1
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Allen-Perkins A, García-Callejas D, Bartomeus I, Godoy O. Structural asymmetry in biotic interactions as a tool to understand and predict ecological persistence. Ecol Lett 2023; 26:1647-1662. [PMID: 37515408 DOI: 10.1111/ele.14291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023]
Abstract
A universal feature of ecological systems is that species do not interact with others with the same sign and strength. Yet, the consequences of this asymmetry in biotic interactions for the short- and long-term persistence of individual species and entire communities remains unclear. Here, we develop a set of metrics to evaluate how asymmetric interactions among species translate to asymmetries in their individual vulnerability to extinction under changing environmental conditions. These metrics, which solve previous limitations of how to independently quantify the size from the shape of the so-called feasibility domain, provide rigorous advances to understand simultaneously why some species and communities present more opportunities to persist than others. We further demonstrate that our shape-related metrics are useful to predict short-term changes in species' relative abundances during 7 years in a Mediterranean grassland. Our approach is designed to be applied to any ecological system regardless of the number of species and type of interactions. With it, we show that is possible to obtain both mechanistic and predictive information on ecological persistence for individual species and entire communities, paving the way for a stronger integration of theoretical and empirical research.
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Affiliation(s)
- Alfonso Allen-Perkins
- Departamento de Ingeniería Eléctrica, Electrónica, Automática y Física Aplicada, ETSIDI, Technical University of Madrid, Madrid, Spain
| | - David García-Callejas
- School of Biological Sciences, University of Canterbury, Christchurch, New Zealand
- Landcare Research, Lincoln, New Zealand
| | | | - Oscar Godoy
- Departamento de Biología, Instituto Universitario de Ciencias del Mar (INMAR), Universidad de Cádiz, Puerto Real, Spain
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2
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Blaszczak JR, Yackulic CB, Shriver RK, Hall RO. Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience. Ecol Lett 2023; 26:1510-1522. [PMID: 37353910 DOI: 10.1111/ele.14269] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 05/20/2023] [Accepted: 05/23/2023] [Indexed: 06/25/2023]
Abstract
Directly observing autotrophic biomass at ecologically relevant frequencies is difficult in many ecosystems, hampering our ability to predict productivity through time. Since disturbances can impart distinct reductions in river productivity through time by modifying underlying standing stocks of biomass, mechanistic models fit to productivity time series can infer underlying biomass dynamics. We incorporated biomass dynamics into a river ecosystem productivity model for six rivers to identify disturbance flow thresholds and understand the resilience of primary producers. The magnitude of flood necessary to disturb biomass and thereby reduce ecosystem productivity was consistently lower than the more commonly used disturbance flow threshold of the flood magnitude necessary to mobilize river bed sediment. The estimated daily maximum percent increase in biomass (a proxy for resilience) ranged from 5% to 42% across rivers. Our latent biomass model improves understanding of disturbance thresholds and recovery patterns of autotrophic biomass within river ecosystems.
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Affiliation(s)
- Joanna R Blaszczak
- Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada, USA
- Flathead Lake Biological Station, University of Montana, Polson, Montana, USA
| | - Charles B Yackulic
- U.S. Geological Survey, Southwest Biological Science Center, Flagstaff, Arizona, USA
| | - Robert K Shriver
- Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada, USA
| | - Robert O Hall
- Flathead Lake Biological Station, University of Montana, Polson, Montana, USA
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3
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Paniw M, García-Callejas D, Lloret F, Bassar RD, Travis J, Godoy O. Pathways to global-change effects on biodiversity: new opportunities for dynamically forecasting demography and species interactions. Proc Biol Sci 2023; 290:20221494. [PMID: 36809806 PMCID: PMC9943645 DOI: 10.1098/rspb.2022.1494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023] Open
Abstract
In structured populations, persistence under environmental change may be particularly threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life cycle stages, as opposed to a single stage. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting-but are rarely available. Here, we first review the current shortcomings in assessing demographic feedbacks in population and community dynamics. We then present an overview of advances in statistical tools that provide an opportunity to leverage population-level data on abundances of multiple species to infer stage-specific demography. Lastly, we showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction for several interacting species in a Mediterranean shrub community. This case study shows that climate change threatens populations most strongly by changing the interaction effects of conspecific and heterospecific neighbours on both juvenile and adult survival. Thus, the repurposing of multi-species abundance data for mechanistic forecasting can substantially improve our understanding of emerging threats on biodiversity.
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Affiliation(s)
- Maria Paniw
- Department of Conservation Biology and Global Change, Estación Biológica de Doñana (EBD-CSIC), Seville, 41001 Spain.,Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich 8057, Switzerland
| | - David García-Callejas
- Department of Integrative Ecology, Estación Biológica de Doñana (EBD-CSIC), Seville, 41001 Spain.,Instituto Universitario de Investigación Marina (INMAR), Departamento de Biología, Universidad de Cádiz, Campus Río San Pedro, 11510 Puerto Real, Spain
| | - Francisco Lloret
- Center for Ecological Research and Forestry Applications (CREAF), Cerdanyola del Vallès 08193, Spain.,Department Animal Biology, Plant Biology and Ecology, Universitat Autònoma Barcelona, Cerdanyola del Vallès 08193, Spain
| | - Ronald D Bassar
- Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA
| | - Joseph Travis
- Department of Biological Science, Florida State University, Tallahassee, FL 32306, USA
| | - Oscar Godoy
- Instituto Universitario de Investigación Marina (INMAR), Departamento de Biología, Universidad de Cádiz, Campus Río San Pedro, 11510 Puerto Real, Spain
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4
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Tredennick AT, Hooker G, Ellner SP, Adler PB. A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology 2021; 102:e03336. [PMID: 33710619 PMCID: PMC8187274 DOI: 10.1002/ecy.3336] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 10/08/2020] [Accepted: 12/06/2020] [Indexed: 11/12/2022]
Abstract
Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions.
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Affiliation(s)
- Andrew T Tredennick
- Western EcoSystems Technology, Inc., 1610 East Reynolds Street, Laramie, Wyoming, 82072, USA
| | - Giles Hooker
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, 14853, USA
| | - Stephen P Ellner
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, 14853, USA
| | - Peter B Adler
- Department of Wildland Resources and the Ecology Center, Utah State University, 5230 Old Main Hill, Logan, Utah, 84322, USA
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5
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Evers SM, Knight TM, Inouye DW, Miller TEX, Salguero-Gómez R, Iler AM, Compagnoni A. Lagged and dormant season climate better predict plant vital rates than climate during the growing season. GLOBAL CHANGE BIOLOGY 2021; 27:1927-1941. [PMID: 33586192 DOI: 10.1111/gcb.15519] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 12/19/2020] [Accepted: 12/28/2020] [Indexed: 06/12/2023]
Abstract
Understanding the effects of climate on the vital rates (e.g., survival, development, reproduction) and dynamics of natural populations is a long-standing quest in ecology, with ever-increasing relevance in the face of climate change. However, linking climate drivers to demographic processes requires identifying the appropriate time windows during which climate influences vital rates. Researchers often do not have access to the long-term data required to test a large number of windows, and are thus forced to make a priori choices. In this study, we first synthesize the literature to assess current a priori choices employed in studies performed on 104 plant species that link climate drivers with demographic responses. Second, we use a sliding-window approach to investigate which combination of climate drivers and temporal window have the best predictive ability for vital rates of four perennial plant species that each have over a decade of demographic data (Helianthella quinquenervis, Frasera speciosa, Cylindriopuntia imbricata, and Cryptantha flava). Our literature review shows that most studies consider time windows in only the year preceding the measurement of the vital rate(s) of interest, and focus on annual or growing season temporal scales. In contrast, our sliding-window analysis shows that in only four out of 13 vital rates the selected climate drivers have time windows that align with, or are similar to, the growing season. For many vital rates, the best window lagged more than 1 year and up to 4 years before the measurement of the vital rate. Our results demonstrate that for the vital rates of these four species, climate drivers that are lagged or outside of the growing season are the norm. Our study suggests that considering climatic predictors that fall outside of the most recent growing season will improve our understanding of how climate affects population dynamics.
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Affiliation(s)
- Sanne M Evers
- Institute of Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Tiffany M Knight
- Institute of Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Community Ecology, Helmholtz Centre for Environmental Research - UFZ, Halle (Saale), Germany
| | - David W Inouye
- Department of Biology, University of Maryland, College Park, MD, USA
- Rocky Mountain Biological Laboratory, Crested Butte, CO, USA
| | - Tom E X Miller
- Program in Ecology and Evolutionary Biology, Department of BioSciences, Rice University, Houston, TX, USA
| | | | - Amy M Iler
- Rocky Mountain Biological Laboratory, Crested Butte, CO, USA
- The Negaunee Institute for Plant Conservation Science and Action, Chicago Botanic Garden, Glencoe, IL, USA
| | - Aldo Compagnoni
- Institute of Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
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6
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Goodsell RM, Childs DZ, Spencer M, Coutts S, Vergnon R, Swinfield T, Queenborough SA, Freckleton RP. Developing hierarchical density‐structured models to study the national‐scale dynamics of an arable weed. ECOL MONOGR 2021. [DOI: 10.1002/ecm.1449] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Robert M. Goodsell
- Department of Animal and Plant Sciences University of Sheffield Sheffield S10 2TN United Kingdom
| | - Dylan Z. Childs
- Department of Animal and Plant Sciences University of Sheffield Sheffield S10 2TN United Kingdom
| | - Matthew Spencer
- School of Environmental Sciences University of Liverpool Liverpool L69 3GP United Kingdom
| | - Shaun Coutts
- Lincoln Institute for Agri‐food Technology University of Lincoln Lincoln LN2 2LG United Kingdom
| | - Remi Vergnon
- Department of Animal and Plant Sciences University of Sheffield Sheffield S10 2TN United Kingdom
| | - Tom Swinfield
- RSPB Potton road Sandy Bedfordshire SH19 2DL United Kingdom
| | - Simon A. Queenborough
- Yale School of Forestry & Environmental Studies Yale University New Haven Connecticut 06511 USA
| | - Robert P. Freckleton
- Department of Animal and Plant Sciences University of Sheffield Sheffield S10 2TN United Kingdom
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7
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The Net Effect of Functional Traits on Fitness. Trends Ecol Evol 2020; 35:1037-1047. [PMID: 32807503 DOI: 10.1016/j.tree.2020.07.010] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 07/14/2020] [Accepted: 07/17/2020] [Indexed: 11/21/2022]
Abstract
Generalizing the effect of traits on performance across species may be achievable if traits explain variation in population fitness. However, testing relationships between traits and vital rates to infer effects on fitness can be misleading. Demographic trade-offs can generate variation in vital rates that yield equal population growth rates, thereby obscuring the net effect of traits on fitness. To address this problem, we describe a diversity of approaches to quantify intrinsic growth rates of plant populations, including experiments beyond range boundaries, density-dependent population models built from long-term demographic data, theoretical models, and methods that leverage widely available monitoring data. Linking plant traits directly to intrinsic growth rates is a fundamental step toward rigorous predictions of population dynamics and community assembly.
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8
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Block S, Alexander JM, Levine JM. Phenological plasticity is a poor predictor of subalpine plant population performance following experimental climate change. OIKOS 2020; 129:184-193. [PMID: 32001946 DOI: 10.1111/oik.06667] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Phenological shifts, changes in the seasonal timing of life cycle events, are among the best documented responses of species to climate change. However, the consequences of these phenological shifts for population dynamics remain unclear. Population growth could be enhanced if species that advance their phenology benefit from longer growing seasons and gain a pre-emptive advantage in resource competition. However, it might also be reduced if phenological advances increase exposure to stresses, such as herbivores and, in colder climates, harsh abiotic conditions early in the growing season. We exposed subalpine grasslands to ~ 3 K of warming by transplanting intact turfs from 2000 m to 1400 m elevation in the eastern Swiss Alps, with turfs transplanted within the 2000 m site acting as a control. In the first growing season after transplantation, we recorded species' flowering phenology at both elevations. We also measured species' cover change for three consecutive years as a measure of plant performance. We used models to estimate species' phenological plasticity (the response of flowering time to the change in climate) and analysed its relationship with cover changes following climate change. The phenological plasticity of the 18 species in our study varied widely but was unrelated to their changes in cover. Moreover, early- and late-flowering species did not differ in their cover response to warming, nor in the relationship between cover changes and phenological plasticity. These results were replicated in a similar transplant experiment within the same subalpine community, established one year earlier and using larger turfs. We discuss the various ecological processes that can be affected by phenological shifts, and argue why the population-level consequences of these shifts are likely to be species- and context-specific. Our results highlight the importance of testing assumptions about how warming-induced changes in phenotypic traits, like phenology, impact population dynamics.
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Affiliation(s)
- Sebastián Block
- Institute of Integrative Biology, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
| | - Jake M Alexander
- Institute of Integrative Biology, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
| | - Jonathan M Levine
- Institute of Integrative Biology, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland.,Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey 08544-1003, USA
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9
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Chevalier M, Knape J. The cost of complexity in forecasts of population abundances is reduced but not eliminated by borrowing information across space using a hierarchical approach. OIKOS 2019. [DOI: 10.1111/oik.06401] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Mathieu Chevalier
- Dept of Ecology, Swedish Univ. of Agricultural Sciences Box 7044 SE‐750 07 Uppsala Sweden
- Dept of Ecology and Evolution, Univ. of Lausanne Lausanne Switzerland
| | - Jonas Knape
- Dept of Ecology, Swedish Univ. of Agricultural Sciences Box 7044 SE‐750 07 Uppsala Sweden
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10
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Barraquand F, Gimenez O. Integrating multiple data sources to fit matrix population models for interacting species. Ecol Modell 2019. [DOI: 10.1016/j.ecolmodel.2019.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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11
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Yen JDL, Tonkin Z, Lyon J, Koster W, Kitchingman A, Stamation K, Vesk PA. Integrating Multiple Data Types to Connect Ecological Theory and Data Among Levels. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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12
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Oldfather MF, Ackerly DD. Microclimate and demography interact to shape stable population dynamics across the range of an alpine plant. THE NEW PHYTOLOGIST 2019; 222:193-205. [PMID: 30372539 DOI: 10.1111/nph.15565] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 10/20/2018] [Indexed: 06/08/2023]
Abstract
Heterogeneous terrain in montane systems results in a decoupling of climatic gradients. Population dynamics across species' ranges in these heterogeneous landscapes are shaped by relationships between demographic rates and these interwoven climate gradients. Linking demography and climate variables across species' ranges refines our understanding of the underlying mechanisms of species' current and future ranges. We explored the importance of multiple microclimatic gradients in shaping individual demographic rates and population growth rates in 16 populations across the elevational distribution of an alpine plant (Ivesia lycopodioides var. scandularis). Using integral projection modeling, we ask how each rate varies across three microclimate gradients: accumulated degree-days, growing-season soil moisture, and days of snow cover. Range-wide variation in demographic rates was best explained by the combined influence of multiple microclimatic variables. Different pairs of demographic rates exhibited both similar and inverse responses to the same microclimatic gradient, and the microclimatic effects often varied with plant size. These responses resulted in range-wide projected population persistence, with no declining populations at either elevational range edge or at the extremes of the microclimate gradients. The complex relationships between topography, microclimate and demography suggest that populations across a species' range may have unique demographic pathways to stable population dynamics.
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Affiliation(s)
- Meagan F Oldfather
- Department of Integrative Biology, University of California, Berkeley, CA, 94720, USA
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, 80309, USA
| | - David D Ackerly
- Department of Integrative Biology, University of California, Berkeley, CA, 94720, USA
- Jepson Herbarium, University of California, Berkeley, CA, 94720, USA
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13
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Caughlin TT, Damschen EI, Haddad NM, Levey DJ, Warneke C, Brudvig LA. Landscape heterogeneity is key to forecasting outcomes of plant reintroduction. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2019; 29:e01850. [PMID: 30821885 DOI: 10.1002/eap.1850] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 10/26/2018] [Accepted: 12/04/2018] [Indexed: 06/09/2023]
Abstract
Conservation and restoration projects often involve starting new populations by introducing individuals into portions of their native or projected range. Such efforts can help meet many related goals, including habitat creation, ecosystem service provisioning, assisted migration, and the reintroduction of imperiled species following local extirpation. The outcomes of reintroduction efforts, however, are highly variable, with results ranging from local extinction to dramatic population growth; reasons for this variation remain unclear. Here, we ask whether population growth following plant reintroductions is governed by variation at two scales: the scale of individual habitat patches to which individuals are reintroduced, and larger among-landscape scales in which similar patches may be situated in landscapes that differ in matrix type, soil conditions, and other factors. Quantifying demographic variation at these two scales will help prioritize locations for introduction and, once introductions take place, forecast population growth. This work took place within a large-scale habitat fragmentation experiment, where individuals of two perennial forb species were reintroduced into eight replicate ~50-ha landscapes, each containing a set of five ~1-ha patches that varied in their degree of isolation (connected by habitat corridors or unconnected) and edge-to-area ratio. Using data on individual growth, survival, reproductive output, and recruitment collected one to two years after reintroduction, we developed models to forecast population growth, then compared forecasts to observed population sizes, three and six years later. Both the type of patch (connected and unconnected) and identity of the landscape to which individuals were reintroduced had effects on forecasted population growth rates, but only variation associated with landscape identity was an accurate predictor of subsequently observed population growth rates. Models that did not include landscape identity had minimal forecasting ability, revealing the key importance of variation at this scale for accurate prediction. Of the five demographic rates used to model population dynamics, seed production was the most important source of forecast error in population growth rates. Our results point to the importance of accounting for landscape-scale variation in demographic models and demonstrate how such models might assist with prioritizing particular landscapes for species reintroduction projects.
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Affiliation(s)
- T Trevor Caughlin
- Department of Biological Sciences and Program in Ecology, Evolution, and Behavior, Boise State University, Boise, Idaho 83725 USA
| | - Ellen I Damschen
- Department of Integrative Biology, University of Wisconsin-Madison, 451 Birge Hall, 430 Lincoln Drive, Madison, Wisconsin, 53706, USA
| | - Nick M Haddad
- Kellogg Biological Station and Department of Integrative Biology, Michigan State University, Hickory Corners, Michigan, 49060, USA
| | - Douglas J Levey
- Division of Environmental Biology, National Science Foundation, Alexandria, Virginia, 22314, USA
| | - Christopher Warneke
- Department of Plant Biology and Program in Ecology, Evolutionary Biology, and Behavior, Michigan State University, East Lansing, Michigan 48824 USA
| | - Lars A Brudvig
- Department of Plant Biology and Program in Ecology, Evolutionary Biology, and Behavior, Michigan State University, East Lansing, Michigan 48824 USA
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14
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Lambert JPT, Hicks HL, Childs DZ, Freckleton RP, Gonzalez‐Andujar J. Evaluating the potential of Unmanned Aerial Systems for mapping weeds at field scales: a case study with Alopecurus myosuroides. WEED RESEARCH 2018; 58:35-45. [PMID: 29527066 PMCID: PMC5832304 DOI: 10.1111/wre.12275] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 08/25/2017] [Indexed: 05/30/2023]
Abstract
Mapping weed densities within crops has conventionally been achieved either by detailed ecological monitoring or by field walking, both of which are time-consuming and expensive. Recent advances have resulted in increased interest in using Unmanned Aerial Systems (UAS) to map fields, aiming to reduce labour costs and increase the spatial extent of coverage. However, adoption of this technology ideally requires that mapping can be undertaken automatically and without the need for extensive ground-truthing. This approach has not been validated at large scale using UAS-derived imagery in combination with extensive ground-truth data. We tested the capability of UAS for mapping a grass weed, Alopecurus myosuroides, in wheat crops. We addressed two questions: (i) can imagery accurately measure densities of weeds within fields and (ii) can aerial imagery of a field be used to estimate the densities of weeds based on statistical models developed in other locations? We recorded aerial imagery from 26 fields using a UAS. Images were generated using both RGB and Rmod (Rmod 670-750 nm) spectral bands. Ground-truth data on weed densities were collected simultaneously with the aerial imagery. We combined these data to produce statistical models that (i) correlated ground-truth weed densities with image intensity and (ii) forecast weed densities in other fields. We show that weed densities correlated with image intensity, particularly Rmod image data. However, results were mixed in terms of out of sample prediction from field-to-field. We highlight the difficulties with transferring models and we discuss the challenges for automated weed mapping using UAS technology.
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Affiliation(s)
- J P T Lambert
- Department of Animal & Plant ScienceUniversity of SheffieldSheffieldUK
| | - H L Hicks
- Department of Animal & Plant ScienceUniversity of SheffieldSheffieldUK
| | - D Z Childs
- Department of Animal & Plant ScienceUniversity of SheffieldSheffieldUK
| | - R P Freckleton
- Department of Animal & Plant ScienceUniversity of SheffieldSheffieldUK
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