Ecology of Threatened Semi-Arid Wetlands: Long-Term Research in Las Tablas de Daimiel

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The RDA relates each modeled temporal fluctuation pattern with a significant canonical axis. The R software generates linear combination lc score plots, which visually present the modeled temporal patterns of species groups that are associated with each canonical axis.

Hydrological and botanical man-made changes in the Spanish wetland of Las Tablas de Daimiel

Counting the number of significant canonical axes, the cross-scale aspect of community dynamics important for resilience can be quantified. All relevant steps in the analyses are carried out with two functions implemented in R 2. This function accounts for the connectivity of linear time steps, and a connectivity matrix, which needs to be calculated in spatial analysis, especially in hierarchical or dendritic designs [ 52 - 54 ], is therefore not necessary in time series analysis. The calculations are therefore based exclusively on an automatic statistical procedure, thereby avoiding potential researcher-induced bias in model construction.

The scale-specific relevance of taxon richness, i. We used Spearman rank correlation analyses, relating the raw biovolume data of individual phytoplankton taxa with the modeled species group patterns, to assess scale-specific taxon richness. We also evaluated the number of species with presumably stochastic dynamics that is, those that were not associated with any significant canonical axis by subtracting the sum of species that correlated with canonical axes from the total number of species used for time series modeling for each site PG, MM, PN and state wet, dry.

In and of itself, discerning between species that explain the dominant temporal frequencies from stochastic species in a system is critical, because it allows us to separate patterns of cyclic change from stochastic noise, providing a more refined view of the contribution of species richness to within and cross scale reinforcement of processes feedbacks and thus resilience. Clear seasonal patterns in flooded area, with high flooded area in spring and lower water levels in summer, autumn and winter, were discerned between and , showing the typical seasonally recurrent wet-dry phases in semiarid wetlands Figure 1b.

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However, two distinct hydrological periods were identified on a supraseasonal scale, based on threshold detection using the STARS algorithm, one associated with very high water levels in the floodplain from to wet state and a second with low water levels during a supraseasonal drought dry state starting approximately by mid Figure 1b. Based on community metrics richness, diversity, evenness and total biovolume , different temporal patterns were present in the wet and dry states Figure 2. Richness and diversity had different temporal patterns during both states, with higher between-year variability in the wet state higher in ; lower in compared to the dry state where fluctuation patterns were similar between both years of study Figure 2.

On average, richness and diversity were higher whilst evenness was lower during the dry compared to the wet state Figure 2 , highlighting a different phytoplankton community structure between states. A significant interaction term was found for richness, diversity and evenness metrics Table 1 , highlighting that sites differ in their average richness, diversity and evenness and in their temporal patterns during both states Figure 2.

Total biovolume had similar temporal patterns across sites and these were not significantly different between both states Table 1. Shown are degrees of freedom d. Significant terms are highlighted in bold. Assessing the temporal structure of phytoplankton in both states using time series modeling, we found contrasting patterns of scale-specific variability across the sites.

During the wet state, the temporal dynamics of phytoplankton at the MM and PN sites showed diverse patterns of temporal variability, manifested in 5 MM and 3 PN distinct frequency patterns during the month period. This identified groups of species within the phytoplankton community that had fluctuation patterns at distinct temporal scales Figure 3. These scales usually covered between-year variability at canonical axes 1 and 2 RDA 1 and RDA 2, Figure 3 that indicate slower temporal dynamics at these scales, and elements of faster change reflected by a stronger component of within-year variability at the remaining axes during the months period Figure 3.

No clear patterns in the number of species contributing to these patterns i. No comparisons could be made for the other canonical axes because these were not significant in the models of the dry state see below; Table 2. The explanatory power of each axis adjusted R 2 is given in parentheses. Values in parentheses show percentages from that total. Values are given only for RDA axes that were significant see Figure 3. Time series modeling also revealed different patterns of temporal structure between both states Figure 3.

All sites had temporal fluctuation patterns at two temporal scales in the dry state. For the MM and PN sites this means a decrease in temporal diversity patterns in the dry relative to the wet state i. Slow temporal patterns, reflecting mainly between-year variability characterized the dominant phytoplankton dynamics across site, while shorter-term fluctuations within-year variability were less important in the dry relative to the wet period.

This is also reflected in the individual AEM variables retained in the RDA models after forward selection; that is, variables indicating faster patterns of change e. Differences between the wet and dry states were also evident in terms of the strength of the reduced RDA models that provide insight into the relative importance of deterministic vs stochastic processes mediating community assembly. In the dry state the strength of models decreased in MM 0. This trend was also reflected in the percentage of species that did not correlate significantly with the temporal frequency patterns identified in MM and PN Table 2.

Alternative states theory posits that the ecological structure, functions and processes differ between states e. Early warning statistics have been developed in recent years to assess when the resilience of one state erodes and a shift to another state occurs [ 56 - 59 ]. However, an estimation of the resilience of alternative states that emerges from the structure, functions and processes that define these states has remained elusive and methodologically challenging. We used time series modeling to reveal patterns of fluctuation frequencies of phytoplankton at independent temporal scales in contrasting wet-dry states of a semiarid floodplain.

In turn, the patterns of within-scale species richness compartmentalized by scale and cross-scale temporal scales of fluctuations patterns structure identified allowed for an assessment of critical components of resilience and provided insight into the dynamic structure of phytoplankton communities in these states.

Although our approach allowed us to study causal effects of non-linear hydrological patterns on phytoplankton communities that are relevant for understanding ecohydrological processes in wetlands [ 60 , 61 ], our approach limited an assessment of non-linear processes triggering phytoplankton changes between the wet and dry state. Notwithstanding, our approach shows how community dynamics can be inferred under contrasting ecological conditions and is therefore also suitable for assessing the relative resilience of alternative states in ecosystems and other complex systems.

Given the strong impacts of hydrological disturbance on floodplain communities [ 62 ], it may not be surprising that phytoplankton dynamics differed between wet and dry states, thereby supporting also our hypothesis. We found that RDA models for the dry state did not select AEM variables indicating short-term fluctuations, suggesting that slower dynamics increase in relative importance in the dry relative to the wet state. Because the time series models were constructed from 24 time steps months for both states the differences observed are not confounded by unequal sampling frequencies and lengths of study periods.

Jiang et al. Although our observational study precludes an assessment of biological interaction as the mechanism shaping the fluctuation patterns, abiotic factors cannot be ruled out. In the absence of flood pulsing, droughts have been considered as a disturbance that slowly increases in magnitude over time i.

Thus, the slowed dynamics during drought might arise from complex community responses to slow external factors e. Regarding resilience, model analysis has shown that slower dynamics can have a stabilizing effect of transitional dynamics and retard the return to stable alternative states following disturbance [ 64 ]. While this suggests that slow dynamics can increase resilience, we acknowledge that resilience is characterized by many attributes [ 41 ], and our study shows that several of these attributes of resilience should be assessed simultaneously to increase inference.

Our time series modeling approach not only allowed assessing dominant fluctuation patterns of phytoplankton but also how patterns were compartmentalized by scale, a critical attribute of resilience [ 11 ]. In turn, this helped us evaluate the role of species richness mediating resilience. Increased resilience has been associated with a higher species richness and diversity in communities [ 19 , 20 ]. Species richness and diversity were higher during the dry compared to the wet state in this study [ 40 ]. However, our time series models suggest that resilience in the dry state may not have been necessarily higher.

While the within-scale aspect of resilience did not show any clear differences between the wet and the dry state because the number of species at each scale was similar across the models, we found clear differences in the cross-scale structure. The number of dominant fluctuation patterns of phytoplankton or temporal scales was lower in the dry relative to the wet state. Theory and empirical studies have related resilience to the number of scales present in a system [ 12 , 65 , 66 ], assuming that resilience is increased with a higher cross-scale structure, thereby contributing to strengthen feedbacks through a stronger reinforcement of processes across scales.

Because the number of temporal scales was reduced during the drought relative to the wet state, we can interpret this as a decreased cross-scale reinforcement of dynamics and thus lower resilience of phytoplankton communities in the dry state. Not only is this decreased resilience in agreement with the interpretation that drought comprises a perturbation for ecological communities [ 26 , 27 ], it also highlights a paradox: increased species richness may not necessarily increase resilience through a cross-scale reinforcement of patterns.

This paradox can be further scrutinized with the number of species with stochastic dynamics identified by the time series models. The number of species with apparently stochastic dynamics was on average higher in the dry compared to the wet state. Although inference is limited in our study because we could only compare a few sites, our results are consistent with other studies that have shown an increased component of stochastic community assembly during drought [ 26 , 27 ].

Also, the lower explanatory power of some statistical models of the dry state, highlighting increased stochasticity, is in agreement with this interpretation. If resilience is apparently reduced in the dry state, which role do species with stochastic patterns play in alternative states? With the exception of a single study within an engineering resilience context [ 67 ], the role of stochastic processes in resilience research has not been explored.

Stochastic patterns may be found in some rare taxa with low abundances.

Salvador Sánchez-Carrillo - Google Scholar Citations

Numerically rare species may have a significant role as they may increase in abundance following disturbance and thereby sustain important functions when dominant species are removed or novel conditions introduced [ 68 ]. However, the importance of rare species is easy to overlook, because the strength of ecological patterns is related to more dominant species. In time series models, the within and cross scale patterns identified are comprised of the temporal dynamics of these dominant species.

However, when environmental conditions change, i. Thus, species with stochastic dynamics may not contribute to resilience per se in a specific state by means of a within and cross scale reinforcement of patterns but rather provide another critical component that influences the capacity of reorganization i. Because our aims were to assess within and cross-scale structures mediating resilience a quantification of the adaptive capacity facet was beyond the scope of this study.

However, our study makes clear how the role of species richness can be scrutinized if partitioned into patterns that reflect both the dynamic system structure at different scales and random noise. These patterns can be further explored for gaining a more process-based understanding of the role of species when ecosystems and communities re-organize in alternative states. We conclude by highlighting the implications of our results for assessing resilience of ecological systems. The distribution and redundancies of functional attributes of species within and between scales or the capacity of organisms within functional groups to respond to disturbance response diversity critically mediate the overall resilience of ecological systems [ 11 , 12 , 69 ].

Assessing these functional distributions will require a sound estimate of the underlying scale-specific structure related to species distributions. The use of multivariate time series modelling is straightforward because it makes rates of environmental change at distinct temporal scales tractable, making possible inference regarding the relative resilience of ecological systems from a dynamic perspective. There is concern that current rates of anthropogenic impact will increase the incidence and frequency of regime shifts in ecological and combined social-ecological systems, with many of the new states providing fewer goods and services to humanity [ 19 , 37 ].

Using phytoplankton community dynamics in wet and dry states of a floodplain, our results show that the dynamic system structure necessary for understanding resilience can differ substantially between states. This highlights the usefulness of time series modeling to infer the relative resilience of alternative states across ecological systems and other complex systems with known histories of regime shifts when adequate time series data are available for analysis.

The authors are grateful for the constructive criticism received by two anonymous reviewers that helped improve the paper. The authors acknowledge financial support from the August T. Reference to trade names does not imply endorsement by the authors or the U. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

National Center for Biotechnology Information , U. PLoS One. Published online Oct David G. Craig R. Valentine, Editor. Author information Article notes Copyright and License information Disclaimer. Competing Interests: The authors have declared that no competing interests exist. Received Jul 4; Accepted Aug Copyright notice. This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.

This article has been cited by other articles in PMC. Appendix S2: Flow chart outlining the steps involved in time series modeling. Application of spectroscopial, hyperspectral and multispectral data to study wetlands in semi-arid environments Central Spain. Modeling Flash Flood Hazards in an arid region by remote sensing.

Abstracts — part 2, Session , Paper 5, p. Application of hyperspectral data to study saline wetland areas in semiarid environments. Synergetic use of multispectral and hyperspectral data in characterizing changes in semiarid wetlands in Spain. DiBlasi, M. Monitoring of saline soils within wetland areas under semiarid conditions applying hyperspectral and multispectral data. Wavelet-based de-noising of derivative spectra. Semi-arid wetland spectral library and its application to study wetland and soil degradation with hyperspectral and multispectral data.

Synergistic use of hyperspectral and multispectral data for identification and monitoring of salinity in Mediterranean environments. The study of anthropogenic affected wetlands in semi-arid environments applying airborne hyperspectral data. In: J. Rosell Urrutia and J. Universitat de Lleida — Editorial Milenio, pp. Synergetic use of hyperspectral and multispectral data for the study of semi-arid wetland degradation in Spain.

Application of ALOS in arid land studies: land degradation, natural hazards and water resources. Flash flood hazard mapping in Oman using GIS. Spectral and textural classification of multi-source imagery to identify soil degradation stages in semi-arid environments. Spectral and textural classification of active wadi systems in arid lands by Landsat TM and Radarsat data. A methodology to identify soil degradation stages using remote sensing in semi-arid environments.

Characterizing active wadi channels in arid lands by linear mixture modeling. Identifying geo-indicators of land degradation in a semi-arid Mediterranean environment using remote sensing. Extension of the technique of principal components analysis for use in end-member selection. Characterising soils as land degradation indicators in a semi-arid Mediterranean area using a spectral unmixing methodology. Mapping the distribution of evaporitic soils in Los Monegros using spectral unmixing methods. The use of linear mixture modelling in defining the spatial extent of landscape components in a semi-arid area.

Factor and vector analysis approaches to the estimation of mixture proportions. Lithological mapping in semi-arid regions. II, Evaluation of combined spectral and textural information for lithological discrimination. Abuelgasim, A. Abstract, p. I, Abstract, Use of satellite images to map the oil lakes in Kuwait.

Use of space photographs in deciphering the relationship between fractures and drainage in the Khor Quwab-Ashat area of eastern Sudan. Hydrologic modeling of the tomb of Nefertari, Luxor, Egypt. Abstracts, Vol. Carbonell, M. Call number. Holocene 1.

Burning issue of Spain's wetlands - 5 Nov 09

Organization as author. Geological Survey U. Environmental Protection Agency 10 United States. Natural Resources Conservation Service 9 U. Fish and Wildlife Service. Region 8. Office of Biological Services 4 United States.

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Sort by relevance relevance new to the Libraries year new to old year old to new author title. Ecology and bio-diversity of the Tanguar Haor : a unique wetland of Bangladesh []. Description Book — xii, p. B35 I75 Unknown. Coastal wetlands []. Cham, Switzerland : Springer, [] Description Book — 1 online resource. Summary The Everglades: An overview of alteration and restoration.

Spain Bay of Biscay. The alteration, and subsequent restoration, of wetland habitats remain key issues among coastal scientists. These topics are introduced through case studies and pilot programs that are designed to better understand the best practices of trying to save what is left of these fragile ecosystems.

Local approaches, as well as national and international efforts to restore the functionality of marsh systems are summarily approached and evaluated by their efficacy in producing resilient reclamations in terms of climate-smart habitat conservation. The outlook of this work is global in extent and local by intent. Included here in summarized form are professional opinions of experts in the field that investigate the crux of the matter, which proves to be human pressure on coastal wetland environments.

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Even though conservation and preservation of these delicate environmental systems may be coming at a later date, many multi-pronged approaches show promise through advances in education, litigation, and engineering to achieve sustainable coastal systems. The examples in this book are not only of interest to those working exclusively with coastal wetlands, but also to those working to protect the surrounding coastal areas of all types.

Coastal wetlands : alteration and remediation []. Cham, Switzerland : Springer, Description Book — 1 online resource pages : illustrations, tables. Ecology of freshwater and estuarine wetlands []. Second edition. Prominent scholars help students understand both general concepts of different wetland types as well as complex topics related to these dynamic physical environments. Careful syntheses review wetland soils, hydrology, and geomorphology; abiotic constraints for wetland plants and animals; microbial ecology and biogeochemistry; development of wetland plant communities; wetland animal ecology; and carbon dynamics and ecosystem processes.

In addition, contributors document wetland regulation, policy, and assessment in the US and provide a clear roadmap for adaptive management and restoration of wetlands. New material also includes an expanded review of the consequences for wetlands in a changing global environment. Ideally suited for wetlands ecology courses, Ecology of Freshwater and Estuarine Wetlands, Second Edition, includes updated content, enhanced images many in color , and innovative pedagogical elements that guide students and interested readers through the current state of our wetlands.

M3 E Unknown. Berkeley, Calif. Description Book — xiii, p. Summary List of Contributors Preface 1. Why Are Wetlands Important? Prominent wetland scholars address the physical environment, geomorphology, biogeochemistry, soils, and hydrology of both freshwater and estuarine wetlands.

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Ideal for the classroom, this book is a fundamental resource for anyone interested in the current state of our wetlands. M3 E Available. Mires -- swamp, bog, fen, and moor []. Description Book — 2 v.