Similarity in spatial structure constrains ecosystem relationships: Building a macroscale understanding of lakes. Lapierre, J., Collins, S. M., Seekell, D. A., Cheruvelil, K. S., Tan, P., Skaff, N. K., Taranu, Z. E., Fergus, C. E., & Soranno, P. A. Global Ecology and Biogeography, 27:1251–1263, September, 2018. 00000![Similarity in spatial structure constrains ecosystem relationships: Building a macroscale understanding of lakes [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Aim We aimed to measure the dominant spatial patterns in ecosystem properties (such as nutrients and measures of primary production) and the multi-scaled geographical driver variables of these properties and to quantify how the spatial structure of pattern in all of these variables influences the strength of relationships among them. Location and time period: We studied \textgreater 8,500 lakes in a 1.8 million km2 area of Northeast U.S.A. Data comprised 10-year medians (2002–2011) for measured ecosystem properties, long-term climate averages and recent land use/land cover variables. Major taxa studied: We focused on ecosystem properties at the base of aquatic food webs, including concentrations of nutrients and algal pigments that are proxies of primary productivity. Methods We quantified spatial structure in ecosystem properties and their geographical driver variables using distance-based Moran eigenvector maps (dbMEMs). We then compared the similarity in spatial structure for all pairs of variables with the correlation between variables to illustrate how spatial structure constrains relationships among ecosystem properties. Results The strength of spatial structure decreased in order for climate, land cover/use, lake ecosystem properties and lake and landscape morphometry. Having a comparable spatial structure is a necessary condition to observe a strong relationship between a pair of variables, but not a sufficient one; variables with very different spatial structure are never strongly correlated. Lake ecosystem properties tended to have an intermediary spatial structure compared with that of their main drivers, probably because climate and landscape variables with known ecological links induce spatial patterns. Main conclusions:Our empirical results describe inherent spatial constraints that dictate the expected relationships between ecosystem properties and their geographical drivers at macroscales. Our results also suggest that understanding the spatial scales at which ecological processes operate is necessary to predict the effects of multi-scaled environmental changes on ecosystem properties.
@article{lapierre_similarity_2018,
title = {Similarity in spatial structure constrains ecosystem relationships: {Building} a macroscale understanding of lakes},
volume = {27},
copyright = {© 2018 John Wiley \& Sons Ltd},
issn = {1466-8238},
shorttitle = {Similarity in spatial structure constrains ecosystem relationships},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/geb.12781},
doi = {10.1111/geb.12781},
abstract = {Aim We aimed to measure the dominant spatial patterns in ecosystem properties (such as nutrients and measures of primary production) and the multi-scaled geographical driver variables of these properties and to quantify how the spatial structure of pattern in all of these variables influences the strength of relationships among them. Location and time period: We studied {\textgreater} 8,500 lakes in a 1.8 million km2 area of Northeast U.S.A. Data comprised 10-year medians (2002–2011) for measured ecosystem properties, long-term climate averages and recent land use/land cover variables. Major taxa studied: We focused on ecosystem properties at the base of aquatic food webs, including concentrations of nutrients and algal pigments that are proxies of primary productivity. Methods We quantified spatial structure in ecosystem properties and their geographical driver variables using distance-based Moran eigenvector maps (dbMEMs). We then compared the similarity in spatial structure for all pairs of variables with the correlation between variables to illustrate how spatial structure constrains relationships among ecosystem properties. Results The strength of spatial structure decreased in order for climate, land cover/use, lake ecosystem properties and lake and landscape morphometry. Having a comparable spatial structure is a necessary condition to observe a strong relationship between a pair of variables, but not a sufficient one; variables with very different spatial structure are never strongly correlated. Lake ecosystem properties tended to have an intermediary spatial structure compared with that of their main drivers, probably because climate and landscape variables with known ecological links induce spatial patterns. Main conclusions:Our empirical results describe inherent spatial constraints that dictate the expected relationships between ecosystem properties and their geographical drivers at macroscales. Our results also suggest that understanding the spatial scales at which ecological processes operate is necessary to predict the effects of multi-scaled environmental changes on ecosystem properties.},
language = {en},
urldate = {2018-09-20},
journal = {Global Ecology and Biogeography},
author = {Lapierre, Jean-Francois and Collins, Sarah M. and Seekell, David A. and Cheruvelil, Kendra Spence and Tan, Pang-Ning and Skaff, Nicholas K. and Taranu, Zofia E. and Fergus, C. Emi and Soranno, Patricia A.},
month = sep,
year = {2018},
note = {00000},
keywords = {\#nosource, Moran eigenvector maps, climate, ecosystem, lake, landscape, macroscales, spatial autocorrelation, spatial scale, spatial structure},
pages = {1251--1263},
}
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Data comprised 10-year medians (2002–2011) for measured ecosystem properties, long-term climate averages and recent land use/land cover variables. Major taxa studied: We focused on ecosystem properties at the base of aquatic food webs, including concentrations of nutrients and algal pigments that are proxies of primary productivity. Methods We quantified spatial structure in ecosystem properties and their geographical driver variables using distance-based Moran eigenvector maps (dbMEMs). We then compared the similarity in spatial structure for all pairs of variables with the correlation between variables to illustrate how spatial structure constrains relationships among ecosystem properties. Results The strength of spatial structure decreased in order for climate, land cover/use, lake ecosystem properties and lake and landscape morphometry. Having a comparable spatial structure is a necessary condition to observe a strong relationship between a pair of variables, but not a sufficient one; variables with very different spatial structure are never strongly correlated. Lake ecosystem properties tended to have an intermediary spatial structure compared with that of their main drivers, probably because climate and landscape variables with known ecological links induce spatial patterns. Main conclusions:Our empirical results describe inherent spatial constraints that dictate the expected relationships between ecosystem properties and their geographical drivers at macroscales. 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