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Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network.

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Title: Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network.
Authors: Prager, Case M.1,2 (AUTHOR) case.prager@gmail.com, Classen, Aimee T.1,2,3 (AUTHOR), Sundqvist, Maja K.3,4 (AUTHOR), Barrios‐Garcia, Maria Noelia5,6 (AUTHOR), Cameron, Erin K.7 (AUTHOR), Chen, Litong8 (AUTHOR), Chisholm, Chelsea9 (AUTHOR), Crowther, Thomas W.9 (AUTHOR), Deslippe, Julie R.10 (AUTHOR), Grigulis, Karl11 (AUTHOR), He, Jin‐Sheng12 (AUTHOR), Henning, Jeremiah A.2,13 (AUTHOR), Hovenden, Mark14 (AUTHOR), Høye, Toke T. Thomas15 (AUTHOR), Jing, Xin3,16 (AUTHOR), Lavorel, Sandra11 (AUTHOR), McLaren, Jennie R.17 (AUTHOR), Metcalfe, Daniel B.18 (AUTHOR), Newman, Gregory S.19 (AUTHOR), Nielsen, Marie Louise15 (AUTHOR)
Source: Ecology & Evolution (20457758). Oct2022, Vol. 12 Issue 10, p1-14. 14p.
Subject Terms: *Biological extinction, *Climate change, *Ecosystems, *Mountain forests, Statistical models, Communities, Factorial experiment designs
Abstract: A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta‐analyses across many independent experiments. However, results from single‐site studies tend to have limited generality. Although meta‐analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long‐term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high‐ and low‐elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above‐ and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities. [ABSTRACT FROM AUTHOR]
Copyright of Ecology & Evolution (20457758) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network.
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  Data: <searchLink fieldCode="AR" term="%22Prager%2C+Case+M%2E%22">Prager, Case M.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> case.prager@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Classen%2C+Aimee+T%2E%22">Classen, Aimee T.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sundqvist%2C+Maja+K%2E%22">Sundqvist, Maja K.</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Barrios‐Garcia%2C+Maria Noelia%22">Barrios‐Garcia, Maria Noelia</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cameron%2C+Erin+K%2E%22">Cameron, Erin K.</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Litong%22">Chen, Litong</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chisholm%2C+Chelsea%22">Chisholm, Chelsea</searchLink><relatesTo>9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Crowther%2C+Thomas+W%2E%22">Crowther, Thomas W.</searchLink><relatesTo>9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deslippe%2C+Julie+R%2E%22">Deslippe, Julie R.</searchLink><relatesTo>10</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Grigulis%2C+Karl%22">Grigulis, Karl</searchLink><relatesTo>11</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Jin‐Sheng%22">He, Jin‐Sheng</searchLink><relatesTo>12</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Henning%2C+Jeremiah+A%2E%22">Henning, Jeremiah A.</searchLink><relatesTo>2,13</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hovenden%2C+Mark%22">Hovenden, Mark</searchLink><relatesTo>14</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Høye%2C+Toke+T%2E+Thomas%22">Høye, Toke T. Thomas</searchLink><relatesTo>15</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jing%2C+Xin%22">Jing, Xin</searchLink><relatesTo>3,16</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lavorel%2C+Sandra%22">Lavorel, Sandra</searchLink><relatesTo>11</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22McLaren%2C+Jennie+R%2E%22">McLaren, Jennie R.</searchLink><relatesTo>17</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Metcalfe%2C+Daniel+B%2E%22">Metcalfe, Daniel B.</searchLink><relatesTo>18</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Newman%2C+Gregory+S%2E%22">Newman, Gregory S.</searchLink><relatesTo>19</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nielsen%2C+Marie+Louise%22">Nielsen, Marie Louise</searchLink><relatesTo>15</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Ecology+%26+Evolution+%2820457758%29%22">Ecology & Evolution (20457758)</searchLink>. Oct2022, Vol. 12 Issue 10, p1-14. 14p.
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  Data: *<searchLink fieldCode="DE" term="%22Biological+extinction%22">Biological extinction</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br />*<searchLink fieldCode="DE" term="%22Ecosystems%22">Ecosystems</searchLink><br />*<searchLink fieldCode="DE" term="%22Mountain+forests%22">Mountain forests</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Communities%22">Communities</searchLink><br /><searchLink fieldCode="DE" term="%22Factorial+experiment+designs%22">Factorial experiment designs</searchLink>
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  Data: A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta‐analyses across many independent experiments. However, results from single‐site studies tend to have limited generality. Although meta‐analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long‐term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high‐ and low‐elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above‐ and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Ecology & Evolution (20457758) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=159936860
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        Value: 10.1002/ece3.9396
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        Text: English
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      – SubjectFull: Communities
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      – SubjectFull: Factorial experiment designs
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