A novel model to accurately predict continental-scale timing of forest green-up

Neupane, Naresh; Peruzzi, Michele; Arab, Ali; Mayor, Stephen J.; Withey, John C.; Ries, Leslie; Finley, Andrew O.

doi:10.1016/j.jag.2022.102747

Cited by 3 publications

(6 citation statements)

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“…2015; Mayor et al . 2017; La Sorte and Graham 2021; Neupane et al . 2022) and we sought to further explore these relationships by using environmental data associated with purple martin first arrivals.…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies have linked temperature, the onset of vegetation greenness, and avian first arrival dates (Wang et al 2015;Mayor et al 2017;La Sorte and Graham 2021;Neupane et al 2022) and we sought to further explore these relationships by using environmental data associated with purple martin first arrivals. We extracted environmental predictor variables from ground-based meteorological observations and remotely sensed satellites.…”

Section: Environmental Predictorsmentioning

confidence: 99%

“…Using the CPC daily minimum and maximum temperature values, we calculated GDD values for each pixel/day. We used a minimum threshold of −5°C (based on Fu et al 2014;Neupane et al 2022). We summed all the degrees above this minimum threshold (daily minimum) up to the daily maximum temperature.…”

Section: Environmental Predictorsmentioning

confidence: 99%

“…Threshold heat requirements vary depending on the forest type (Leinonen and Kramer 2002), with 591°C heat units needed for green-up in deciduous forests of the eastern United States (Fu et al 2014). Neupane et al (2022) showed that the GDD model is particularly accurate for predicting forest greening onset in the eastern United States. Although GDD-based models have been used in agricultural contexts for decades to predict plant and insect development in agricultural contexts (Russelle et al 1984;Herms 2004), they have only recently been used to predict avian lifecycle events because of the degree to which birds rely on insects and plants as food resources (Sockman and Courter 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Neupane et al . (2022) showed that the GDD model is particularly accurate for predicting forest greening onset in the eastern United States. Although GDD-based models have been used in agricultural contexts for decades to predict plant and insect development in agricultural contexts (Russelle et al .…”

Section: Introductionmentioning

confidence: 99%

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Satellite-based environmental variables complement traditional variables in spatio-temporal models of purple martin migration

et al. 2023

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Context As advanced satellite-based environmental data become widely accessible, emerging opportunities exist to understand avian lifecycle events at continental scales. Although this growing toolbox offers much promise, an abundance of options may appear overwhelming to ecologists and point to the need for interdisciplinary collaborations to develop and interpret complex, spatio-temporal models. Aims Here, we demonstrate that satellite-based environmental variables complement conventional variables in spatio-temporal phenology models. The objective of this case study was to assess the degree to which including more sophisticated, satellite-based greenness data in association with a customised growing degree-day metric, can improve traditional phenological models based solely on monthly temperature and precipitation. Methods Using 2001–2018 purple martin (Progne subis) first arrival dates (n = 49 481) from the Purple Martin Conservation Association, we develop a predictive model for their first arrival dates on the basis of traditional temperature and precipitation values from ground-based meteorological stations, the MODIS satellite-based greenness index, and a more sophisticated growing degree-day metric. We used a Bayesian framework to construct 10 spatio-temporal candidate models on the basis of different combinations of predictor variables and our best model included a combination of both traditional and customised MODIS-based variables. Key results Our results indicated that purple martins arrive earlier when greening occurs earlier than the mean, which is also associated with warmer spring temperatures. In addition, wetter February months also predicted earlier martin arrivals. There was no directional change in purple martin first arrival dates from 2001 to 2018 in our study region. Conclusions Our results suggest that satellite-based environmental variables complement traditional variables such as mean monthly temperature and precipitation in models of purple martin migratory phenology. Implications Including emerging and conventional variables in spatio-temporal models allows complex migratory changes to be detected and interpreted at broad spatial scales, which is critical as Citizen Science efforts expand. Our results also pointed to the importance of assembling interdisciplinary research teams to assess the utility of novel data products.

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Section: Methodsmentioning

confidence: 99%