Estimating population parameters is critical for analysis and management of wildlife populations. Drawing inference at the population level requires a robust sampling scheme and information about the representativeness of the studied population (Williams et al. 2002). In their textbook, Williams et al. (see chapter 5, 2002) listed several sampling issues, including both temporal and spatial heterogeneity and especially imperfect detection. Several methods, either sampling-based or model-based can be used to circumvent these issues.

In their paper, Kissling et al. (2024) addressed the case of the Kittlitz’s murrelet (Brachyramphus brevirostris), a cryptic ice-associated seabird, combining spatial variation in its distribution, temporal variation in breeding propensity, imperfect detection and logistical challenges to access the breeding area. The Kittlitz’s murrelet is thus the perfect species to illustrate common issues and logistical difficulties to implement a standard sampling scheme. 

The authors proposed a modelling framework unifying several datasets from different surveys to extract information on each step of the detection process: the spatial match between the targeted population and the sampled population, the probability of presence in the sample area, the probability of availability given presence in the sample area and finally, the probability of detection given presence and availability. All these components were part of the framework to estimate abundance and trend for this species. 

They took advantage of a radiotracking survey during several years to inform spatial match and probability of presence. They performed a behavioural experiment to assess the probability of availability of murrelets given it was present in sampling area, and they used a conventional distance-sampling boat survey to estimate detection of individuals. This is worth noting that the most variable components were the probability of presence in the sample area, with a global mean of 0.50, and the probability of detection given presence and availability ranging from 0.49 to 0.77. The estimated trend for Kittlitz’s murrelet was negative and all the information gathered in this study will be useful for future conservation plan. 

Coupling a decomposition of the detection process with different data sources was the key to solve problems raised by such “difficult” species, and the paper of Kissling et al. (2024) is a good way to follow for other species, allowing to inform the detection components for the targeted species - and also for our global understanding of detection process, and to infer about the temporal trend of species of conservation concern. 

References

Williams, B. K., Nichols, J. D., and Conroy, M. J. (2002). Analysis and management of animal populations. Academic Press.

Michelle L. Kissling, Paul M. Lukacs, Kelly Nesvacil, Scott M. Gende, Grey W. Pendleton (2024) Using multiple datasets to account for misalignment between statistical and biological populations for abundance estimation. EcoEvoRxiv, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.32942/X2W03T

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

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

Habitat complexity is an important mediator of processes spanning levels of biological organization from organisms to ecosystems (Shumway et al. 2007, Soukup et al. 2022). This complexity, which can be biogenic (e.g., foundation species; Bracken et al. 2007, Ellison 2019) or abiotic (e.g., substrate rugosity; Kovalenko et al. 2012), shapes processes ranging from individual foraging behavior (Michel and Adams 2009) to species’ interactions to food-web structure and biogeochemical rates (Langellotto and Denno 2006, Larsen et al. 2021, Soukup et al. 2022). For example, in the presence of simulated aquatic vegetation, predatory diving beetle larvae shift from active foraging to sit-and-wait predation, reducing activity and prey encounter rates (Michel and Adams 2009).

In this contribution, Edeline et al. (2023) present a detailed perspective on the role of habitat complexity in shaping populations of a freshwater fish (medaka, Oryzias latipes, Adrianichthyidae), including survival, age-class diversity, population growth rate, and density-dependence in the stock-recruitment relationship associated with changes in carrying capacity. Importantly, changes in these population demographic attributes and rates were associated with the role of habitat complexity in mitigating cannibalism – consumption of juvenile O. latipes by conspecific adults. Whereas this is not unexpected – Langelotto and Denno (2006) showed that habitat complexity reduces cannibalism in wolf spiders – the careful work of Edeline et al. (2023) to link changes in habitat complexity to multiple population-level attributes provides a uniquely detailed description of the role of submerged aquatic vegetation in mediating population diversity (e.g., higher age-class diversity) and productivity (e.g., population growth rate).

In many ways, this work by Edeline et al. (2023) provides population-level parallels to perspectives on the role of habitat complexity in determining community-level diversity and productivity. Structurally complex habitats, such as those provided by foundation species (Bracken et al. 2007, Ellison 2019) and substrate heterogeneity (Fairchild et al. 2024), are associated with higher species diversity and abundance at the community level. Edeline et al. (2023) extend these perspectives to the population level, highlighting the importance of habitat complexity across levels of biological organization. Their work highlights within-population diversity and interactions, including cannibalism and competition, illustrating often-neglected aspects of food-web complexity (Polis and Strong 1996).

References

Matthew E. S. Bracken, Barry E. Bracken, Laura Rogers-Bennett (2007) Species diversity and foundation species: potential indicators of fisheries yields and marine ecosystem functioning. California Cooperative Oceanic Fisheries Investigations Reports 48: 82-91. https://calcofi.org/downloads/publications/calcofireports/v48/Vol_48_Bracken.pdf

Eric Edeline, Yoann Bennevault, David Rozen-Rechels (2023) Habitat structural complexity increases age-class coexistence and population growth rate through relaxed cannibalism in a freshwater fish. bioRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://www.biorxiv.org/content/10.1101/2023.07.18.549540v4

Aaron M. Ellison (2019) Foundation species, non-trophic interactions, and the value of being common. iScience 13: 254-68. https://doi.org/10.1016/j.isci.2019.02.020

Tom P. Fairchild, Bettina Walter, Joshua J. Mutter, John N. Griffin. (2024) Topographic heterogeneity triggers complementary cascades that enhance ecosystem multifunctionality. Ecology 105: e4434. https://doi.org/10.1002/ecy.4434

Katya E. Kovalenko, Sidinei M. Thomaz, Danielle M. Warfe (2012) Habitat complexity: approaches and future directions. Hydrobiologia 685: 1-17. https://doi.org/10.1007/s10750-011-0974-z

Gail A. Langellotto, Robert F. Denno. (2006) Refuge from cannibalism in complex-structured habitats: implications for the accumulation of invertebrate predators. Ecological Entomology 31: 575-81. https://doi.org/10.1111/j.1365-2311.2006.00816.x

Annegret Larsen, Joshua R. Larsen, Stuart N. Lane (2021) Dam builders and their works: beaver influences on the structure and Function of river corridor hydrology, geomorphology, biogeochemistry and ecosystems. Earth-Science Reviews 218: 103623. https://doi.org/10.1016/j.earscirev.2021.103623

Matt J. Michel, Melinda M. Adams. (2009) Differential effects of structural complexity on predator foraging behavior. Behavioral Ecology: 313-17. https://doi.org/10.1093/beheco/arp005

Gary A. Polis, Donald R. Strong (1996) Food web complexity and community dynamics. American Naturalist 147: 813-46. https://doi.org/10.1086/285880

Caroly A. Shumway, Hans A. Hofmann, Adam P. Dobberfuhl (2007) Quantifying habitat complexity in aquatic ecosystems. Freshwater Biology 52: 1065-76. https://doi.org/10.1111/j.1365-2427.2007.01754.x.

Pavel R. Soukup, Joacim Näslund, Johan Höjesjö, David S. Boukal (2022) From individuals to communities: habitat complexity affects all levels of organization in aquatic environments. Wiley Interdisciplinary Reviews: Water 9: e1575.  https://doi.org/10.1002/wat2.1575

Decline in body size over time have been widely observed in fish (but see Solokas et al. 2023), and the ecological consequences of this pattern can be severe (e.g., Audzijonyte et al. 2013, Oke et al. 2020). Therefore, studying the interrelationships between life history traits to understand the causal mechanisms of this pattern is timely and valuable. 

This phenomenon was the subject of a study by Josset et al. (2024), in which the authors analysed data from 39 years of trout trapping in the Bresle River in France. The authors focused mainly on the length of trout on their first return from the sea.   

The most important results of the study were the decrease in fish length-at-first return and the change in the age structure of first-returning trout towards younger (and earlier) returning fish. It seems then that the smaller size of trout is caused by a shorter time spent in the sea rather than a change in a growth pattern, as length-at-age remained relatively constant, at least for those returning earlier. Fish returning after two years spent in the sea had a relatively smaller length-at-age. The authors suggest this may be due to local changes in conditions during fish's stay in the sea, although there is limited environmental data to confirm the causal effect. Another question is why there are fewer of these older fish. The authors point to possible increased mortality from disease and/or overfishing.

These results may suggest that the situation may be getting worse, as another study finding was that “the more growth seasons an individual spent at sea, the greater was its length-at-first return.” The consequences may be the loss of the oldest and largest individuals, whose disproportionately high reproductive contribution to the population is only now understood (Barneche et al. 2018, Marshall and White 2019). 

References

Audzijonyte, A. et al. 2013. Ecological consequences of body size decline in harvested fish species: positive feedback loops in trophic interactions amplify human impact. Biol Lett 9, 20121103. https://doi.org/10.1098/rsbl.2012.1103

Barneche, D. R. et al. 2018. Fish reproductive-energy output increases disproportionately with body size. Science Vol 360, 642-645. https://doi.org/10.1126/science.aao6868

Josset, Q. et al. 2024. Changes in length-at-first return of a sea trout (Salmo trutta) population in northern France. biorXiv, 2023.11.21.568009, ver 4, Peer-reviewed and recommended by PCI Ecology. https://doi.org/10.1101/2023.11.21.568009

Marshall, D. J. and White, C. R. 2019. Have we outgrown the existing models of growth? Trends in Ecology & Evolution, 34, 102-111. https://doi.org/10.1016/j.tree.2018.10.005

Oke, K. B. et al. 2020. Recent declines in salmon body size impact ecosystems and fisheries. Nature Communications, 11, 4155. https://doi.org/10.1038/s41467-020-17726-z

Solokas, M. A. et al. 2023. Shrinking body size and climate warming: many freshwater salmonids do not follow the rule. Global Change Biology, 29, 2478-2492. https://doi.org/10.1111/gcb.16626