Iterative model predictions for wildlife populations impacted by rapid climate change

Marolla, Filippo; Henden, John‐André; Fuglei, Eva; Pedersen, Åshild Ønvik; Itkin, Mikhail; Ims, Rolf A.

doi:10.1111/gcb.15518

Cited by 16 publications

(21 citation statements)

References 77 publications

(122 reference statements)

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“…Concerning near-term forecasting, the predictive performance of all models generally increased (i.e. the prediction error diminished) with increasing length of the time series used to parameterize the models, as expected (Henden et al 2020, Marolla et al 2021. In both case studies, however, the more complex models did not perform markedly better than the simpler models.…”

Section: Short-term Predictionsmentioning

confidence: 61%

“…Many of these processes concern scavengingdriven predation (Mellard et al 2021), where carrion links target species indirectly through shared predators. We observe that target species in our food webs can have a negative, or apparent competition, relationship to carrion availability (Marolla et al 2019(Marolla et al , 2021 or a positive, or apparent mutualism, relationship to carrion availability (Henden et al 2020), mediated by shared predators. A candidate explanation for the contrasting effect of this subsidy is the difference in timing of reproduction between target ground-nesting bird species.…”

Section: Including Direct and Indirect Interactionsmentioning

confidence: 82%

“…For the Svalbard case, increased temperatures during winter had the strongest positive effect on ptarmigan population growth, likely because it reduced the energy needed for thermoregulation during winter. Hence, our mod- eling approach has widened the scope for potential mitigating actions, by highlighting novel drivers of ptarmigan population dynamics, including manageable drivers such as management-enhanced forest regrowth after moth outbreaks (Henden et al 2020, Marolla et al 2021. Interestingly, our results indicate that protection against hunting or reduced hunting quotas would have limited effects on population state because current harvest was not among the key drivers of ptarmigan population dynamics of either Sval-bard rock ptarmigan or willow ptarmigan (see also Sandercock et al 2011).…”

Section: Short-term Predictionsmentioning

confidence: 91%

“…2a,b; figures after Henden et al 2017) and combine them with time series of those biotic variables along with relevant abiotic variables (see Fig. 2c,d; figures after Marolla et al 2019Marolla et al , 2021 to form hypotheses on likely strong interactions in the food web and tightly linked abiotic/biotic components. We convert these hypothesized relationships to conceptual models (see Fig.…”

Section: Including Direct and Indirect Interactionsmentioning

confidence: 99%

“…We performed a near-term forecasting approach, based on explanatory statistical food web models, to case studies of harvested populations of low-Arctic willow ptarmigan Lagopus lagopus (Henden et al 2020) and Svalbard rock ptarmigan L. muta hyper-borea (Marolla et al 2021). A key goal was to inform stakeholders about the near-future state of the population, information to be used as a basis for adaptive management of ptarmigan.…”

Section: Short-term Predictionsmentioning

confidence: 99%

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Food web approach for managing Arctic wildlife populations in an era of rapid environmental change

et al. 2022

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Scientists and wildlife managers implementing adaptive monitoring and management schemes, are tasked with providing predictions of population responses to harvest and environmental changes. Such predictions are useful not only to forecast direct effects of climate, productivity, land use, or habitat degradation, but also changes in the food web, such as expanding/�increasing species that are predators, prey, and competitors of populations of concern. Explicit consideration of food webs and their dynamics in more complex models could provide better predictions of future changes, and allow us to better assess the influence of management actions. Here, we present our perspective on what we have learned from conducting a number of case studies using such a food web approach with a focus on climate and harvest impacts and their implications for management. We found empirical support for many of our hypothesized food web effects, and were able in some cases to obtain short-term forecasts with slightly lower prediction error using models that account for food web dynamics compared with simpler models. Predictions are the foundation of adaptive management because they allow quantitative assessment of the effects of management actions; however, evaluating predictions requires adequate and high-quality monitoring data. Results from our case studies show that a combination of long-term monitoring and different types of study designs coupled with models of adequate complexity are likely required to better understand populations’ responses to environmental changes and harvest, as well as the consequences for food webs.

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Section: Short-term Predictionsmentioning

confidence: 61%

Section: Including Direct and Indirect Interactionsmentioning

confidence: 82%

Section: Short-term Predictionsmentioning

confidence: 91%

Section: Including Direct and Indirect Interactionsmentioning

confidence: 99%

Section: Short-term Predictionsmentioning

confidence: 99%

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Food web approach for managing Arctic wildlife populations in an era of rapid environmental change

et al. 2022

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When can model‐based estimates replace surveys of wildlife populations that span many discrete management units?

Priadka

Brown

Fedy

et al. 2022

Ecol Sol and Evidence

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Monitoring widely distributed species on a budget presents challenges for the spatio‐temporal allocation of survey effort. When there are multiple discrete units to monitor, survey alternatives such as model‐based estimates can be useful to fill information gaps but may not reliably reflect biological complexity and change. The spatio‐temporal allocation of survey effort that minimizes uncertainty for the greatest number of units within a budget can help to ensure monitoring is optimized. We used aerial survey‐based population estimates of moose (Alces alces) across 30 Wildlife Management Units (WMUs) in Ontario, Canada to parameterize simulated populations and test the performance of different monitoring scenarios in capturing WMU‐specific annual variation and trends. Firstly, we tested scenarios that prioritized conducting a survey for a unit based on one of three management criteria: population state, population uncertainty or number of years between surveys. Also incorporated in the decision framework were WMU‐specific costs and annual budget constraints. Secondly, we tested how using model‐based estimates to fill information gaps improved population and trend estimates. Lastly, we assessed how the utility (based on minimizing population uncertainty) of using a model‐based estimate rather than conducting a survey was impacted by population density, severity of environmental stressors and years since the last survey. Interval‐based monitoring that minimized the number of years between surveys captured accurate trends for the highest number of WMUs, but annual variation was poorly captured regardless of management criteria prioritized. Using model‐based estimates to fill information gaps improved trend estimation. Further, the utility of conducting a survey increased with time since the last survey and was greater for populations with low densities when the severity of environmental stressors was high, while being greater for populations with high densities when environmental severity was low. Overall, the utility of aerial survey monitoring was strongly associated with WMU‐specific monitoring precision and the predictive power of model‐based estimates. If long‐term trends are evident, then there is greater value in using alternatives such as model‐based predictions to replace surveys, but model‐based estimates may be a poor substitute when there is strong annual variation and when using a simple model.

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Defining model complexity: An ecological perspective

Malmborg,

Willson,

Bradley

et al. 2024

Meteorological Applications

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Models have become a key component of scientific hypothesis testing and climate and sustainability planning, as enabled by increased data availability and computing power. As a result, understanding how the perceived ‘complexity’ of a model corresponds to its accuracy and predictive power has become a prevalent research topic. However, a wide variety of definitions of model complexity have been proposed and used, leading to an imprecise understanding of what model complexity is and its consequences across research studies, study systems, and disciplines. Here, we propose a more explicit definition of model complexity, incorporating four facets—model class, model inputs, model parameters, and computational complexity—which are modulated by the complexity of the real‐world process being modelled. We illustrate these facets with several examples drawn from ecological literature. Overall, we argue that precise terminology and metrics of model complexity (e.g., number of parameters, number of inputs) may be necessary to characterize the emergent outcomes of complexity, including model comparison, model performance, model transferability and decision support.

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Iterative model predictions for wildlife populations impacted by rapid climate change

Cited by 16 publications

References 77 publications

Food web approach for managing Arctic wildlife populations in an era of rapid environmental change

Food web approach for managing Arctic wildlife populations in an era of rapid environmental change

When can model‐based estimates replace surveys of wildlife populations that span many discrete management units?

Defining model complexity: An ecological perspective

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