This very interesting article describes the changes taking place on artificial substrates placed in an intertidal zone. The study presents an ambitious data set and demonstrates the importance of long-term monitoring to identify community dynamics. In summary, in the short term, the authors observe a phase of complexification of the communities and a peak in productivity, but after a few years, the macro-algae disappear in favour of limpets, a situation that persists after 10 years of monitoring. Monitoring over the short term would lead to an erroneous analysis of the succession patterns and dynamics of the communities, which has important consequences in terms of the recolonisation of artificial substrates in the marine environment.

References

Aline Migné, François Bordeyne, Dominique Davoult (2023) Long-term survey of intertidal rocky shore macrobenthic community metabolism and structure after primary succession. HAL, ver.2 peer-reviewed and recommended by PCI Ecology https://hal.science/hal-04347756

Accurately estimating survival rate and identifying the drivers of its variation is essential for our understanding of population dynamics and life history strategies (Sæther and Bakke 2000), as well as for population management and conservation (Francis et al. 1998, Doherty et al. 2014). Many studies estimate survival from capture–recapture data using the Cormack–Jolly–Seber (CJS) model (Lebreton et al. 1992). However, survival estimates are confounded with permanent emigration from the study area, which can be particularly problematic for mobile species. This is problematic, not only because CJS models under estimate true survival in populations where permanent emigration occurs (i.e. they estimate “apparent” survival), but also because some factors of interest may affect both survival and emigration (e.g., habitat quality, Paquet et al. 2020), leaving the interpretation of results challenging, for example in terms of management decisions.

Several methods have been developed to account for permanent emigration when estimating survival, for example by jointly analyzing CMR data with data on individuals’ locations at each capture/resighting site (to estimate their dispersal distances; Schaub and Royle 2013, Badia Boher et al. 2023), with telemetry data (Powel et al. 2000), mark recovery data (Burnham 1993, Fay et al. 2019), or with live-resight data (Barker 1997).

The Barker joint live-recapture/live-resight (JLRLR) model can estimate survival when resight data are continuous over a long interval and from a larger area than the capture recapture data. This model becomes particularly promising with the growing collection of data from citizen science, or remote detection tools (Dzul et al. 2023). However, as pointed out by Dzul et al., this model assumes that resight probability is homogeneous across the area where individuals can move, and this assumption is likely violated for example because of non-random movements or because of non-random location of resighting sites.

In their manuscript, Dzul et al. performed a thorough simulation study to evaluate the accuracy of survival estimates from JLRLR models under various study designs regarding the location of resight sites (global, random, fixed including the capture site, and fixed excluding the capture site). They simulated data with varying survival and movement values, varying recapture and resight probabilities, and varying sample sizes. Finally, they also developed and fitted a multi state version of the JLRLR model. They show that JLRLR models performed better than CJS models. Survival estimates were still often biased (either positively or negatively) but they were less biased when sesight sites were randomly located (rather than at fixed locations), when recapture sites were included in the resighting design, and when using the multi state JLRLR model they developed.

This study highlights (multistate) JLRLR models as an alternative to CJS models one should consider when auxiliary resight data can be collected. Moreover, it shows the importance of evaluating not only model performance, but also the efficiency of alternative sampling designs before choosing one for our studies. Hopefully, this study will help the authors and other researchers making a more informed and efficient choice of model and design to estimate survival in their study populations.

References

Jaume A. Badia-Boher, Joan Real, Joan Lluís Riera, Frederic Bartumeus, Francesc Parés, Josep Maria Bas, and Antonio Hernández-Matías. Joint estimation of survival and dispersal effectively corrects the permanent emigration bias in mark-recapture analyses. (2023) Scientific reports 13, no. 1: 6970. https://doi.org/10.1038/s41598-023-32866-0 

Richard J Barker (1997) Joint modeling of live-recapture, tag-resight, and tag-recovery data. Biometrics: 666-677. https://doi.org/10.2307/2533966 

Kenneth P. Burnham (1993) Marked Individuals in the Study of Bird Populations (ed. J.D. Lebreton), pp. 199–213. Birkhäuser, Basel

Kevin E. Doherty, David E. Naugle, Jason D. Tack, Brett L. Walker, Jon M. Graham, Jeffrey L. Beck (2014) Linking conservation actions to demography: grass height explains variation in greater sage‐grouse nest survival. Wildlife biology 20, no. 6 : 320-325. https://doi.org/10.2981/wlb.00004

Maria C. Dzul, Charles B. Yackulic, William L. Kendall (2023) The importance of sampling design for unbiased estimation of survival using joint live-recapture and live resight models. arXiv, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.48550/arXiv.2312.13414

Rémi Fay, Stephanie Michler, Jacques Laesser, and Michael Schaub (2019) Integrated population model reveals that kestrels breeding in nest boxes operate as a source population. Ecography 42, no. 12: 2122-2131. https://doi.org/10.1111/ecog.04559

Charles M. Francis, John R. Sauer, Jerome R. Serie (1998) Effect of restrictive harvest regulations on survival and recovery rates of American black ducks. The Journal of Wildlife Management : 1544-1557. https://doi.org/10.2307/3802021

Jean-Dominique Lebreton, Kenneth P. Burnham, Jean Clobert, David R. Anderson (1992) Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecological monographs 62.1: 67-118. https://doi.org/10.2307/2937171

Matthieu Paquet, Debora Arlt, Jonas Knape, Matthew Low, Pär Forslund, and Tomas Pärt (2020) Why we should care about movements: Using spatially explicit integrated population models to assess habitat source–sink dynamics. Journal of Animal Ecology 89, no. 12: 2922-2933. https://doi.org/10.1111/1365-2656.13357

Larkin A. Powell, Michael J. Conroy, James E. Hines, James D. Nichols, and David G. Krementz. Simultaneous use of mark-recapture and radiotelemetry to estimate survival, movement, and capture rates. (2000) The Journal of Wildlife Management : 302-313. https://doi.org/10.2307/3803003

Bernt-Erik Sæther, Øyvind Bakke (2000) Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81.3 : 642-653. https://doi.org/10.1890/0012-9658(2000)081[0642:ALHVAC]2.0.CO;2

Michael Schaub, J. Andrew Royle. Estimating true instead of apparent survival using spatial Cormack–Jolly–Seber models (2014) Methods in Ecology and Evolution 5, no. 12: 1316-1326. https://doi.org/10.1111/2041-210X.12134

As biodiversity has become a major global concern for a variety of stakeholders, and society in general, assessments of biodiversity trends at all spatial scales have flourished in the past decades. To assess trends, one needs data, and the more precise the data, the more precise the trend. Or, if precision is not perfect, uncertainty in the data must be acknowledged and accounted for. Such considerations have already been raised in ecology, most notably regarding the value of species distribution data to model the current and future distribution of species (Rocchini et al., 2011, Duputié et al., 2014, Tessarolo et al., 2021), leading to serious doubts regarding the value of public databases (Maldonado et al., 2015). And more recently similar issues have been raised regarding databases of species traits (Augustine et al., 2024), emphasizing the importance of good data practice and traceability.

Science is by nature a self-correcting human process, with many steps of the scientific activity prone to errors and misinterpretations. Collation of ecological data, sadly, is proof of this. Spurred by the astonishing results of Hallmann et al. (2017) regarding the decline of insect biomass, and to more precisely answer the question of biodiversity trends in insects and settle an ongoing debate (Cardinale et al., 2018), van Klink et al. (2020, 2021) established the InsectChange database. Several perceptive comments have already been made regarding the possible issues in the methods and interpretations of this study (Desquilbet et al., 2020, Jähnig et al., 2021, Duchenne et al., 2022). However, the biggest issue might have been finally unearthed by Gaume & Desquilbet (2024): with poorly curated data, the InsectChange database is unlikely to support most of the initial claims regarding insect biodiversity trends.

The compilation of errors and inconsistencies present in InsectChange and evinced by Gaume & Desquilbet (2024) is stunning to say the least, with a mix of field and experimental data combined without regard for experimental manipulation of environmental factors, non-standardised transformations of abundances, the use of non-insect taxa to compute insect trends, and inadequate geographical localizations of samplings. I strongly advise all colleagues interested in the study of biodiversity from global databases to consider the points raised by the authors, as it is quite likely that other databases might suffer from the same ailments as well. Reading this paper is also educating and humbling in its own way, since the publication of the original papers based on InsectChange seems to have proceeded without red flags from reviewers or editors. The need for publishing fast results that will make the next buzz, thus obeying the natural selection of bad science (Smaldino and McElreath, 2016), might be the systemic culprit. However, this might also be the opportunity ecology needs to consider the reviewing and curation of data as a crucial step of science quality assessment. To make final assessments, let us proceed with less haste.

References

Augustine, S. P., Bailey-Marren, I., Charton, K. T., Kiel, N. G. & Peyton, M. S. (2024) Improper data practices erode the quality of global ecological databases and impede the progress of ecological research. Global Change Biology, 30, e17116. https://doi.org/10.1111/gcb.17116

Cardinale, B. J., Gonzalez, A., Allington, G. R. H. & Loreau, M. (2018) Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biological Conservation, 219, 175-183. https://doi.org/10.1016/j.biocon.2017.12.021

Desquilbet, M., Gaume, L., Grippa, M., Céréghino, R., Humbert, J.-F., Bonmatin, J.-M., Cornillon, P.-A., Maes, D., Van Dyck, H. & Goulson, D. (2020) Comment on “Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances”. Science, 370, eabd8947. https://doi.org/10.1126/science.abd8947

Duchenne, F., Porcher, E., Mihoub, J.-B., Loïs, G. & Fontaine, C. (2022) Controversy over the decline of arthropods: a matter of temporal baseline? Peer Community Journal, 2. https://doi.org/10.24072/pcjournal.131

Duputié, A., Zimmermann, N. E. & Chuine, I. (2014) Where are the wild things? Why we need better data on species distribution. Global Ecology and Biogeography, 23, 457-467. https://doi.org/10.1111/geb.12118

Gaume, L. & Desquilbet, M. (2024) InsectChange: Comment. biorXiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2023.06.17.545310

Hallmann, C. A., Sorg, M., Jongejans, E., Siepel, H., Hofland, N., Schwan, H., Stenmans, W., Müller, A., Sumser, H., Hörren, T., Goulson, D. & de Kroon, H. (2017) More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLOS ONE, 12, e0185809. https://doi.org/10.1371/journal.pone.0185809

Jähnig, S. C., Baranov, V., Altermatt, F., Cranston, P., Friedrichs-Manthey, M., Geist, J., He, F., Heino, J., Hering, D., Hölker, F., Jourdan, J., Kalinkat, G., Kiesel, J., Leese, F., Maasri, A., Monaghan, M. T., Schäfer, R. B., Tockner, K., Tonkin, J. D. & Domisch, S. (2021) Revisiting global trends in freshwater insect biodiversity. WIREs Water, 8, e1506. https://doi.org/10.1002/wat2.1506

Maldonado, C., Molina, C. I., Zizka, A., Persson, C., Taylor, C. M., Albán, J., Chilquillo, E., Rønsted, N. & Antonelli, A. (2015) Estimating species diversity and distribution in the era of Big Data: to what extent can we trust public databases? Global Ecology and Biogeography, 24, 973-984. https://doi.org/10.1111/geb.12326

Rocchini, D., Hortal, J., Lengyel, S., Lobo, J. M., Jiménez-Valverde, A., Ricotta, C., Bacaro, G. & Chiarucci, A. (2011) Accounting for uncertainty when mapping species distributions: The need for maps of ignorance. Progress in Physical Geography, 35, 211-226. https://doi.org/10.1177/0309133311399491

Smaldino, P. E. & McElreath, R. (2016) The natural selection of bad science. Royal Society Open Science, 3. https://doi.org/10.1098/rsos.160384

Tessarolo, G., Ladle, R. J., Lobo, J. M., Rangel, T. F. & Hortal, J. (2021) Using maps of biogeographical ignorance to reveal the uncertainty in distributional data hidden in species distribution models. Ecography, 44, 1743-1755. https://doi.org/10.1111/ecog.05793

van Klink, R., Bowler, D. E., Comay, O., Driessen, M. M., Ernest, S. K. M., Gentile, A., Gilbert, F., Gongalsky, K. B., Owen, J., Pe'er, G., Pe'er, I., Resh, V. H., Rochlin, I., Schuch, S., Swengel, A. B., Swengel, S. R., Valone, T. J., Vermeulen, R., Wepprich, T., Wiedmann, J. L. & Chase, J. M. (2021) InsectChange: a global database of temporal changes in insect and arachnid assemblages. Ecology, 102, e03354. https://doi.org/10.1002/ecy.3354

van Klink, R., Bowler, D. E., Gongalsky, K. B., Swengel, A. B., Gentile, A. & Chase, J. M. (2020) Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science, 368, 417-420. https://doi.org/10.1126/science.aax9931

Population ecologists are in the business of decrypting the drivers of variation in the abundance of organisms across space and time (Begon et al. 1986). Comprehensive studies of wild vertebrate populations which provide the necessary information on variations in vital rates in relation to environmental conditions to construct informative models of large-scale population dynamics are rare, ostensibly because of the huge effort required to monitor individuals across ecological contexts and over generations. In this current aim, Nater et al. (2024) are leading the way forward by combining distance sampling data collected through a large-scale citizen science (Fraisl et al. 2022) programme in Norway with state-of-the-art modelling approaches to build a comprehensive overview of the population dynamics of willow ptarmigan. Their work enhances our fundamental understanding of this system and provides evidence-based tools to improve its management (Williams et al. 2002). Even better, they are working for the common good, by providing an open-source workflow that should enable ecologists and managers together to predict what will happen to their favourite model organism when the planet throws its next curve ball. In the case of the ptarmigan, for example, it seems that the impact of climate change on their population dynamics will differ across the species’ distributional range, with a slower pace of life (sensu Stearns 1983) at higher latitudes and altitudes. 

On a personal note, I have often mused whether citizen science, with its inherent limits and biases, was just another sticking plaster over the ever-deeper cuts in the research budgets to finance long-term ecological research. Here, Nater et al. are doing well to convince me that we would be foolish to ignore such opportunities, particularly when citizens are engaged, motivated, with an inherent capacity for the necessary discipline to employ common protocols in a standardised fashion. A key challenge for us professional ecologists is to inculcate the next generation of citizens with a sense of their opportunity to contribute to a better understanding of the natural world.

References

Begon, Michael, John L Harper, and Colin R Townsend. 1986. Ecology: individuals, populations and communities. Blackwell Science.

Fraisl, Dilek, Gerid Hager, Baptiste Bedessem, Margaret Gold, Pen-Yuan Hsing, Finn Danielsen, Colleen B Hitchcock, et al. 2022. Citizen Science in Environmental and Ecological Sciences. Nature Reviews Methods Primers 2 (1): 64. https://doi.org/10.1038/s43586-022-00144-4

Chloé R. Nater, Francesco Frassinelli, James A. Martin, Erlend B. Nilsen (2024) Large-scale spatio-temporal variation in vital rates and population dynamics of an alpine bird. EcoEvoRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.32942/X2VP6J

Stearns, S.C. 1983. The influence of size and phylogeny of covariation among life-history traits in the mammals. Oikos, 41, 173–187. https://doi.org/10.2307/3544261

Williams, Byron K, James D Nichols, and Michael J Conroy. 2002. Analysis and Management of Animal Populations. Academic Press.

 Understanding the effects of environmental perturbations on coexistence is a key challenge in ecology, and models have played an important role in structuring our ideas and generating predictions, leading to quantitative hypotheses. In such models, environmental perturbations are often captured by changes in parameter values, such as the intrinsic growth rates of species (1–3). The question then becomes how much one can change these parameters without breaking coexistence and thus losing species (4). 
 
An intuitively appealing approach to address this question is to calculate a model’s feasibility domain (5–7). Loosely defined, it is the fraction of parameter settings leading to the coexistence of all species. Mathematically speaking, it is a high-dimensional triangle, of which one can calculate the size, just as for plain two-dimensional triangles. Parameter settings outside of this triangle break coexistence. Thus, it seems logical that greater feasibility domains would make for more robust ecosystems. However, careful interpretation is key: a greater feasibility domain merely implies that across many attempts at running a model with different random parameter settings, coexistence will be more frequent. It does not necessarily inform us how much one can perturb the parameters of a community with a predefined parameter setting. To get this information, we also need to know the shape of the triangle (7): perturbations more easily knock the parameter setting out of a flat triangle than out of an equilateral triangle. 
 
Desaillais et al. (8) develop a new theory that sheds light on what drives the shape of the feasibility domain. Specifically, they present the probability distribution that tells how close to the edge of the feasibility domain the parameter settings in that domain tend to be. For example, all points in a very flat triangle are close to its edge, while in an equilateral triangle, most points are safely stowed inside. The results show how, in a Lotka-Volterra model, the matrix of species interactions fully defines this distribution, which makes the technique empirically applicable in so far as one can estimate these interactions. The analysis then continues to explore the role of specific species in putative loss of coexistence. Desaillais et al. identify two species-level quantities: the first measures the total influence of the surrounding community on a focal species, while the second is a proxy for how close that focal species is to being lost, should a perturbation occur. While these two quantities are not mathematically independent, their correlation is not perfect, allowing one to categorize species into distinct ecological roles. A dataset of plant communities with different compositions illustrates how to apply this idea and gain some additional insight into the robustness of coexistence. These results pave the way for a number of potentially rewarding applications. How does the robustness of coexistence differ across network types? For which network types do we find back a more diverse set of ecological roles for species, i.e. for which networks are the two quantities least correlated? 

References

1. Baert, J.M., Janssen, C.R., Sabbe, K., and De Laender, F. (2016). Per capita interactions and stress tolerance drive stress-induced changes in biodiversity effects on ecosystem functions. Nat. Commun. 7, 12486. https://doi.org/10.1038/ncomms12486

2. Pásztor, L., Botta-Dukat, Z., Magyar, G., Czaran, T., and Meszéna, G. (2016). Theory-based ecology: A Darwinian approach 1st ed. (Oxford University Press).

3. Cenci, S., Montero-Castaño, A., and Saavedra, S. (2018). Estimating the effect of the reorganization of interactions on the adaptability of species to changing environments. J. Theor. Biol. 437, 115–125. https://doi.org/10.1016/j.jtbi.2017.10.016

4. Spaak, J.W., Baert, J.M., Baird, D.J., Eisenhauer, N., Maltby, L., Pomati, F., Radchuk, V., Rohr, J.R., Van den Brink, P.J., and De Laender, F. (2017). Shifts of community composition and population density substantially affect ecosystem function despite invariant richness. Ecol. Lett. 20, 1315–1324. https://doi.org/10.1111/ele.12828

5. Meszéna, G., Gyllenberg, M., Pásztor, L., and Metz, J.A.J. (2006). Competitive exclusion and limiting similarity: A unified theory. Theor. Popul. Biol. 69, 68–87. https://doi.org/10.1016/j.tpb.2005.07.001

6. Saavedra, S., Rohr, R.P., Bascompte, J., Godoy, O., Kraft, N.J.B., and Levine, J.M. (2017). A structural approach for understanding multispecies coexistence. Ecol. Monogr. 87, 470–486. https://doi.org/10.1002/ecm.1263

7. Grilli, J., Adorisio, M., Suweis, S., Barabás, G., Banavar, J.R., Allesina, S., and Maritan, A. (2017). Feasibility and coexistence of large ecological communities. Nat. Commun. 8. https://doi.org/10.1038/ncomms14389

8. Desallais M, Loreau M, Arnoldi J.F. (2024) The distribution of distances to the edge of species coexistence. bioRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2024.01.21.575550