Spatial and Temporal Patterns in Macrofaunal Diversity Components Relative to Sea Floor Landscape Structure

Zajac, Roman N.; Vozarik, Joseph M.; Gibbons, Brittney R.

doi:10.1371/journal.pone.0065823

Cited by 43 publications

(26 citation statements)

References 55 publications

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“…DFA is a multivariate time-series analysis technique to estimate common trends, to study the interactions between response variables and to determine the effects of explanatory variables in a time series data set (Zuur and Pierce, 2004;Zuur et al, 2003a). The underlying DFA model is given by (Zuur and Pierce, 2004):…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…(2) translates into Eq. (3) (Kisekka et al, 2013;Kuo et al, 2011;Ritter and Muñoz-Carpena, 2006;Zuur and Pierce, 2004;Zuur et al, 2007):…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…C n is a constant level parameter as in linear regression model which increases or decreases the linear combination of common trends (Kuo and Lin, 2010). If the time series are standardized, the constant parameters are 0 (Zuur and Pierce, 2004).…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…Benthic communities may fluctuate over time because of characteristics of the species' life cycles and/or in response to environmental variability (Convey, 1996;Sibly and Calow, 1989). In this context, assessing temporal patterns of benthic community development and their underlying drivers is critical for understanding the ecology of diverse marine ecosystems (Robinson and Sandgren, 1983;Zajac et al, 2013). In fact, understanding patterns of change in benthic fauna through the monitoring of communities (e.g., community structure and composition, species richness) might allow for separating effects of climate variability and anthropogenic disturbance on diversity and the functioning of the marine benthic ecosystem (Munari, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…It also quantifies the canonical correlation between temporal trends and macrofaunal abundance time series (Nye et al, 2010). DFA is used to identify underlying common trends among multivariate time series while taking the effects of explanatory variables into account (Kuo and Lin, 2010;Zuur and Pierce, 2004). Here, we focus on a 30 year time series of benthic macrofaunal abundance and environmental variables in the German Bight.…”

Section: Introductionmentioning

confidence: 99%

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Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Shojaei

Gutow

Dannheim

et al. 2016

Journal of Sea Research

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We examined long-term variability in the abundance of German Bight soft bottom macro-zoobenthos together with major environmental factors (sea surface temperature, winter NAO index, salinity, phosphate, nitrate and silicate) using one of the most comprehensive ecological long-term data sets in the North Sea . Two techniques, Min/Max Autocorrelation Factor Analysis (MAFA) and Dynamic Factor Analysis (DFA) were used to identify underlying common trends in the macrofaunal time series and the relationships between this series and environmental variables. These methods are particularly suitable for relatively short (N15-25 years), non-stationary multivariate data series. Both MAFA and DFA identify a common trend in German Bight macrofaunal abundance i.e. a slight decrease (1981-mid-1990s) followed by a sharp trough in the late 1990s. Subsequently, scores increased again towards 2011. Our analysis indicates that winter temperature and North Atlantic Oscillation were the predominant environmental drivers of temporal variation in German Bight macrofaunal abundance. The techniques applied here are suitable tools to describe temporal fluctuations in complex and noisy multiple time series data and can detect distinct shifts and trends within such time series.

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Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…(2) translates into Eq. (3) (Kisekka et al, 2013;Kuo et al, 2011;Ritter and Muñoz-Carpena, 2006;Zuur and Pierce, 2004;Zuur et al, 2007):…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Shojaei

Gutow

Dannheim

et al. 2016

Journal of Sea Research

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Towards time and cost‐efficient habitat assessment: Challenges and opportunities for benthic ecology and management

Schenone,

Azhar,

Delmas

et al. 2023

Aquatic Conservation

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To the untrained eye, soft‐sediment habitats are often viewed as vast expanses of featureless sand and mud. With new technology, it is finally possible to begin to map the ecological heterogeneity of these ecosystems to reveal their importance for ecosystem service provision and open up new opportunities to manage their use and protect them. When compared with terrestrial or marine vegetated habitats, the characterization of unvegetated soft sediments still lacks rapid and cost‐effective practices that allow the direct collection of ecological data at multiple spatial and temporal scales facilitating direct relevance to conservation and management. New technologies that allow for the building of maps that incorporate multiple scales of heterogeneity also provide new ways of upscaling necessarily detailed and small‐scale process studies. The study of the relationship between benthic structure, biodiversity and ecosystem functioning can benefit from a range of established and novel techniques and creates new opportunities to empirically test generalities and predictions based on theory or small‐scale experimental studies. The capacity to map ecological changes rapidly and at scale and to link small‐scale experiments to landscapes uncovers new insights for seafloor conservation including rapid response to disturbance events, broad‐scale ecologically meaningful mapping and assessments of biodiversity–ecosystem function and biodiversity–ecosystem services relationships. As technology advances and becomes more accessible to all, it offers promising new avenues of research to expand our current capabilities, and the adoption of nested approaches, with a synergy of different samplings simultaneously covering several scales and resolutions, is likely to have the most benefit, supporting decision‐making with ecologically insightful data, as well as the timely assessment of the outcomes of conservation actions.

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Species–area relationships and additive partitioning of diversity of native and nonnative herpetofauna of the West Indies

Gao

Perry

2016

Ecology and Evolution

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To evaluate the regional biogeographical patterns of West Indian native and nonnative herpetofauna, we derived and updated data on the presence/absence of all herpetofauna in this region from the recently published reviews. We divided the records into 24 taxonomic groups and classified each species as native or nonnative at each locality. For each taxonomic group and in aggregate, we then assessed the following: (1) multiple species–area relationship (SAR) models; (2) C‐ and Z‐values, typically interpreted to represent insularity or dispersal ability; and (3) the average diversity of islands, among‐island heterogeneity, γ‐diversity, and the contribution of area effect toward explaining among‐island heterogeneity using additive diversity partitioning approach. We found the following: (1) SARs were best modeled using the Cumulative Weibull and Lomolino relationships; (2) the Cumulative Weibull and Lomolino regressions displayed both convex and sigmoid curves; and (3) the Cumulative Weibull regressions were more conservative than Lomolino at displaying sigmoid curves within the range of island size studied. The Z‐value of all herpetofauna was overestimated by Darlington (Zoogeography: The geographic distribution of animals, John Wiley, New York, 1957), and Z‐values were ranked: (1) native > nonnative; (2) reptiles > amphibians; (3) snake > lizard > frog > turtle > crocodilian; and (4) increased from lower‐ to higher‐level taxonomic groups. Additive diversity partitioning showed that area had a weaker effect on explaining the among‐island heterogeneity for nonnative species than for native species. Our findings imply that the flexibility of Cumulative Weibull and Lomolino has been underappreciated in the literature. Z‐value is an average of different slopes from different scales and could be artificially overestimated due to oversampling islands of intermediate to large size. Lower extinction rate, higher colonization, and more in situ speciation could contribute to high richness of native species on large islands, enlarging area effect on explaining the between‐island heterogeneity for native species, whereas economic isolation on large islands could decrease the predicted richness, lowering the area effect for nonnative species. For most of the small islands less affected by human activities, extinction and dispersal limitation are the primary processes producing low species richness pattern, which decreases the overall average diversity with a large among‐island heterogeneity corresponding to the high value of this region as a biodiversity hotspot.

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Spatial and Temporal Patterns in Macrofaunal Diversity Components Relative to Sea Floor Landscape Structure

Cited by 43 publications

References 55 publications

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Towards time and cost‐efficient habitat assessment: Challenges and opportunities for benthic ecology and management

Species–area relationships and additive partitioning of diversity of native and nonnative herpetofauna of the West Indies

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