Advancing plant phenology causes an increasing trophic mismatch in an income breeder across a wide elevational range

Rehnus, Maik; Peláez, Marta; Bollmann, Kurt

doi:10.1002/ecs2.3144

Cited by 18 publications

(12 citation statements)

References 60 publications

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“…Conversely increasing temperature mostly had a divergent association, increasing peak productivity but lowering season-long productivity. Implications of warming temperatures on productivity can vary between vegetation functional groups (Livensperger et al, 2016), have cascading ecosystem effects (Beard et al, 2019;Rehnus et al, 2020;Renner & Zohner, 2018), and include consequences to human society (Bezerra et al, 2019;Stevenson et al, 2015).…”

Section: The Role Of Climate In Phenological Variabilitymentioning

confidence: 99%

“…Conversely, not accounting for spatiotemporal patterns in phenology can lead to mismanagement, including food-security risks (e.g., Bezerra et al, 2019;Sadras & Monzon, 2006;Stevenson et al, 2015), ecological disruption caused by phenological mismatches (e.g., Rehnus et al, 2020;Renner & Zohner, 2018), and sampling biases (Gibson et al, 2016;Smith et al, 2017). Vegetation phenology interacts with climate and has cascading impacts to ecosystem processes such as nutrient cycling and the maintenance of ecosystem services (Beard et al, 2019;Cleland et al, 2007;Morisette et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Vegetation phenology interacts with climate and has cascading impacts to ecosystem processes such as nutrient cycling and the maintenance of ecosystem services (Beard et al, 2019;Cleland et al, 2007;Morisette et al, 2009). Furthermore, wildlife species within a system may respond to vegetation phenology and productivity with potential cascading effects on seasonal timings, fecundity, interspecific interactions, and behavior (Donnelly et al, 2018;Rehnus et al, 2020;Stoner et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Some predictions of phenological changes through time, like potential future changes to productivity, may be identified using annual weather variables. Furthermore, analyses utilizing phenology such as models of ecosystem-wide carbon uptake or responses of wildlife to climate change and variability should consider multiple phenological measures and context dependencies(Donnelly et al, 2018;Rehnus et al, 2020;…”

mentioning

confidence: 99%

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Is the grass always greener? Land surface phenology reveals differences in peak and season‐long vegetation productivity responses to climate and management

Wood

Powell

Stoy

et al. 2021

Ecology and Evolution

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Section: The Role Of Climate In Phenological Variabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Is the grass always greener? Land surface phenology reveals differences in peak and season‐long vegetation productivity responses to climate and management

Wood

Powell

Stoy

et al. 2021

Ecology and Evolution

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“…Understanding the timing, magnitude, and duration of life-history events, phenology, forms a key aspect of the assessment of the function of vegetation communities [29]. Changes in phenology can lead to ecological disruptions, plant-herbivore mismatch, alterations to competitive interactions, and disrupted nutrient fluxes, e.g., [30][31][32][33]. Phenology and production of rangelands assessed at the community level has identified important spatial and temporal variability and trends [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Multiple UAV Flights across the Growing Season Can Characterize Fine Scale Phenological Heterogeneity within and among Vegetation Functional Groups

et al. 2022

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Grasslands and shrublands exhibit pronounced spatial and temporal variability in structure and function with differences in phenology that can be difficult to observe. Unpiloted aerial vehicles (UAVs) can measure vegetation spectral patterns relatively cheaply and repeatably at fine spatial resolution. We tested the ability of UAVs to measure phenological variability within vegetation functional groups and to improve classification accuracy at two sites in Montana, U.S.A. We tested four flight frequencies during the growing season. Classification accuracy based on reference data increased by 5–10% between a single flight and scenarios including all conducted flights. Accuracy increased from 50.6 to 61.4% at the drier site, while at the more mesic/densely vegetated site, we found an increase of 59.0 to 64.4% between a single and multiple flights over the growing season. Peak green-up varied by 2–4 weeks within the scenes, and sparse vegetation classes had only a short detectable window of active phtosynthesis; therefore, a single flight could not capture all vegetation that was active across the growing season. The multi-temporal analyses identified differences in the seasonal timing of green-up and senescence within herbaceous and sagebrush classes. Multiple UAV measurements can identify the fine-scale phenological variability in complex mixed grass/shrub vegetation.

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Fawn birthdays: From opportunistically sampled fawn rescue data to true wildlife demographic parameters

Kauffert

Baur

Matiu

et al. 2023

Ecol Sol and Evidence

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Spring mowing in May and June is one of the main causes of mortality of roe deer fawns in agricultural regions. Knowing the exact birth distribution of fawns is important to guide farmers in their pre‐mowing precautions to avoid fawn deaths. Wildlife volunteers searching fields prior to mowing can act as citizen scientists by producing data sets of rescued fawns and their approximate age at find. However, due to weather‐dependent searches, the corresponding birth distributions can be highly skewed. We simulated virtual field data to examine the shortcomings of such data sources and introduced two algorithms for reconstructing reliable birth distribution parameters (mean and standard deviation) based on skewed samples. We found that weather‐dependent search data biased the calculated means and standard deviations by up to 14 and 5 days, respectively. However, the use of the proposed advanced algorithms (Grid Search and Machine Learning) resulted in better estimates of the sample means and standard deviations by reducing the root‐mean‐square error by 65% and 80% respectively. Furthermore, the Grid Search algorithm was able to capture birth distribution parameters based on real citizen science data in Bavaria, Germany, from 2021, which are close to the results of more systematic samples of the same year. The simulation exercise highlighted the shortcomings and discrepancies of using non‐probabilistic samples, for example on the occasion of mowing activities, to study demographic parameters compared to the true simulated distribution. Yet, the proposed algorithms can address these drawbacks and potentially turn citizen science data into an important data source for wildlife studies. This could ultimately help reduce wildlife losses during the mowing season by better knowing the distribution of births in a region.

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Advancing plant phenology causes an increasing trophic mismatch in an income breeder across a wide elevational range

Abstract: Advancing plant phenology causes an increasing trophic mismatch in an income breeder across a wide elevational range.

Cited by 18 publications

References 60 publications

Is the grass always greener? Land surface phenology reveals differences in peak and season‐long vegetation productivity responses to climate and management

Is the grass always greener? Land surface phenology reveals differences in peak and season‐long vegetation productivity responses to climate and management

Multiple UAV Flights across the Growing Season Can Characterize Fine Scale Phenological Heterogeneity within and among Vegetation Functional Groups

Fawn birthdays: From opportunistically sampled fawn rescue data to true wildlife demographic parameters

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