mapping ecological variables, such as interpolation, trend surface analysis, and constrained clustering, yield maps providing valuable insights into the spatial dynamics of ecological systems.
 
This refined consideration of spatial autocorrelation emerges as an imperative in ecological research, fostering a deeper and more precise understanding of the intricate interplay between species diversity, spatial patterns, and the inherent limitations imposed by spatial autocorrelation (Legendre et al., 2002). This not only contributes significantly to the scientific discourse in ecology but also aligns with McGill's vision of developing predictive theories for effective conservation (Bacaro et al., 2016; McGill, 2011).
 
In this study by Fabien Laroche (2023), titled “Efficient sampling designs to assess biodiversity spatial autocorrelation: should we go fractal?” the primary focus was on addressing the challenges associated with estimating the autocorrelation range of species distribution across spatial scales. The study aimed to explore alternative sampling designs, with a particular focus on the application of fractal designs—self-similar designs with well-identified scales. The overarching goal was to evaluate whether fractal designs could offer a more efficient compromise compared to traditional hybrid designs, which involve mixing random sampling points with a systematic grid.
 
Virtual ecology provides a way to test whether sampling designs can accurately detect or quantify effects of interest before implementing them in the field. Beyond the question of assessing the power of empirical designs, a virtual ecology analysis contributes to clearly formulating the set of questions associated with a design. However, only a few virtual studies have focused on efficient designs to accurately estimate the autocorrelation range of biodiversity variables. In this study, the statistical framework of optimal design of experiments was employed—a methodology often used in building and comparing designs of temporal or spatiotemporal biodiversity surveys but rarely applied to the specific problem of quantifying spatial autocorrelation.
 
Key findings from the study shed light on optimal sampling strategies, with a notable dependence on the feasible grid mesh size over the study area in relation to expected autocorrelation range values. The results demonstrated that the efficiency of designs varied based on the specific effect under study. Fractal designs, however, exhibited superior performance, particularly when assessing the effect of a monotonic environmental gradient across space.
 
In conclusion, the study provides valuable insights into the potential benefits of incorporating fractal designs in biodiversity studies, offering a nuanced and efficient approach to estimate spatial autocorrelation. These findings contribute significantly to the ongoing scientific discourse in ecology, providing practical considerations for improving sampling designs in biodiversity assessments.
 
References
 
Bacaro, G., Altobelli, A., Cameletti, M., Ciccarelli, D., Martellos, S., Palmer, M.W., Ricotta, C., Rocchini, D., Scheiner, S.M., Tordoni, E., Chiarucci, A., 2016. Incorporating spatial autocorrelation in rarefaction methods: Implications for ecologists and conservation biologists. Ecological Indicators 69, 233-238. https://doi.org/10.1016/j.ecolind.2016.04.026
 
Chase, J.M., Jeliazkov, A., Ladouceur, E., Viana, D.S., 2020. Biodiversity conservation through the lens of metacommunity ecology. Annals of the New York Academy of Sciences 1469, 86-104. https://doi.org/10.1111/nyas.14378
 
Condit, R., Pitman, N., Leigh, E.G., Chave, J., Terborgh, J., Foster, R.B., Núñez, P., Aguilar, S., Valencia, R., Villa, G., Muller-Landau, H.C., Losos, E., Hubbell, S.P., 2002. Beta-Diversity in Tropical Forest Trees. Science 295, 666-669. https://doi.org/10.1126/science.1066854
 
Dale, M.R.T., Fortin, M.-J., 2002. Spatial autocorrelation and statistical tests in ecology. Écoscience 9, 162-167. https://doi.org/10.1080/11956860.2002.11682702
 
Delmelle, E.M., 2021. Spatial Sampling, in: Fischer, M.M., Nijkamp, P. (Eds.), Handbook of Regional Science. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 1829-1844.
 
Dormann, C.F., 2007. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecology & Biogeography 16, 129-128. https://doi.org/10.1111/j.1466-8238.2006.00279.x
 
Dormann, C.F., McPherson, J.M., Araújo, M.B., Bivand, R., Bolliger, J., Carl, G., Davies, R.G., Hirzel, A., Jetz, W., Kissling, W.D., Kühn, I., Ohlemüler, R., Peres-Neto, P.R., Reineking, B., Schröder, B., Schurr, F.M., Wilson, R., 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 33, 609-628. https://doi.org/10.1111/j.2007.0906-7590.05171.x
 
Fortin, M.-J., 1999a. Effects of quadrat size and data measurement on the detection of boundaries. Journal of Vegetation Science 10, 43-50. https://doi.org/10.2307/3237159
 
Fortin, M.-J., 1999b. Effects of sampling unit resolution on the estimation of spatial autocorrelation. Écoscience 6, 636-641. https://doi.org/10.1080/11956860.1999.11682547
 
Getis, A., 2010. Spatial Autocorrelation, in: Fischer, M.M., Getis, A. (Eds.), Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 255-278.
 
Laroche, F., 2023. Efficient sampling designs to assess biodiversity spatial autocorrelation: should we go fractal? bioRxiv, 2022.07.29.501974, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.07.29.501974
 
Legendre, P., 1993. Spatial Autocorrelation: Trouble or New Paradigm? Ecology 74, 1659-1673. https://doi.org/10.2307/1939924
 
Legendre, P., Dale, M.R.T., Fortin, M.-J., Gurevitch, J., Hohn, M., Myers, D., 2002. The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography 25, 601-615. https://doi.org/10.1034/j.1600-0587.2002.250508.x
 
Legendre, P., Fortin, M.J., 1989. Spatial pattern and ecological analysis. Vegetatio 80, 107-138. https://doi.org/10.1007/BF00048036
 
Legendre, P., Legendre, L., 2012. Numerical Ecology, Third Edition ed. Elsevier, The Netherlands.
 
McDonald, T., 2012. Spatial sampling designs for long-term ecological monitoring, in: Cooper, A.B., Gitzen, R.A., Licht, D.S., Millspaugh, J.J. (Eds.), Design and Analysis of Long-term Ecological Monitoring Studies. Cambridge University Press, Cambridge, pp. 101-125.
 
McGill, B.J., 2010. Matters of Scale. Science 328, 575-576. https://doi.org/10.1126/science.1188528
 
McGill, B.J., 2011. Linking biodiversity patterns by autocorrelated random sampling. American Journal of Botany 98, 481-502. https://doi.org/10.3732/ajb.1000509
 
Rahbek, C., 2005. The role of spatial scale and the perception of large-scale species-richness patterns. Ecology Letters 8, 224-239. https://doi.org/10.1111/j.1461-0248.2004.00701.x
 
Steel, E.A., Kennedy, M.C., Cunningham, P.G., Stanovick, J.S., 2013. Applied statistics in ecology: common pitfalls and simple solutions. Ecosphere 4, art115. https://doi.org/10.1890/ES13-00160.1
 
Tobler, W.R., 1970. A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 46, 234-240. https://doi.org/10.2307/143141
 
Wang, S., Lamy, T., Hallett, L.M., Loreau, M., 2019. Stability and synchrony across ecological hierarchies in heterogeneous metacommunities: linking theory to data. Ecography 42, 1200-1211. https://doi.org/10.1111/ecog.04290
 
Wiens, J.A., 1989. The ecology of bird communities. Cambridge University Press.
Efficient sampling designs to assess biodiversity spatial autocorrelation : should we go fractal?Fabien Laroche<p>Quantifying the autocorrelation range of species distribution in space is necessary for applied ecological questions, like implementing protected area networks or monitoring programs. However, the power of spatial sampling designs to estimate t...Biodiversity, Landscape ecology, Spatial ecology, Metacommunities & Metapopulations, Statistical ecologyEric Goberville2023-04-21 10:54:29 View
02 Jan 2024
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Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models

Useful clarity on the value of considering temporal variability in detection probability

Recommended by ORCID_LOGO based on reviews by Dana Karelus and Ben Augustine

As so often quoted, "all models are wrong; more specifically, we always neglect potentially important factors in our models of ecological systems. We may neglect these factors because no-one has built a computational framework to include them; because including them would be computationally infeasible; or because we don't have enough data.  When considering whether to include a particular process or form of heterogeneity, the gold standard is to fit models both with and without the component, and then see whether we needed the component in the first place ​-- that is, whether including that component leads to an important difference in our conclusions. However, this approach is both tedious and endless, because there are an infinite number of components that we could consider adding to any given model.

Therefore, thoughtful exercises that evaluate the importance of particular complications under a realistic range of simulations and a representative set of case studies are extremely valuable for the field. While they cannot provide ironclad guarantees, they give researchers a general sense of when they can (probably) safely ignore some factors in their analyses. This paper by Sollmann (2024) shows that for a very wide range of scenarios, temporal and spatiotemporal variability in the probability of detection have little effect on the conclusions of spatial capture-recapture and occupancy models.  The author is thoughtful about when such variability may be important, e.g. when variation in detection and density is correlated and thus confounded, or when variation is driven by animals' behavioural responses to being captured.

Reference

Sollmann R (2024). Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models. bioRxiv, 2023.08.08.552394, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.08.08.552394

Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy modelsRahel Sollmann<p>State variables such as abundance and occurrence of species are central to many questions in ecology and conservation, but our ability to detect and enumerate species is imperfect and often varies across space and time. Accounting for imperfect...Euring Conference, Statistical ecologyBenjamin Bolker Dana Karelus, Ben Augustine, Ben Augustine 2023-08-10 09:18:56 View
27 Nov 2023
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Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees

Gradient Boosted Trees can deliver more than accurate ecological predictions

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Tick-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.
https://doi.org/10.1111/j.1461-0248.2012.01772.x

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 TreesWilliam 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 ecologyTimothée PoisotAnonymous, Anonymous2023-03-23 23:41:17 View
24 Nov 2023
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Consistent individual positions within roosts in Spix's disc-winged bats

Consistent individual differences in habitat use in a tropical leaf roosting bat

Recommended by ORCID_LOGO based on reviews by Annemarie van der Marel and 2 anonymous reviewers

Consistent individual differences in habitat use are found across species and can play a role in who an individual mates with, their risk of predation, and their ability to compete with others (Stuber et al. 2022). However, the data informing such hypotheses come primarily from temperate regions (Stroud & Thompson 2019, Titley et al. 2017). This calls into question the generalizability of the conclusions from this research until further investigations can be conducted in tropical regions.

Giacomini and colleagues (2023) tackled this task in an investigation of consistent individual differences in habitat use in the Central American tropics. They explored whether Spix’s disc-winged bats form positional hierarchies in roosts, which is an excellent start to learning more about the social behavior of this species - a species that is difficult to directly observe. They found that individual bats use their roosting habitat in predictable ways by positioning themselves consistently either in the bottom, middle, or top of the roost leaf. Individuals chose the same positions across time and across different roost sites. They also found that age and sex play a role in which sections individuals are positioned in.

Their research shows that consistent individual differences in habitat use are present in a tropical system, and sets the stage for further investigations into social behavior in this species, particularly whether there is a dominance hierarchy among individuals and whether some positions in the roost are more protective and sought after than others.

References

Giacomini G, Chaves-Ramirez S, Hernandez-Pinson A, Barrantes JP, Chaverri G. (2023). Consistent individual positions within roosts in Spix's disc-winged bats. bioRxiv, https://doi.org/10.1101/2022.11.04.515223 

Stroud, J. T., & Thompson, M. E. (2019). Looking to the past to understand the future of tropical conservation: The importance of collecting basic data. Biotropica, 51(3), 293-299. https://doi.org/10.1111/btp.12665

Stuber, E. F., Carlson, B. S., & Jesmer, B. R. (2022). Spatial personalities: a meta-analysis of consistent individual differences in spatial behavior. Behavioral Ecology, 33(3), 477-486. https://doi.org/10.1093/beheco/arab147 

Titley, M. A., Snaddon, J. L., & Turner, E. C. (2017). Scientific research on animal biodiversity is systematically biased towards vertebrates and temperate regions. PloS one, 12(12), e0189577. https://doi.org/10.1371/journal.pone.0189577

Consistent individual positions within roosts in Spix's disc-winged batsGiada Giacomini, Silvia Chaves-Ramirez, Andres Hernandez-Pinson, Jose Pablo Barrantes, Gloriana Chaverri<p style="text-align: justify;">Individuals within both moving and stationary groups arrange themselves in a predictable manner; for example, some individuals are consistently found at the front of the group or in the periphery and others in the c...Behaviour & Ethology, Social structure, ZoologyCorina Logan2022-11-05 17:39:35 View
21 Nov 2023
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Pathogen community composition and co-infection patterns in a wild community of rodents

Reservoirs of pestilence: what pathogen and rodent community analyses can tell us about transmission risk

Recommended by ORCID_LOGO based on reviews by Adrian Diaz, Romain Pigeault and 1 anonymous reviewer

Rodents are well known as one of the main animal groups responsible for human-transmitted pathogens. As such, it seems logical to try and survey what kinds of pathogenic microbes might be harboured by wild rodents, in order to establish some baseline surveillance and prevent future zoonotic outbreaks (Bernstein et al., 2022). This is exactly what Abbate et al. (2023) endeavoured and their findings are intimidating. Based on quite a large sampling effort, they collected more than 700 rodents of seven species around two villages in northeastern France. They looked for molecular markers indicative of viral and bacterial infections and proceeded to analyze their pathogen communities using multivariate techniques.

Variation in the prevalence of the different pathogens was found among host species, with e.g. signs of CPXV more prevalent in Cricetidae while some Mycoplasma strains were more prevalent in Muridae. Co-circulation of pathogens was found in all species, with some evidencing signs of up to 12 different pathogen taxa. The diversity of co-circulating pathogens was markedly different between host species and higher in adult hosts, but not affected by sex. The dataset also evinced some slight differences between habitats, with meadows harbouring a little more diversity of rodent pathogens than forests. Less intuitively, some pathogen associations seemed quite repeatable, such as the positive association of Bartonella spp. with CPXV in the montane water vole. The study allowed the authors to test several associations already described in the literature, including associations between different hemotropic Mycoplasma species.

I strongly invite colleagues interested in zoonoses, emerging pandemics and more generally One Health to read the paper of Abbate et al. (2023) and try to replicate them across the world. To prevent the next sanitary crises, monitoring rodents, and more generally vertebrates, population demographics is a necessary and enlightening step (Johnson et al., 2020), but insufficient. Following the lead of colleagues working on rodent ectoparasites (Krasnov et al., 2014), we need more surveys like the one described by Abbate et al. (2023) to understand the importance of the dilution effect in the prevalence and transmission of microbial pathogens (Andreazzi et al., 2023) and the formation of epidemics. We also need other similar studies to assess the potential of different rodent species to carry pathogens more or less capable of infecting other mammalian species (Morand et al., 2015), in other places in the world.

References

Abbate, J. L., Galan, M., Razzauti, M., Sironen, T., Voutilainen, L., Henttonen, H., Gasqui, P., Cosson, J.-F. & Charbonnel, N. (2023) Pathogen community composition and co-infection patterns in a wild community of rodents. BioRxiv, ver.4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.02.09.940494 

Andreazzi, C. S., Martinez-Vaquero, L. A., Winck, G. R., Cardoso, T. S., Teixeira, B. R., Xavier, S. C. C., Gentile, R., Jansen, A. M. & D'Andrea, P. S. (2023) Vegetation cover and biodiversity reduce parasite infection in wild hosts across ecological levels and scales. Ecography, 2023, e06579.
https://doi.org/10.1111/ecog.06579
 
Bernstein, A. S., Ando, A. W., Loch-Temzelides, T., Vale, M. M., Li, B. V., Li, H., Busch, J., Chapman, C. A., Kinnaird, M., Nowak, K., Castro, M. C., Zambrana-Torrelio, C., Ahumada, J. A., Xiao, L., Roehrdanz, P., Kaufman, L., Hannah, L., Daszak, P., Pimm, S. L. & Dobson, A. P. (2022) The costs and benefits of primary prevention of zoonotic pandemics. Science Advances, 8, eabl4183.
https://doi.org/10.1126/sciadv.abl4183
 
Johnson, C. K., Hitchens, P. L., Pandit, P. S., Rushmore, J., Evans, T. S., Young, C. C. W. & Doyle, M. M. (2020) Global shifts in mammalian population trends reveal key predictors of virus spillover risk. Proceedings of the Royal Society B: Biological Sciences, 287, 20192736.
https://doi.org/10.1098/rspb.2019.2736
 
Krasnov, B. R., Pilosof, S., Stanko, M., Morand, S., Korallo-Vinarskaya, N. P., Vinarski, M. V. & Poulin, R. (2014) Co-occurrence and phylogenetic distance in communities of mammalian ectoparasites: limiting similarity versus environmental filtering. Oikos, 123, 63-70.
https://doi.org/10.1111/j.1600-0706.2013.00646.x
 
Morand, S., Bordes, F., Chen, H.-W., Claude, J., Cosson, J.-F., Galan, M., Czirjak, G. Á., Greenwood, A. D., Latinne, A., Michaux, J. & Ribas, A. (2015) Global parasite and Rattus rodent invasions: The consequences for rodent-borne diseases. Integrative Zoology, 10, 409-423.
https://doi.org/10.1111/1749-4877.12143

Pathogen community composition and co-infection patterns in a wild community of rodentsJessica Lee Abbate, Maxime Galan, Maria Razzauti, Tarja Sironen, Liina Voutilainen, Heikki Henttonen, Patrick Gasqui, Jean-François Cosson, Nathalie Charbonnel<p style="text-align: justify;">Rodents are major reservoirs of pathogens that can cause disease in humans and livestock. It is therefore important to know what pathogens naturally circulate in rodent populations, and to understand the factors tha...Biodiversity, Coexistence, Community ecology, Eco-immunology & Immunity, Epidemiology, Host-parasite interactions, Population ecology, Species distributionsFrancois Massol2020-02-11 12:42:28 View
15 Nov 2023
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The challenges of independence: ontogeny of at-sea behaviour in a long-lived seabird

On the road to adulthood: exploring progressive changes in foraging behaviour during post-fledging immaturity using remote tracking

Recommended by based on reviews by Juliet Lamb and 1 anonymous reviewer

In most vertebrate species, the period of life spanning from departure from the growing site until reaching a more advanced life stage (immature or adult) is critical. During this period, juveniles are often highly vulnerable because they have not reached the morphological, physiological and behavioural maturity levels of adults yet and are therefore at high risk of mortality, e.g. through starvation, depredation or competition (e.g. Marchetti & Price 1989, Wunderle 1991, Naef-Daenzer & Grüebler 2016). In line with this, juvenile survival is most often far lower than adult survival (e.g. Wooller et al. 1992). In species with parental care, juveniles have to acquire behavioural independence from their parents and possibly establish their own territory during this period of life. Very often, this is also the period that is least well-known in the life cycle (Cox et al. 2014, Naef-Daenzer & Grüebler 2016) because of reduced accessibility to individuals and/or adoption of low conspicuous behaviours. Therefore, our understanding of how juveniles acquire typical adult behaviours and how this progressively increases their survival prospects is still very limited (Naef-Daenzer & Grüebler 2016), and questions such as the length of this transition period or the cognitive (e.g. learning, memorization) mechanisms involved remain largely unresolved. This is particularly true regarding the acquisition of independent foraging behaviour (Marchetti & Price 1989).

Because direct observations of juvenile behaviours are usually very difficult except in specific situations or at the cost of an enormous effort, the use of remote tracking devices can be particularly appealing in this context (e.g. Ponchon et al. 2013, Kays et al. 2015). Over the past decades, technical advances have allowed the monitoring of not only individuals’ movements at both large and small spatial scales but also their activities and behaviours based on different parameters recording e.g. speed of movement or diving depth (Whitford & Klimley 2019). Device miniaturization has in particular allowed smaller species to be equipped and/or longer periods of time to be monitored (e.g. Naef-Daenzer et al. 2005). This has opened up whole fields of research, and has been particularly used on marine seabirds. In these species, individuals are most often inaccessible when at sea, representing most of the time outside (and even within) the breeding season, and the life cycle of these long-lived species can include an extended immature period (up to many years) during which most of them will remain unseen, until they come back as breeders or pre-breeders (e.g. Wooller et al. 1992, Oro & Martínez-Abraín 2009). Survival has been found to increase gradually with age in these species before reaching high values characteristic of the adult stage. However, the mechanisms underlying this increase are still to be deciphered.

The study by Delord et al. (2023) builds upon the hypothesis that juveniles gradually learn foraging techniques and movement strategies, improving their foraging efficiency, as previous data on flight parameters seemed to show in different long-lived bird species. Yet, these previous studies obtained data over a limited period of time, i.e. a few months at best. Whether these data could capture the whole dynamics of the progressive acquisition of foraging and movement skills can only be assessed by measuring behaviour over a longer time period and comparing it to similar data in adults, to account for seasonal variation in relation to both resource availability and energetic demands, e.g. due to molt.

The present study (Delord et al. 2023) addresses these questions by taking advantage of longer-lasting recordings of the location and activity of juvenile, immature and adult birds obtained simultaneously to investigate changes over time in juvenile behaviour and thereby provide hints about how young progressively acquire foraging skills. This study is performed on Amsterdam albatrosses, a highly endangered long-lived sea bird, with obvious conservation issues (Thiebot et al. 2015). The results show progressive changes in foraging effort over the first two months after departure from the birth colony, but large differences remain between life stages over a much longer time frame. They also reveal strong variations between sexes and over time in the year. Overall, this study, therefore, confirms the need for very long-term data to be collected in order to address the question of progressive behavioural maturation and associated survival consequences in such species with strongly deferred maturity. Ideally, the same individuals should be monitored over different life stages, from the juvenile period up to adulthood, but this would require further technical development to release the issue of powering duration limitation.

As reviewers emphasized in the first review round, one main challenge now remains to ascertain the outcome of the observed behavioural changes in foraging behaviour: we expect them to reflect improvement in foraging skills and thus performance of juveniles over time, but this would need to be tested. Collecting data on foraging efficiency is yet another challenge, that future technical developments may also help overcome. Importantly also, data were available only for individuals that could be caught again because the tracking device had to be retrieved from the bird. Here, a substantial fraction of the loggers (one-fifth) could not be found again (Delord et al. 2023). To what extent the birds for which no data could be obtained are a random sample of the equipped birds would also need to be assessed. The further development of remote tracking techniques allowing data to be downloaded from a long distance should help further exploration of behavioural ontogeny of juveniles while maturing and its survival consequences. Because the maturation process explored here is likely to show very different characteristics (e.g. timing and speed) in smaller / shorter-lived species (see Cox et al. 2014, Naef-Daenzer & Grüebler 2016), the development of miniaturization is also expected to allow further investigation of post-fledging behavioural maturation in a wider range of bird species. Our understanding of this crucial life phase in different types of species should thus continue to progress in the coming years.

References

Cox W. A., Thompson F. R. III, Cox A. S. & Faaborg J. 2014. Post-fledging survival in passerine birds and the value of post-fledging studies to conservation. Journal of Wildlife Management, 78: 183-193. https://doi.org/10.1002/jwmg.670

Delord K., Weimerskirch H. & Barbraud C. 2023. The challenges of independence: ontogeny of at-sea behaviour in a long-lived seabird. bioRxiv, ver. 6 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2021.10.23.465439

Kays R., Crofoot M. C., Jetz W. & Wikelski M. 2015. Terrestrial animal tracking as an eye on life and planet. Science, 348 (6240). https://doi.org/10.1126/science.aaa2478

Marchetti K: & Price T. 1989. Differences in the foraging of juvenile and adult birds: the importance of developmental constraints. Biological Reviews, 64: 51-70. https://doi.org/10.1111/j.1469-185X.1989.tb00638.x

Naef-Daenzer B., Fruh D., Stalder M., Wetli P. & Weise E. 2005. Miniaturization (0.2 g) and evaluation of attachment techniques of telemetry transmitters. The Journal of Experimental Biology, 208: 4063–4068. https://doi.org/10.1242/jeb.01870

Naef-Daenzer B. & Grüebler M. U. 2016. Post-fledging survival of altricial birds: ecological determinants and adaptation. Journal of Field Ornithology, 87: 227-250. https://doi.org/10.1111/jofo.12157

Oro D. & Martínez-Abraín A. 2009. Ecology and behavior of seabirds. Marine Ecology, pp.364-389.

Ponchon A., Grémillet D., Doligez B., Chambert T., Tveera T., Gonzàles-Solìs J & Boulinier T. 2013. Tracking prospecting movements involved in breeding habitat selection: insights, pitfalls and perspectives. Methods in Ecology and Evolution, 4: 143-150. https://doi.org/10.1111/j.2041-210x.2012.00259.x

Thiebot J.-B., Delord K., Barbraud C., Marteau C. & Weimerskirch H. 2015. 167 individuals versus millions of hooks: bycatch mitigation in longline fisheries underlies conservation of Amsterdam albatrosses. Aquatic Conservation 26: 674-688. https://doi.org/10.1002/aqc.2578

Whitford M & Klimley A. P. An overview of behavioral, physiological, and environmental sensors used in animal biotelemetry and biologging studies. Animal Biotelemetry, 7: 26. https://doi.org/10.1186/s40317-019-0189-z

Wooller R.D., Bradley J. S. & Croxall J. P. 1992. Long-term population studies of seabirds. Trends in Ecology and Evolution, 7: 111-114. https://doi.org/10.1016/0169-5347(92)90143-y

Wunderle J. M. 1991. Age-specific foraging proficiency in birds. Current Ornithology, 8: 273-324.

The challenges of independence: ontogeny of at-sea behaviour in a long-lived seabirdKarine Delord, Henri Weimerskirch, Christophe Barbraud<p style="text-align: justify;">The transition to independent foraging represents an important developmental stage in the life cycle of most vertebrate animals. Juveniles differ from adults in various life history traits and tend to survive less w...Behaviour & Ethology, Foraging, OntogenyBlandine Doligez2021-10-26 07:51:49 View
09 Nov 2023
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Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets

Marks lost in action, biased estimations

Recommended by ORCID_LOGO based on reviews by Olivier Gimenez, Devin Johnson and 1 anonymous reviewer

Capture-Mark-Recapture (CMR) data are commonly used to estimate ecological variables such as abundance, survival probability, or transition rates from one state to another (e.g. from juvenile to adult, or migration from one site to another). Many studies have shown how estimations can be affected by neglecting one aspect of the population under study (e.g. the heterogeneity in survival between individuals) or one limit of the methodology itself (e.g. the fact that observers might not detect an individual although it is still alive). Strikingly, very few studies have yet assessed the robustness of one fundamental assumption of all CMR-based inferences: marks are supposed definitive and immutable. If they are not, how are estimations affected? Addressing this issue is the main goal of the paper by Touzalin et al. (2023), and they did a very nice work. But, because the answer is not that simple, it also calls for further investigations.

When and why would mark loss bias estimation? In at least two situations. First, when estimating survival rates: if an individual loses its mark, it will be considered as dead, hence death rates will be overestimated. Second, more subtly, when estimating transition rates: if one individual loses its mark at the specific moment where its state changes, then a transition will be missed in data. The history of the marked individual would then be split into two independent CMR sequences as if there were two different individuals, including one which died.

Touzalin et al. (2023) thoroughly studied these two situations by estimating ecological parameters on 1) well-thought simulated datasets, that cover a large range of possible situations inspired from a nice compilation of hundreds of estimations from fish and bats studies, and 2) on their own bats dataset, for which they had various sources of information about mark losses, i.e. different mark types on the same individuals, including mark based on genotypes, and marks found on the soil in the place where bats lived. Their main findings from the simulated datasets are that there is a general trend for underestimation of survival and transition rates if mark loss is not accounting for in the model, as it would be intuitively expected. However, they also showed from the bats dataset that biases do not show any obvious general trend, suggesting complex interactions between different ecological processes and/or with the estimation procedure itself.

The results by Touzalin et al. (2023) strongly suggest that mark loss should systematically be included in models estimating parameters from CMR data. In addition to adapt the inferential models, the authors also recommend considering either a double marking, or even a single but ‘permanent’ mark such as one based on the genotypes. However, the potential gain of a double marking or of the use of genotypes is still to be evaluated both in theory and practice, and it seems to be not that obvious at first sight. First because double marking can be costly for experimenters but also for the marked animals, especially as several studies showed that marks can significantly affect survival or recapture rates. Second because multiple sources of errors can affect genotyping, which would result in wrong individual assignations especially in populations with low genetic diversity or high inbreeding, or no individual assignation at all, which would increase the occurrence of missing data in CMR datasets. Touzalin et al. (2023) supposed in their paper that there were no genotyping errors, but one can doubt it to be true in most situations. They have now important and interesting other issues to address.

References

Frédéric Touzalin, Eric J. Petit, Emmanuelle Cam, Claire Stagier, Emma C. Teeling, Sébastien J. Puechmaille (2023) Mark loss can strongly bias demographic rates in multi-state models: a case study with simulated and empirical datasets. BioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.03.25.485763

Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasetsFrédéric Touzalin, Eric J. Petit, Emmanuelle Cam, Claire Stagier, Emma C. Teeling, Sébastien J. Puechmaille<p style="text-align: justify;">1. The development of methods for individual identification in wild species and the refinement of Capture-Mark-Recapture (CMR) models over the past few decades have greatly improved the assessment of population demo...Conservation biology, DemographySylvain Billiard2022-04-12 18:49:34 View
06 Nov 2023
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Influence of mimicry on extinction risk in Aculeata: a theoretical approach

Mullerian and Batesian mimicry can influence population and community dynamics

Recommended by based on reviews by Jesus Bellver and 1 anonymous reviewer

Mimicry between species has long attracted the attention of scientists. Over a century ago, Bates first proposed that palatable species should gain a benefit by resembling unpalatable species (Bates 1862). Not long after, Müller suggested that there could also be a mutual advantage for two unpalatable species to mimic one another to reduce predator error (Müller 1879). These forms of mimicry, Batesian and Müllerian, are now widely studied, providing broad insights into behaviour, ecology and evolution.

Numerous taxa, including both invertebrates and vertebrates, show examples of Batesian or Müllerian mimicry. Bees and wasps provide a particularly interesting case due to the differences in defence between females and males of the same species. While both males and females may display warning colours, only females can sting and inject venom to cause pain and allow escape from predators. Therefore, males are palatable mimics and can resemble females of their own species or females of another species (dual sex-limited mimicry). This asymmetry in defence could have impacts on both population structure and community assembly, yet research into mimicry largely focuses on systems without sex differences.

Here, Boutin and colleagues (2023) use a differential equations model to explore the effect of mimicry on population structure and community assembly for sex-limited defended species. Specifically, they address three questions, 1) how do female noxiousness and sex-ratio influence the extinction risk of a single species?; 2) what is the effect of mimicry on species co-existence? and 3) how does dual sex-limited mimicry influence species co-existence? Their results reveal contexts in which populations with undefended males can persist, the benefit of Müllerian mimicry for species coexistence and that dual sex-limited mimicry can have a destabilising impact on species coexistence.

The results not only contribute to our understanding of how mimicry is maintained in natural systems but also demonstrate how changes in relative abundance or population structure of one species could impact another species. Further insight into the population and community dynamics of insects is particularly important given the current population declines (Goulson 2019; Seibold et al 2019).

References

Bates, H. W. 1862. Contributions to the insect fauna of the Amazon Valley, Lepidoptera: Heliconidae. Trans. Linn. Soc. Lond. 23:495- 566. https://doi.org/10.1111/j.1096-3642.1860.tb00146.x

Boutin, M., Costa, M., Fontaine, C., Perrard, A., Llaurens, V. 2022 Influence of sex-limited mimicry on extinction risk in Aculeata: a theoretical approach. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.10.21.513153

Goulson, D. 2019. The insect apocalypse, and why it matters. Curr. Biol. 29: R967-R971. https://doi.org/10.1016/j.cub.2019.06.069

Müller, F. 1879. Ituna and Thyridia; a remarkable case of mimicry in butterflies. Trans. Roy. Entom. Roc. 1879:20-29.

Seibold, S., Gossner, M. M., Simons, N. K., Blüthgen, N., Müller, J., Ambarlı, D., ... & Weisser, W. W. 2019. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature, 574: 671-674. https://doi.org/10.1038/s41586-019-1684-3

Influence of mimicry on extinction risk in Aculeata: a theoretical approachMaxime Boutin, Manon Costa, Colin Fontaine, Adrien Perrard, Violaine Llaurens<p style="text-align: justify;">Positive ecological interactions, such as mutualism, can play a role in community structure and species co-existence. A well-documented case of mutualistic interaction is Mullerian mimicry, the convergence of colour...Biodiversity, Coexistence, Eco-evolutionary dynamics, Evolutionary ecology, Facilitation & MutualismAmanda Franklin2022-10-25 19:11:55 View
23 Oct 2023
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The Moa the Merrier: Resolving When the Dinornithiformes Went Extinct

Are Moas ancient Lazarus species?

Recommended by ORCID_LOGO based on reviews by Tim Coulson and Richard Holdaway

Ancient human colonisation often had catastrophic consequences for native fauna. The North American Megafauna went extinct shortly after humans entered the scene and Madagascar suffered twice, before 1500 CE and around 1700 CE after the Malayan and European colonisation. Maoris colonised New Zealand by about 1300 and a century later the giant Moa birds (Dinornithiformes) sharply declined. But did they went extinct or are they an ancient example of Lazarus species, species thought to be extinct but still alive? Scattered anecdotes of late sightings of living Moas even up to the 20th century seem to suggest the latter. The quest for later survival has also a criminal aspect. Who did it, the Maoris or the white colonisers in the late 18th century?

The present work by Floe Foxon (2023) tries to settle this question. It uses a survival modelling approach and an assessment of the reliability of nearly 100 alleged sightings. The model favours the so-called overkill hypothesis, that Moas probably went extinct in the 15th century shortly after Maori colonisation. A small but still remarkable probability remained for survival up to 1770. Later sightings turned out to be highly unreliable.

The paper is important as it does not rely on subjective discussions of late sightings but on a probabilistic modelling approach with sensitivity testing prior applied to marsupials. As common in probabilistic approaches, the study does not finally settle the case. A probability of as much as 20% remained for late survival after 1450 CE. This is not improbable as New Zealand was sufficiently unexplored in those days to harbour a few refuges for late survivors. However, in this respect, it is a bit unfortunate that at the end of the discussion, the paper cites Heuvelmans, the founder of cryptozoology, and it mentions the ivory-billed woodpecker, which has recently been redetected. No Moa remains were found after 1450.

References

Foxon F (2023) The Moa the Merrier: Resolving When the Dinornithiformes Went Extinct. bioRxiv, 2023.08.07.552261, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.08.07.552261

The Moa the Merrier: Resolving When the Dinornithiformes Went ExtinctFloe Foxon<p style="text-align: justify;">The Moa (Aves: Dinornithiformes) are an extinct group of the ratite clade from New Zealand. The overkill hypothesis asserts that the first New Zealand settlers hunted the Moa to extinction by 1450 CE, whereas the st...Conservation biology, Human impact, Statistical ecology, ZoologyWerner Ulrich Tim Coulson, Richard Holdaway2023-08-08 17:14:30 View
11 Oct 2023
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Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis

Disentangling microbial exopolymer dynamics in intertidal sediments

Recommended by and ORCID_LOGO based on reviews by 2 anonymous reviewers

The secretion of extracellular polymeric substances (EPS) enables microorganisms to shape and interact with their environment [1]. EPS support cell adhesion and motility, offer protection from unfavorable conditions, and facilitate nutrient acquisition and transfer between microorganisms [2]. EPS production and consumption thus control the formation and structural organization of biofilms [3]. However, in marine environments, our understanding of the sources and composition of EPS is limited.
 
In this study, Hubas et al. [4] compare the carbon and nitrogen isotope ratios in EPS with the carbon isotope ratios of fatty acid biomarkers to identify the main EPS producers in intertidal sediments. The authors find pronounced differences in the diversity, composition, isotope signatures, and production/consumption dynamics of EPS between muddy and sandy environments. While the contribution of diatoms was highest in the bound fraction of EPS in muddy environments, diatom contribution was highest in the colloidal fraction of EPS in sandy environments. These differences between sites likely reflect the functional differences in EPS dynamics of epipelic and episammic sediment communities.
 
Taken together, the innovative approach of the authors provides insights into the diversity and origin of EPS in microphytobenthic communities and highlights the importance of different microbial groups in EPS production. These findings are vital for understanding EPS dynamics in microbial interactions and their role in the functioning of coastal ecosystems.

References

  1. Flemming, H.-C. (2016) EPS-then and now. Microorganisms 4, 41 https://doi.org/10.3390/microorganisms4040041
  2. Wolfaardt, G.M. et al. (1999) Function of EPS. In Microbial Extracellular Polymeric Substances, pp. 171–200, Springer Berlin Heidelberg https://doi.org/10.1007/978-3-642-60147-7
  3. Flemming, H.-C. et al. (2007) The EPS matrix: the “house of biofilm cells.” J. Bacteriol. 189, 7945–7947 https://doi.org/10.1128/jb.00858-07
  4. Hubas, C. et al. (2022) Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.12.02.516908
Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysisCédric Hubas, Julie Gaubert-Boussarie, An-Sofie D’Hondt, Bruno Jesus, Dominique Lamy, Vona Meleder, Antoine Prins, Philippe Rosa, Willem Stock, Koen Sabbe<p style="text-align: justify;">Extracellular polymeric substances (EPS) refer to a wide variety of high molecular weight molecules secreted outside the cell membrane by biofilm microorganisms. In the present study, EPS from marine microphytobenth...Biodiversity, Ecological stoichiometry, Ecosystem functioning, Food webs, Marine ecology, Microbial ecology & microbiology, Soil ecologyUte Risse-Buhl2022-12-06 14:13:11 View