End‐user involvement to improve predictions and management of populations with complex dynamics and multiple drivers

Henden, John‐André; Ims, Rolf A.; Yoccoz, Nigel G.; Asbjørnsen, Einar Johannes; Stien, Audun; Mellard, Jarad P.; Tveraa, Torkild; Marolla, Filippo; Jepsen, Jane Uhd

doi:10.1002/eap.2120

Cited by 23 publications

(24 citation statements)

References 93 publications

(140 reference statements)

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“…The 12‐year action period in this case study included three 4‐year rodent cycles. The amplitude of these cycles, which is known to affect ptarmigan dynamics mediated by predator functional and numerical response to rodent abundance (Angelstam et al., 1984; Henden, Ims, et al., 2020; Lack, 1954; Steen et al., 1988), varied in both time and space. Having sufficiently long time series at the scale of sub‐regions allowed us to account for this source of complexity and thereby improve the assessment of the culling impact.…”

Section: Discussionmentioning

confidence: 99%

“…We here emphasize four food web processes that potentially constitute important drivers of ptarmigan population dynamics in the study area (cf. Henden, Ims, et al., 2020, Figure 2); small rodents (multi‐annual interaction cycles), reindeer (carrion subsidies to predators), red fox (predation and control actions) and humans (ptarmigan hunting).…”

Section: Methodsmentioning

confidence: 99%

“…Time series of willow ptarmigan population counts during 2000–2016 were obtained from Hønsefuglportalen (http://honsefugl.nina.no/Innsyn/) and are based on annual surveys of transect lines distributed across Finnmark County (Henden, Ims, et al., 2020; Nilsen et al., 2018). Transect lines are surveyed annually from the 5th–20th of August by trained personnel with pointing dogs according to a distance sampling protocol (Buckland et al., 2001).…”

Section: Methodsmentioning

confidence: 99%

“…Ptarmigan hunting affects ptarmigan population dynamics proportionally to the harvesting rate (Henden, Ims, et al., 2020; Pedersen et al., 2004). Considerable changes in hunting regulations in the study area over the last two decades have likely caused temporally variable impacts of hunting on ptarmigan populations.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Accounting for food web dynamics when assessing the impact of mesopredator control on declining prey populations

Henden

Ehrich

Soininen

et al. 2020

Journal of Applied Ecology

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Accounting for food web dynamics when assessing the impact of mesopredator control on declining prey populations

Henden

Ehrich

Soininen

et al. 2020

Journal of Applied Ecology

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“…The short timescale used for predictions allows analyses to be repeated, models to be validated, and evidence to increase rapidly (Dietze et al, 2018). The near‐term forecasting approach has proved especially profitable to deal with ecosystems, species, or populations subject to management (Henden et al, 2020; Nichols et al, 2015), because forecasts are generated at a timescale that can be influenced by decision‐making. Near‐term forecasting, in fact, constitutes the foundation of adaptive management (Nichols et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Iterative model predictions for wildlife populations impacted by rapid climate change

Marolla

Henden

Fuglei

et al. 2021

Global Change Biology

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To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long‐term monitoring data series to generate iterative near‐term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near‐term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near‐term forecasting in the case of a harvested population of rock ptarmigan in high‐arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state‐space models to ptarmigan counts from point transect distance sampling during 2005–2019 and developed two types of predictions: (1) explanatory predictions to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) anticipatory predictions to assess the ability of candidate models of increasing complexity to forecast next‐year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain‐on‐snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near‐term forecasting ability of the models improved nonlinearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next‐year population density, demonstrating the value of ecosystem‐based monitoring. Overall, our study illustrates the power of integrating near‐term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach.

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Increased adoption of best practices in ecological forecasting enables comparisons of forecastability

Lewis

Woelmer

Wander

et al. 2021

Ecological Applications

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Near‐term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross‐ecosystem analysis of near‐term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near‐term (≤10‐yr forecast horizon) ecological forecasting papers to understand the development and current state of near‐term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near‐term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near‐term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.

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End‐user involvement to improve predictions and management of populations with complex dynamics and multiple drivers

Abstract: End-user involvement to improve predictions and management of populations with complex dynamics and multiple drivers. Ecological Applications

Cited by 23 publications

References 93 publications

Accounting for food web dynamics when assessing the impact of mesopredator control on declining prey populations

Accounting for food web dynamics when assessing the impact of mesopredator control on declining prey populations

Iterative model predictions for wildlife populations impacted by rapid climate change

Increased adoption of best practices in ecological forecasting enables comparisons of forecastability

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