Multi‐event capture‐recapture analysis in Alpine chamois reveals contrasting responses to interspecific competition, within and between populations

Gamelon, Marlène; Filli, Flurin; Sæther, Bernt‐Erik; Herfindal, Ivar

doi:10.1111/1365-2656.13299

Cited by 10 publications

(8 citation statements)

References 59 publications

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“…To assess the costs of reproduction, a biologist will be interested in the probability of breeding in year

t

, given breeding

()(, ψ_{B, B} = 1 ‐ ψ_{B, N B})

or not

()(, ψ_{N B, B})

in year

t ‐ 1

, as well as assessing any differences in survival probability between breeders

()(, ϕ_{B})

and non‐breeders

()(, ϕ_{NB})

. By simply re‐expressing the

boldδ

boldΓ

and

P (x_{t})

components in terms of the specific state and observation processes of interest, such models can be used to infer the dynamics of conjunctivitis in house finches (Conn and Cooch, 2009), senescence in deer (Choquet et al ., 2011), reproduction in Florida manatees (Kendall et al ., 2012), interspecific competition between ungulates (Gamelon et al ., 2020) and life‐history trade‐offs in elephant seals (Lloyd et al ., 2020). Similar HMMs can also be used to investigate relationships between life‐history traits and demographic parameters that are important in determining the fitness of phenotypes or genotypes (Stoelting et al ., 2015).…”

Section: Ecological Applications Of Hidden Markov Modelsmentioning

confidence: 99%

Uncovering ecological state dynamics with hidden Markov models

et al. 2020

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Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or ‘hidden’. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.

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“…To assess the costs of reproduction, a biologist will be interested in the probability of breeding in year

t

, given breeding

()(, ψ_{B, B} = 1 ‐ ψ_{B, N B})

or not

()(, ψ_{N B, B})

in year

t ‐ 1

, as well as assessing any differences in survival probability between breeders

()(, ϕ_{B})

and non‐breeders

()(, ϕ_{NB})

. By simply re‐expressing the

boldδ

boldΓ

and

P (x_{t})

Section: Ecological Applications Of Hidden Markov Modelsmentioning

confidence: 99%

Uncovering ecological state dynamics with hidden Markov models

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Spatial variation in habitat or climate conditions can affect how weather impacts, for instance, foraging conditions (Mysterud et al 2001(Mysterud et al , 2002, which influences survival and recruitment (Hansen et al 2011). In addition, even nearby populations often experience different levels of interspecific inter actions which also af fect population dyna mics (Gamelon et al 2020). Theoretical results ob tained from other studies in the SUSTAIN project (Jarillo et al 2018 strongly indicate that climate change will accentuate the necessity for implementing the effects of harvesting on spatial synchrony in the dynamics of interacting species.…”

Section: Discussion and Future Prospectsmentioning

confidence: 98%

“…This illustrates one of the major challenges for research and management of species in spatially structured environments: the need for spatially explicit time series from harvesting practices or other management actions, environmental conditions and populations. Moreover, data from species other than those of focal management interest are also important (Anderwald et al 2015, Henden et al 2021 in this Special), because species do not live in isolation but co-exist and interact with other species (Gamelon et al 2020.…”

Section: Discussion and Future Prospectsmentioning

confidence: 99%

“…Such deviations from the Moran theorem can have great impact on the observed population synchrony . In addition, heterogeneity in demographic rates, such as survival and recruitment, among populations of a species is affected by factors such as interspecific interactions, climate, or habitat and resource availability (Ranta et al 1999, Paradis et al 2000b, Lande et al 2014, Gamelon et al 2020. This can lead to the same environmental condition having a contrasting effect on the growth of different populations of the same species.…”

Section: Introductionmentioning

confidence: 99%

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Environmental effects on spatial population dynamics and synchrony: lessons from northern ecosystems

et al. 2022

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Environmental variation in time and space generates complex patterns in the spatial structure of temporally covarying populations. Accounting for spatial population structure is important for sustainable management and harvest, but there is a need for a better understanding of the many mechanisms affecting the spatial structure of populations. In the large-scale research project SUSTAIN, detailed long-term data from several taxa within the boreal and Arctic ecosystems were used to address key research questions about spatial population structure. Here, we synthesise the main findings from these studies. Because nearby populations experience similar environmental variation, populations close to each other show more correlated dynamics than those at greater distances. However, several mechanisms can affect the extent of such spatial population synchrony, and we point to some similarities across systems that can explain the observed discrepancy between the spatial structure of the environment and that of population dynamics. We discuss the consequences of these findings for the practical management of species in a changing environment and the need for further research on how populations and ecosystems are affected by the spatial structure of the environment.

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“…Natural systems consist of many species that interact with each other in complex ways. Thus, the population dynamics and responses to harvesting and environmental change of one species depend on the dynamics and responses of other species within the ecosystem (Anderwald et al 2015, Gamelon et al 2020. This interdependence calls for a multi-species or ecosystem approach to manage harvesting (Levin et al 2009).…”

Section: Interactions Between Speciesmentioning

confidence: 99%

Sustainable management of populations impacted by harvesting and climate change

et al. 2022

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The sustainable use of natural resources is critical for addressing the global challenges of today. Strategies for sustainable harvesting need to consider not only harvested species, but also other non-harvested species interacting with them in the same ecosystem. In addition, environmental variation needs to be considered, with climate change currently being one of the main sources of this variation. Understanding the consequences of complex interactions between different drivers and processes affecting dynamics of species and ecosystems across spatial scales requires large-scale integrative research projects. The Norwegian research initiative “Sustainable management of renewable resources in a changing environment: an integrated approach across ecosystems” (SUSTAIN) was launched to fill knowledge gaps related to the sustainable management of populations and ecosystems experiencing climate change. SUSTAIN investigated terrestrial, marine and freshwater ecosystems in boreal and Arctic regions, using both theoretical developments and empirical analyses of long-term data. This Climate Research Special contains both synthesis articles and original research exemplifying some of the approaches used in SUSTAIN. In this introduction we highlight 4 key topics addressed by SUSTAIN: (i) population structure, (ii) interactions between species, (iii) spatial processes, and (iv) adaptive management. These topics are fundamental to the understanding of harvested species from an ecosystem perspective, and to ecosystem-based management approaches, which we are striving to work towards.

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Multi‐event capture‐recapture analysis in Alpine chamois reveals contrasting responses to interspecific competition, within and between populations

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 10 publications

References 59 publications

Uncovering ecological state dynamics with hidden Markov models

Uncovering ecological state dynamics with hidden Markov models

Environmental effects on spatial population dynamics and synchrony: lessons from northern ecosystems

Sustainable management of populations impacted by harvesting and climate change

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