High-throughput sequencing-based techniques such as DNA metabarcoding are increasingly advocated as providing numerous benefits over morphology‐based identifications for biodiversity inventories and ecosystem biomonitoring [1]. These benefits are particularly apparent for highly-diversified and/or hardly accessible aquatic and marine environments, where simple water or sediment samples could already produce acceptably accurate biodiversity estimates based on the environmental DNA present in the samples [2,3]. However, sequence-based characterization of biodiversity comes with its own challenges. A major one resides in the capacity to disentangle true biological diversity (be it taxonomic or genetic) from artefactual diversity generated by sequence-errors accumulation during PCR and sequencing processes, or from the amplification of non-target genes (i.e. pseudo-genes). On one hand, the stringent elimination of sequence variants might lead to biodiversity underestimation through the removal of true species, or the clustering of closely-related ones. On the other hand, a more permissive sequence filtering bears the risks of biodiversity inflation. Recent studies have outlined an excellent methodological framework for addressing this issue by proposing bioinformatic tools that allow the amplicon-specific error-correction as alternative or as complement to the more arbitrary approach of clustering into Molecular Taxonomic Units (MOTUs) based on sequence dissimilarity [4,5]. But to date, the relevance of amplicon-specific error-correction tools has been demonstrated only for a limited set of taxonomic groups and gene markers.
The study of Brandt et al. [6] successfully builds upon existing methodological frameworks for filling this gap in current literature. By proposing a bioinformatic pipeline combining Amplicon Sequence Variants (ASV) curation with MOTU clustering and additional post-clustering curation, the authors show that contrary to previous recommendations, ASV-based curation alone does not represent an adequate approach for DNA metabarcoding-based inventories of metazoans. Metazoans indeed, do exhibit inherently higher intra-specific and intra-individual genetic variability, necessarily leading to biased biodiversity estimates unbalanced in favor of species with higher intraspecific diversity in the absence of MOTU clustering. Interestingly, the positive effect of additional clustering showed to be dependent on the target gene region. Additional clustering had proportionally higher effect on the more polymorphic mitochondrial COI region (as compared to the 18S ribosomal gene). Thus, the major advantage of the study lies in the provision of optimal curation parameters that reflect the best possible balance between minimizing the impact of PCR/sequencing errors and the loss of true biodiversity across markers with contrasting levels of intragenomic variation. This is important as combining multiple markers is increasingly considered for improving the taxonomic coverage and resolution of data in DNA metabarcoding studies.
Another critical aspect of the study is the taxonomic assignation of curated OTUs (which is also the case for the majority of DNA metabarcoding-based biodiversity assessments). Facing the double challenge of focusing on taxonomic groups that are both highly diverse and poorly represented in public sequence reference databases, the authors failed to obtain high-resolution taxonomic assignments for several of the most closely-related species. As a result, taxa with low divergence levels were clustered as single taxonomic units, subsequently leading to underestimation of true biodiversity present. This finding adds to the argument that in order to be successful, sequence-based techniques still require the availability of comprehensive, high-quality reference databases.
Perhaps the only regret we might have with the study is the absence of mock community validation for the prokaryotes compartment. Even though the analyses of natural samples seem to suggest a positive effect of the curation pipeline, the concept of intra- versus inter-species variation in naturally occurring prokaryote communities remains at best ambiguous. Of course, constituting a representative sample of taxonomically-resolved prokaryote taxa from deep-sea habitats does not come without difficulties but has the benefit of opening opportunities for further studies on the matter.

References

[1] Porter, T. M., and Hajibabaei, M. (2018). Scaling up: A guide to high-throughput genomic approaches for biodiversity analysis. Molecular Ecology, 27(2), 313–338. doi: 10.1111/mec.14478
[2] Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J., Thomsen, P. F., … Dejean, T. (2016). Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Molecular Ecology, 25(4), 929–942. doi: 10.1111/mec.13428
[3] Leray, M., and Knowlton, N. (2015). DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity. Proceedings of the National Academy of Sciences, 112(7), 2076–2081. doi: 10.1073/pnas.1424997112
[4] Callahan, B. J., McMurdie, P. J., and Holmes, S. P. (2017). Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. The ISME Journal, 11(12), 2639–2643. doi: 10.1038/ismej.2017.119
[5] Edgar, R. C. (2016). UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv, 081257. doi: 10.1101/081257
[6] Brandt, M. I., Trouche, B., Quintric, L., Wincker, P., Poulain, J., and Arnaud-Haond, S. (2020). A flexible pipeline combining clustering and correction tools for prokaryotic and eukaryotic metabarcoding. BioRxiv, 717355, ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/717355

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

Humans are dramatically modifying many aspects of our planet via increasing concentrations of carbon dioxide in the atmosphere, patterns of land-use change, and unsustainable exploitation of the planet’s resources. These changes impact the abundance of species of wild organisms, with winners and losers. Identifying how different species and groups of species are influenced by anthropogenic activity in different biomes, continents, and habitats, has become a pressing scientific question with many publications reporting analyses of disparate data on species population sizes. Many conclusions are based on the linear analysis of rather short time series of organismal abundances.
 
There has been particular interest in how arthropods are impacted by environmental change, with several recent papers reporting contradictory results. To investigate why these contradictions might arise, Duchenne et al. (2022) conducted an analysis of four published data sets along with a series of experimental analyses of simulated time series to examine the power of widely used statistical analyses to gain inference on temporal trends. Their important paper reveals that accurate inference on dynamics, particularly of species that exhibit large temporal fluctuations in abundance, requires time series that are substantially longer than are typically collected, as well as careful thought as to whether linear models are appropriate. Linear analyses of short time series are susceptible to providing unreliable inference as trends can be strongly influenced by points at either end of the time series. 
 
Duchenne et al.’s paper provides important insight on the conditions when strong inference on temporal trends of arthropod (and other species) abundances can be made, and when they should be treated with caution. They do not doubt that many insect and arachnid species are changing their abundances, and that patterns in these changes may vary spatially. What their results do say is that we should treat grand claims of population recovery or rapid declines apparently to extinction with caution when they are based on short time series, particularly of species that show significant boom and bust dynamics. In many ways, these results are not unexpected, but it is nice to see such careful and thoughtful analyses and interpretation. More data are required for most arthropod species before clear assessments of abundance trends can be made. Given our reliance on many arthropods for food, pollination, and numerous ecosystem services, and the ability of other species to spread devastating human diseases such as dengue and malaria, it is advisable that we slow our modification of their habitats while additional data are collected to allow us to better characterise the trajectory of arthropod populations to understand what the consequences of our actions on the natural world are likely to be.  
 
References

Duchenne F, Porcher E, Mihoub J-B, Loïs G, Fontaine C (2022) Controversy over the decline of arthropods: a matter of temporal baseline? bioRxiv, 2022.02.09.479422, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.02.09.479422

Have you ever wondered why some species breed seasonally while others do not? You might think it is all down to lattitude and the harshness of winters but it turns out it is quite a bit more complicated than that. A consequence of this is that climate change may result in the evolution of the degree of seasonal reproduction, with some species perhaps becoming less seasonal and others more so even in the same habitat. 

Burtschell et al. (2023) investigated how various factors influence seasonal breeding by building an individual-based model of a baboon population from which they calculated the degree of seasonality for the fittest reproductive strategy. They then altered key aspects of their model to examine how these changes impacted the degree of seasonality in the reproductive strategy. What they found is fascinating. 

The degree of seasonality in reproductive strategy is expected to increase with increased seasonality in the environment, decreased food availability, increased energy expenditure, and how predictable resource availability is. Interestingly, neither female cycle length nor extrinsic infant mortality influenced the degree of seasonality in reproduction.

What this means in reality for seasonal species is more challenging to understand. Some environments appear to be becoming more seasonal yet less predictable, and some species appear to be altering their daily energy budgets in response to changing climate in quite complex ways. As with pretty much everything in biology, Burtschell et al.'s work reveals much nuance and complexity, and that predicting how species might alter their reproductive timing is fraught with challenges.

The paper is very well written. With a simpler model it may have proven possible to achieve analytical solutions, but this is a very minor gripe. The reviewers were positive about the paper, and I have little doubt it will be well-cited. 

REFERENCES

Burtschell L, Dezeure J, Huchard E, Godelle B (2023) Evolutionary determinants of reproductive seasonality: a theoretical approach. bioRxiv, 2022.08.22.504761, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.08.22.504761

Species Distribution Models (SDMs) are one of the most commonly used tools to predict where species are, where they may be in the future, and, at times, what are the variables driving this prediction. As such, applying an SDM to a dataset is akin to making a bet: that the known occurrence data are informative, that the resolution of predictors is adequate vis-à-vis the scale at which their impact is expressed, and that the model will adequately capture the shape of the relationships between predictors and predicted occurrence.

In this contribution, Lambert & Virgili (2023) perform a comprehensive assessment of different sources of complications to this process, using replicated simulations of two synthetic species. Their experimental process is interesting, in that both the data generation and the data analysis stick very close to what would happen in "real life". The use of synthetic species is particularly relevant to the assessment of SDM robustness, as they enable the design of species for which the shape of the relationship is given: in short, we know what the model should capture, and can evaluate the model performance against a ground truth that lacks uncertainty.

Any simulation study is limited by the assumptions established by the investigators; when it comes to spatial data, the "shape" of the landscape, both in terms of auto-correlation and in where the predictors are available. Lambert & Virgili (2023) nicely circumvent these issues by simulating synthetic species against the empirical distribution of predictors; in other words, the species are synthetic, but the environment for which the prediction is made is real. This is an important step forward when compared to the use of e.g. neutral landscapes (With 1997), which can have statistical properties that are not representative of natural landscapes (see e.g. Halley et al., 2004).

A striking point in the study by Lambert & Virgili (2023) is that they reveal a deep, indeed deeper than expected, stochasticity in SDMs; whether this is true in all models remains an open question, but does not invalidate their recommendation to the community: the interpretation of outcomes is a delicate exercise, especially because measures that inform on the goodness of the model fit do not capture the predictive quality of the model outputs. This preprint is both a call to more caution, and a call to more curiosity about the complex behavior of SDMs, while also providing a sensible template to perform future analyses of the potential issues with predictive models.

References

Halley, J. M., et al. (2004) “Uses and Abuses of Fractal Methodology in Ecology: Fractal Methodology in Ecology.” Ecology Letters, vol. 7, no. 3, pp. 254–71. https://doi.org/10.1111/j.1461-0248.2004.00568.x.

Lambert, Charlotte, and Auriane Virgili (2023). Data Stochasticity and Model Parametrisation Impact the Performance of Species Distribution Models: Insights from a Simulation Study. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.01.17.524386

With, Kimberly A. (1997) “The Application of Neutral Landscape Models in Conservation Biology. Aplicacion de Modelos de Paisaje Neutros En La Biologia de La Conservacion.” Conservation Biology, vol. 11, no. 5, pp. 1069–80. https://doi.org/10.1046/j.1523-1739.1997.96210.x.