Sexual coercion can be defined as the use by a male of force, or threat of force, which increases the chances that a female will mate with him at a time when she is likely to be fertile, and/or decrease the chances that she will mate with other males, at some cost to the female (Smuts & Smuts 1993). It has been evidenced in a wide range of species and may play an important role in the evolution of sexual conflict and social systems. However, identifying sexual coercion in natural systems can be particularly challenging. Notably, while male behaviour may have immediate consequences on mating success (“harassment”), the mating benefits may be delayed in time (“intimidation”), and in such cases, evidencing coercion requires detailed temporal data at the individual level. Moreover, in some species male aggressive behaviours may be subtle or rare and hence hardly observed, yet still have important effects on female mating probability and fitness. Therefore, investigating the occurrence and consequences of sexual coercion in such species is particularly relevant but studying it in a statistically robust way is likely to require a considerable amount of time spent observing individuals.

In this paper, Smit et al. (2022) test three clear predictions of the sexual coercion hypothesis in a natural population of Mandrills, where severe male aggression towards females is rare: (1) male aggression is more likely on sexually receptive females than on females in other reproductive states, (2) receptive females are more likely to be injured and (3) male aggression directed towards females is positively related to subsequent probability of copulation between those dyads. They also tested an alternative hypothesis, the “aggressive male phenotype” under which the correlation between male aggression towards females and subsequent mating could be statistically explained by male overall aggressivity. In agreement with the three predictions of the sexual coercion hypothesis, (1) male aggression was on average 5 times more likely, and (2) injuries twice as likely, to be observed on sexually receptive females than on females in other reproductive states and (3) copulation between males and sexually receptive females was twice more likely to be observed when aggression by this male was observed on the female before sexual receptivity. There was no support for the aggressive male hypothesis.

The reviewers and I were highly positive about this study, notably regarding the way it is written and how the predictions are carefully and clearly stated, tested, interpreted, and discussed.

This study is a good illustration of a case where some behaviours may not be common or obvious yet have strong effects and likely important consequences and thus be clearly worth studying. More generally, it shows once more the importance of detailed long-term studies at the individual level for our understanding of the ecology and evolution of wild populations.

It is also a good illustration of the challenges faced, when comparing the likelihood of contrasting hypotheses means we need to alter sample sizes and/or the likelihood to observe at all some behaviours. For example, observing copulation within minutes after aggression (and therefore, showing statistical support for “harassment”) is inevitably less likely than observing copulations on the longer-term (and therefore showing statistical support for “intimidation”, when of course effort is put into recording such behavioural data on the long-term). Such challenges might partly explain some apparently intriguing results. For example, why are swollen females more aggressed by males if only aggression before the swollen period seems associated with more chances of mating? Here, the authors systematically provide effect sizes (and confidence intervals) and often describe the effects in an intuitive biological way (e.g., “Swollen females were, on average, about five times more likely to become injured”). This clearly helps the reader to not merely compare statistical significances but also the biological strengths of the estimated effects and the uncertainty around them. They also clearly acknowledge limits due to sample size when testing the harassment hypothesis, yet they provide precious information on the probability of observing mating (a rare behaviour) directly after aggression (already a rare behaviour!), that is, 3 times out of 38 aggressions observed between a male and a swollen female. Once again, this highlights how important it is to be able to pursue the enormous effort put so far into closely and continuously monitoring this wild population.

Finally, this study raises exciting new questions, notably regarding to what extent females exhibit “counter-strategies” in response to sexual coercion, notably whether there is still scope for female mate choice under such conditions, and what are the fitness consequences of these dynamic conflicting sexual interactions. No doubt these questions will sooner than later be addressed by the authors, and I am looking forward to reading their upcoming work.

References

Smit N, Baniel A, Roura-Torres B, Amblard-Rambert P, Charpentier MJE, Huchard E (2022) Sexual coercion in a natural mandrill population. bioRxiv, 2022.02.07.479393, ver. 5 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.02.07.479393

Smuts BB, Smuts R w. (1993) Male Aggression and Sexual Coercion of Females in Nonhuman Primates and Other Mammals: Evidence and Theoretical Implications. In: Advances in the Study of Behavior (eds Slater PJB, Rosenblatt JS, Snowdon CT, Milinski M), pp. 1–63. Academic Press. https://doi.org/10.1016/S0065-3454(08)60404-0

Individuals can respond to environmental changes that they undergo directly (within-generation plasticity) but also through transgenerational plasticity, providing lasting effects that are transmitted to the next generations (Donelson et al. 2012; Munday et al. 2013; Kuijper & Hoyle 2015; Auge et al. 2017, Tariel et al. 2020). These parental effects can affect offspring via various mechanisms, notably via maternal transmission of hormones to the eggs or growing embryos (Mousseau & Fox 1998). While the effects of environmental quality may simply carry-over to the next generation (e.g., females in stressful environments give birth to offspring in poorer condition), parental effects may also be a mechanism that adjusts offspring phenotype in response to environmental variation and predictability, and thereby match offspring's phenotype to future environmental conditions (Gluckman et al. 2005; Marshall & Uller 2007; Dey et al. 2016; Yin et al. 2019), for example by preparing their offspring to an expected stressful environment.

When females experience stress during gestation or egg formation, elevations in glucocorticoids (GC) are expected to affect offspring phenotype in many ways, from the offspring's own GC levels, to their growth and survival (Sheriff et al. 2017). This is a well established idea, but how strong is the evidence for this? A meta-analysis on birds found no clear effect of corticosterone manipulation on offspring traits (38 studies on 9 bird species for corticosterone manipulation; Podmokła et al. 2018). Another meta-analysis including 14 vertebrate species found no clear effect of prenatal stress on offspring GC (Thayer et al. 2018). Finally, a meta-analysis on wild vertebrates (23 species) found no clear effect of GC-mediated maternal effects on offspring traits (MacLeod et al. 2021). As often when facing such inconclusive results, context dependence has been suggested as one potential reason for such inconsistencies, for exemple sex specific effects (Groothuis et al. 2019, 2020). However, sex specific measures on offspring are scarce (Podmokła et al. 2018). Moreover, the literature available is still limited to a few, mostly “model” species.

With their study, Amin et al. (2024) show the way to improve our understanding on GC transmission from mother to offspring and its effects in several aspects. First they used innovative non-invasive methods (which could broaden the range of species available to study) by quantifying cortisol metabolites from faecal samples collected from pregnant females, as proxy for maternal GC level, and relating it to GC levels from hairs of their neonate offspring. Second they used a free ranging large mammal (taxa from which literature is missing): the fallow deer (Dama dama). Third, they provide sex specific measures of GC levels. And finally but importantly, they are exemplary in their transparency regarding 1) the exploratory nature of their study, 2) their statistical thinking and procedure, and 3) the study limitations (e.g., low sample size and high within individual variation of measurements). I hope this study will motivate more research (on the fallow deer, and on other species) to broaden and strengthen our understanding of sex specific effects of maternal stress and CG levels on offspring phenotype and fitness.

References

Amin, B., Fishman, R., Quinn, M., Matas, D., Palme, R., Koren, L., & Ciuti, S. (2024). Sex differences in the relationship between maternal and foetal glucocorticoids in a free-ranging large mammal. bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.05.04.538920 

Auge, G.A., Leverett, L.D., Edwards, B.R. & Donohue, K. (2017). Adjusting phenotypes via within-and across-generational plasticity. New Phytologist, 216, 343–349. https://doi.org/10.1111/nph.14495

Dey, S., Proulx, S.R. & Teotonio, H. (2016). Adaptation to temporally fluctuating environments by the evolution of maternal effects. PLoS biology, 14, e1002388. https://doi.org/10.1371/journal.pbio.1002388

Donelson, J.M., Munday, P.L., McCormick, M.I. & Pitcher, C.R. (2012). Rapid transgenerational acclimation of a tropical reef fish to climate change. Nature Climate Change, 2, 30. https://doi.org/10.1038/nclimate1323

Gluckman, P.D., Hanson, M.A. & Spencer, H.G. (2005). Predictive adaptive responses and human evolution. Trends in ecology & evolution, 20, 527–533. https://doi.org/10.1016/j.tree.2005.08.001

Groothuis, Ton GG, Bin-Yan Hsu, Neeraj Kumar, and Barbara Tschirren. "Revisiting mechanisms and functions of prenatal hormone-mediated maternal effects using avian species as a model." Philosophical Transactions of the Royal Society B 374, no. 1770 (2019): 20180115. https://doi.org/10.1098/rstb.2018.0115

Groothuis, Ton GG, Neeraj Kumar, and Bin-Yan Hsu. "Explaining discrepancies in the study of maternal effects: the role of context and embryo." Current Opinion in Behavioral Sciences 36 (2020): 185-192. https://doi.org/10.1016/j.cobeha.2020.10.006 

Kuijper, B. & Hoyle, R.B. (2015). When to rely on maternal effects and when on phenotypic plasticity? Evolution, 69, 950–968. https://doi.org/10.1111/evo.12635   

MacLeod, Kirsty J., Geoffrey M. While, and Tobias Uller. "Viviparous mothers impose stronger glucocorticoid‐mediated maternal stress effects on their offspring than oviparous mothers." Ecology and Evolution 11, no. 23 (2021): 17238-17259.

Marshall, D.J. & Uller, T. (2007). When is a maternal effect adaptive? Oikos, 116, 1957–1963. https://doi.org/10.1111/j.2007.0030-1299.16203.x       

Mousseau, T.A. & Fox, C.W. (1998). Maternal effects as adaptations. Oxford University Press.

Munday, P.L., Warner, R.R., Monro, K., Pandolfi, J.M. & Marshall, D.J. (2013). Predicting evolutionary responses to climate change in the sea. Ecology Letters, 16, 1488–1500. https://doi.org/10.1111/ele.12185

Podmokła, Edyta, Szymon M. Drobniak, and Joanna Rutkowska. "Chicken or egg? Outcomes of experimental manipulations of maternally transmitted hormones depend on administration method–a meta‐analysis." Biological Reviews 93, no. 3 (2018): 1499-1517. https://doi.org/10.1111/brv.12406 

Sheriff, M. J., Bell, A., Boonstra, R., Dantzer, B., Lavergne, S. G., McGhee, K. E., MacLeod, K. J., Winandy, L., Zimmer, C., & Love, O. P. (2017). Integrating ecological and evolutionary context in the study of maternal stress. Integrative and Comparative Biology, 57(3), 437–449. https://doi.org/10.1093/icb/icx105

Tariel, Juliette, Sandrine Plénet, and Émilien Luquet. "Transgenerational plasticity in the context of predator-prey interactions." Frontiers in Ecology and Evolution 8 (2020): 548660. https://doi.org/10.3389/fevo.2020.548660 

Thayer, Zaneta M., Meredith A. Wilson, Andrew W. Kim, and Adrian V. Jaeggi. "Impact of prenatal stress on offspring glucocorticoid levels: A phylogenetic meta-analysis across 14 vertebrate species." Scientific Reports 8, no. 1 (2018): 4942. https://doi.org/10.1038/s41598-018-23169-w 

Yin, J., Zhou, M., Lin, Z., Li, Q.Q. & Zhang, Y.-Y. (2019). Transgenerational effects benefit offspring across diverse environments: a meta-analysis in plants and animals. Ecology letters, 22, 1976–1986. https://doi.org/10.1111/ele.13373

Many ecological drivers can impact ecosystem functionality and multifunctionality, with the latter describing the joint impact of different functions on ecosystem performance and services. It is now generally accepted that taxonomically richer ecosystems are better able to sustain high aggregate functionality measures, like energy transfer, productivity or carbon storage (Buzhdygan 2020, Naeem et al. 2009), and different ecosystem services (Marselle et al. 2021) than those that are less rich. Antonini et al. (2022) analysed an impressive dataset on animal and plant richness of tropical riparian forests and abundances, together with data on key soil parameters. Their work highlights the importance of biodiversity on functioning, while accounting for a manifold of potentially covarying drivers. Although the key result might not come as a surprise, it is a useful contribution to the diversity - ecosystem functioning topic, because it is underpinned with data from tropical habitats. To date, most analyses have focused on temperate habitats, using data often obtained from controlled experiments. 

The paper also highlights that diversity–functioning relationships are complicated. Drivers of functionality vary from site to site and each measure of functioning, including parameters as demonstrated here, can be influenced by very different sets of predictors, often associated with taxonomic and trait diversity. Single correlative comparisons of certain aspects of diversity and functionality might therefore return very different results. Antonini et al. (2022) show that, in general, using 22 predictors of functional diversity, varying predictor subsets were positively associated with soil functioning. Correlational analyses alone cannot resolve the question of causal link. Future studies should therefore focus on inferring precise mechanisms behind the observed relationships, and the environmental constraints on predictor subset composition and strength.

References

Antonini Y, Beirão MV, Costa FV, Azevedo CS, Wojakowski MM, Kozovits AR, Pires MRS, Sousa HC de, Messias MCTB, Fujaco MA, Leite MGP, Vidigal JP, Monteiro GF, Dirzo R (2022) Riparian forest restoration as sources of biodiversity and ecosystem functions in anthropogenic landscapes. bioRxiv, 2021.09.08.459375, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2021.09.08.459375

Buzhdygan OY, Meyer ST, Weisser WW, Eisenhauer N, Ebeling A, Borrett SR, Buchmann N, Cortois R, De Deyn GB, de Kroon H, Gleixner G, Hertzog LR, Hines J, Lange M, Mommer L, Ravenek J, Scherber C, Scherer-Lorenzen M, Scheu S, Schmid B, Steinauer K, Strecker T, Tietjen B, Vogel A, Weigelt A, Petermann JS (2020) Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands. Nature Ecology & Evolution, 4, 393–405. https://doi.org/10.1038/s41559-020-1123-8

Marselle MR, Hartig T, Cox DTC, de Bell S, Knapp S, Lindley S, Triguero-Mas M, Böhning-Gaese K, Braubach M, Cook PA, de Vries S, Heintz-Buschart A, Hofmann M, Irvine KN, Kabisch N, Kolek F, Kraemer R, Markevych I, Martens D, Müller R, Nieuwenhuijsen M, Potts JM, Stadler J, Walton S, Warber SL, Bonn A (2021) Pathways linking biodiversity to human health: A conceptual framework. Environment International, 150, 106420. https://doi.org/10.1016/j.envint.2021.106420

Naeem S, Bunker DE, Hector A, Loreau M, Perrings C (Eds.) (2009) Biodiversity, Ecosystem Functioning, and Human Wellbeing: An Ecological and Economic Perspective. Oxford University Press, Oxford. https://doi.org/10.1093/acprof:oso/9780199547951.001.0001

Environmental Risk Assessment (ERA) is a strategic conceptual framework to characterize the nature and magnitude of risks, to humans and biodiversity, of the release of chemical contaminants in the environment. Several measures have been suggested to enhance the science and application of ERA, including the identification and acknowledgment of uncertainties that potentially influence the outcome of risk assessments, and the appropriate consideration of temporal scale and its linkage to assessment endpoints [1].
Baudrot & Charles [2] proposed to approach these questions by coupling toxicokinetics-toxicodynamics models, which describe the time-course of processes leading to the adverse effects of a toxicant, with Bayesian inference. TKTD models separate processes influencing an organismal internal exposure (´toxicokinetics´, i.e., the uptake, bioaccumulation, distribution, biotransformation and elimination of a toxicant) from processes leading to adverse effects and ultimately its death (´toxicodynamics´) [3]. Although species and substance specific, the mechanistic nature of TKTD models facilitates the comparison of different toxicants, species, life stages, environmental conditions and endpoints [4].
Baudrot & Charles [2] investigated the use of a Bayesian framework to assess the uncertainties surrounding the calibration of General Unified Threshold Models of Survival (a category of TKTD) with data from standard toxicity tests, and their propagation to predictions of regulatory toxicity endpoints such as LC(x,t) [the lethal concentration affecting any x% of the population at any given exposure duration of time t] and MF(x,t) [an exposure multiplication factor leading to any x% effect reduction due to the contaminant at any time t].
Once calibrated with empirical data, GUTS models were used to explore individual survival over time, and under untested exposure conditions. Lethal concentrations displayed a strong curvilinear decline with time of exposure. For a given total amount of contaminant, pulses separated by short time intervals yielded higher mortality than pulses separated by long time intervals, as did few pulses of high amplitude when compared to multiple pulses of low amplitude. The response to a pulsed contaminant exposure was strongly influenced by contaminant depuration times. These findings highlight one important contribution of TKTD modelling in ecotoxicology: they represent just a few of the hundreds of exposure scenarios that could be mathematically explored, and that would be unfeasible or even unethical to conduct experimentally.
GUTS models were also used for interpolations or extrapolations of assessment endpoints, and their marginal distributions. A case in point is the incipient lethal concentration. The responses of model organisms to contaminants in standard toxicity tests are typically assessed at fixed times of exposure (e.g. 24h or 48h in the Daphnia magna acute toxicity test). However, because lethal concentrations are strongly time-dependent, it has been suggested that a more meaningful endpoint would be the incipient (i.e. asymptotic) lethal concentration when time of exposure increases to infinity. The authors present a mathematical solution for calculating the marginal distribution of such incipient lethal concentration, thereby providing both more relevant information and a way of comparing experiments, compounds or species tested for different periods of time.
Uncertainties were found to change drastically with time of exposure, being maximal at extreme values of x for both LC(x,t) and MF(x,t). In practice this means that assessment endpoints estimated when the effects of the contaminant are weak (such as LC10, the contaminant concentration resulting in the mortality of 10% of the experimental population), a commonly used assessment value in ERA, are prone to be highly variable.
The authors end with recommendations for improved experimental design, including (i) using assessment endpoints at intermediate values of x (e.g., LC50 instead of LC10) (ii) prolonging exposure and recording mortality over the course of the experiment (iii) experimenting one or few peaks of high amplitude close to each other when assessing pulsed exposure. Whereas these recommendations are not that different from current practices, they are based on a more coherent mechanistic grounding.
Overall, this and other contributions from Charles, Baudrot and their research group contribute to turn TKTD models into a real tool for Environmental Risk Assessment. Further enhancement of ERA´s science and application could be achieved by extending the use of TKTD models to sublethal rather than lethal effects, and to chronic rather than acute exposure, as these are more controversial issues in decision-making regarding contaminated sites.

References

[1] Dale, V. H., Biddinger, G. R., Newman, M. C., Oris, J. T., Suter, G. W., Thompson, T., ... & Chapman, P. M. (2008). Enhancing the ecological risk assessment process. Integrated environmental assessment and management, 4(3), 306-313. doi: 10.1897/IEAM_2007-066.1
[2] Baudrot, V., & Charles, S. (2018). Recommendations to address uncertainties in environmental risk assessment using toxicokinetics-toxicodynamics models. bioRxiv, 356469, ver. 3 peer-reviewed and recommended by PCI Ecol. doi: 10.1101/356469
[3] EFSA Panel on Plant Protection Products and their Residues (PPR), Ockleford, C., Adriaanse, P., Berny, P., Brock, T., Duquesne, S., ... & Kuhl, T. (2018). Scientific Opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms. EFSA Journal, 16(8), e05377. doi: 10.2903/j.efsa.2018.5377
[4] Jager, T., Albert, C., Preuss, T. G., & Ashauer, R. (2011). General unified threshold model of survival-a toxicokinetic-toxicodynamic framework for ecotoxicology. Environmental science & technology, 45(7), 2529-2540. doi: 10.1021/es103092a

In the field of ecology, there is a growing interest in machine (including deep) learning for processing and automatizing repetitive analyses on large amounts of images collected from camera traps, drones and smartphones, among others. These analyses include species or individual recognition and classification, counting or tracking individuals, detecting and classifying behavior. By saving countless times of manual work and tapping into massive amounts of data that keep accumulating with technological advances, machine learning is becoming an essential tool for ecologists. We refer to recent papers for more details on machine learning for ecology and evolution (Besson et al. 2022, Borowiec et al. 2022, Christin et al. 2019, Goodwin et al. 2022, Lamba et al. 2019, Nazir & Kaleem 2021, Perry et al. 2022, Picher & Hartig 2023, Tuia et al. 2022, Wäldchen & Mäder 2018).

In their paper, Nakagawa et al. (2023) conducted a systematic review of the literature on machine learning for wildlife imagery. Interestingly, the authors used a method unfamiliar to ecologists but well-established in medicine called rapid review, which has the advantage of being quickly completed compared to a fully comprehensive systematic review while being representative (Lagisz et al., 2022). Through a rigorous examination of more than 200 articles, the authors identified trends and gaps, and provided suggestions for future work. Listing all their findings would be counterproductive (you’d better read the paper), and I will focus on a few results that I have found striking, fully assuming a biased reading of the paper. First, Nakagawa et al. (2023) found that most articles used neural networks to analyze images, in general through collaboration with computer scientists. A challenge here is probably to think of teaching computer vision to the generations of ecologists to come (Cole et al. 2023). Second, the images were dominantly collected from camera traps, with an increase in the use of aerial images from drones/aircrafts that raise specific challenges. Third, the species concerned were mostly mammals and birds, suggesting that future applications should aim to mitigate this taxonomic bias, by including, e.g., invertebrate species. Fourth, most papers were written by authors affiliated with three countries (Australia, China, and the USA) while India and African countries provided lots of images, likely an example of scientific colonialism which should be tackled by e.g., capacity building and the involvement of local collaborators. Last, few studies shared their code and data, which obviously impedes reproducibility. Hopefully, with the journals’ policy of mandatory sharing of codes and data, this trend will be reversed. 

REFERENCES

Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF (2022) Towards the fully automated monitoring of ecological communities. Ecology Letters, 25, 2753–2775. https://doi.org/10.1111/ele.14123

Borowiec ML, Dikow RB, Frandsen PB, McKeeken A, Valentini G, White AE (2022) Deep learning as a tool for ecology and evolution. Methods in Ecology and Evolution, 13, 1640–1660. https://doi.org/10.1111/2041-210X.13901

Christin S, Hervet É, Lecomte N (2019) Applications for deep learning in ecology. Methods in Ecology and Evolution, 10, 1632–1644. https://doi.org/10.1111/2041-210X.13256

Cole E, Stathatos S, Lütjens B, Sharma T, Kay J, Parham J, Kellenberger B, Beery S (2023) Teaching Computer Vision for Ecology. https://doi.org/10.48550/arXiv.2301.02211

Goodwin M, Halvorsen KT, Jiao L, Knausgård KM, Martin AH, Moyano M, Oomen RA, Rasmussen JH, Sørdalen TK, Thorbjørnsen SH (2022) Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook†. ICES Journal of Marine Science, 79, 319–336. https://doi.org/10.1093/icesjms/fsab255

Lagisz M, Vasilakopoulou K, Bridge C, Santamouris M, Nakagawa S (2022) Rapid systematic reviews for synthesizing research on built environment. Environmental Development, 43, 100730. https://doi.org/10.1016/j.envdev.2022.100730

Lamba A, Cassey P, Segaran RR, Koh LP (2019) Deep learning for environmental conservation. Current Biology, 29, R977–R982. https://doi.org/10.1016/j.cub.2019.08.016

Nakagawa S, Lagisz M, Francis R, Tam J, Li X, Elphinstone A, Jordan N, O’Brien J, Pitcher B, Sluys MV, Sowmya A, Kingsford R (2023) Rapid literature mapping on the recent use of machine learning for wildlife imagery. EcoEvoRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.32942/X2H59D

Nazir S, Kaleem M (2021) Advances in image acquisition and processing technologies transforming animal ecological studies. Ecological Informatics, 61, 101212. https://doi.org/10.1016/j.ecoinf.2021.101212

Perry GLW, Seidl R, Bellvé AM, Rammer W (2022) An Outlook for Deep Learning in Ecosystem Science. Ecosystems, 25, 1700–1718. https://doi.org/10.1007/s10021-022-00789-y

Pichler M, Hartig F Machine learning and deep learning—A review for ecologists. Methods in Ecology and Evolution, n/a. https://doi.org/10.1111/2041-210X.14061

Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, van Langevelde F, Burghardt T, Kays R, Klinck H, Wikelski M, Couzin ID, van Horn G, Crofoot MC, Stewart CV, Berger-Wolf T (2022) Perspectives in machine learning for wildlife conservation. Nature Communications, 13, 792. https://doi.org/10.1038/s41467-022-27980-y

Wäldchen J, Mäder P (2018) Machine learning for image-based species identification. Methods in Ecology and Evolution, 9, 2216–2225. https://doi.org/10.1111/2041-210X.13075