Multi-scale data on intertidal macrobenthic biodiversity and environmental features in three New Zealand harbours

Kraan, Casper; Greenfield, Barry L.; Thrush, Simon F.

doi:10.5194/essd-2019-151

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…Macrobenthic fauna and associated environmental variables were sampled during austral summer 2012 in Kaipara, Manukau, and Tauranga harbours, North Island, New Zealand (Kraan, Greenfield, et al, 2020) (Figure 1). Full details on data collection, curation and processing methods are available in Kraan, Greenfield, et al (2020).…”

Section: Methodsmentioning

confidence: 99%

Inclusion of biotic variables improves predictions of environmental niche models

Stephenson

Gladstone‐Gallagher

Bulmer

et al. 2022

Diversity and Distributions

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Aim Species Distribution Models (SDMs) are correlative models that predict the occurrence or abundance of species in relation to predictor variables. SDMs have become an important part of resource management and conservation biology yet they rarely incorporate species’ biology or demography into their predictions. To explore the possible influence of biotic relationships in explaining patterns of species’ distribution, abundance and explanatory power of SDMs, we chose two intertidal shellfish species with overlapping but different environmental preferences (Austrovenus stutchburyi and Macomona liliana) and modelled their distributions with and without biotic variables. Location New Zealand. Methods The relationship between environmental and biotic variables on the abundance of our two species was investigated using Boosted Regression Trees (BRTs) with increasing model complexity: (1) BRT models using environmental variables were fitted to each species; (2) BRT models using environmental variables and the co‐occurring abundance of the study taxa not being modelled were fitted; (3) BRT models using environmental variables, the co‐occurring abundance and the estimated abundance of the species’ patch of the study taxa not being modelled were fitted. Results A strong, non‐linear effect of the abundance of Austrovenus on Macomona was observed but only a weak effect of Macomona on Austrovenus. The inclusion of biotic variables improved the model fit metrics for both species, as assessed by withheld evaluation data, markedly so for Macomona. The overall deviance explained by the models increased, the correlation of predicted vs observed abundance data increased and the variability in these measures decreased. Main conclusions The combination of the improvement in model performance and changes in the influence of variables with the inclusion of biotic variables is of importance when predicting into unsampled space (e.g. when predicting impacts of climate change). Our approach improves classic SDMs by integrating ecological theories of how species interactions can alter species distributions across environmental gradients.

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Section: Methodsmentioning

confidence: 99%

Inclusion of biotic variables improves predictions of environmental niche models

Stephenson

Gladstone‐Gallagher

Bulmer

et al. 2022

Diversity and Distributions

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“…Mean sediment grain size and grain size fractions (silt < 63 μm, very fine 63–125 μm, fine 125–250 μm, medium 250–500 μm and coarse > 500 μm), as well as organic content (%), chlorophyll‐a and phaeopigments (mg g −1 ) were estimated from a pool of three surface sediment cores (2 cm diam., 2 cm deep) at each sampling point. Grain size were measured using a Malvern Mastersizer, pigment concentrations were determined using a fluorometer, and loss on ignition was used to assess organic content (Kraan et al 2015, 2020a). Missing data from the environmental factors (consisting of 2.4% of the data from the seagrass, bare sand and shell hash coverage) were estimated using the predictive mean matching method in the R package ‘mice' (van Buuren and Groothuis‐Oudshoorn 2011).…”

Section: Methodsmentioning

confidence: 99%

“…The dataset consists of a total of 1197 sediment and macrofauna samples from 3 seascapes, containing 146 macroinvertebrate taxa and 13 environmental variables (all data available via Kraan et al 2019Kraan et al , 2020a. Samples were collected from three intertidal sandflats in Kaipara (174°17′S, 36°23′E), Manukau (174°41′S, 37°7′E) and Tauranga (175°56′S, 37°27′E), North Island, New Zealand (Fig.…”

Section: Datasetmentioning

confidence: 99%

“…The sampling design was used to allow comparisons across different spatial scales, in which sampling points were spaced along three 1 km long transects repeating a sequences of inter-sample distances of 30 cm, 1, 5, 10, 30, 50, 100, 500 and 1000 m. The macrofauna cores were sieved on a 500 µm mesh sieve and macrofauna preserved in 70% isopropyl alcohol for later taxonomic identification (usually to species level). The percentage of seagrass Zostera mulleri, bare sand and shell hash cover was estimated through photographs (0.25 m 2 ) of each sampling point (Kraan et al 2015(Kraan et al , 2020a for detailed information). Mean sediment grain size and grain size fractions (silt < 63 μm, very fine 63-125 μm, fine 125-250 μm, medium 250-500 μm and coarse > 500 μm), as well as organic content (%), chlorophyll-a and phaeopigments (mg g −1 ) were estimated from a pool of three surface sediment cores (2 cm diam., 2 cm deep) at each sampling point.…”

Section: Datasetmentioning

confidence: 99%

“…Data available at Pangea Digital Repository <https://doi. org/10.1594/PANGAEA.903448> (Kraan et al 2019(Kraan et al , 2020a.…”

Section: Data Availability Statementmentioning

confidence: 99%

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Coupled effects of environment, space and ecological engineering on seafloor beta‐diversity

et al. 2021

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One of the challenges in modern ecology is reconciling how biotic interactions and abiotic factors are interwoven drivers of community assembly. β-diversity is a measure of the variation in species composition across space and time. The drivers of variability in β-diversity are thus responsible for dictating the trajectory of assembling communities. With data from multiscale surveys conducted in three marine intertidal sandflats, we aimed to determine the relative importance and interdependencies of biotic engineering, environment and spatial distances for β-diversity. In each sandflat, macrofauna and environmental properties were assessed in 400 samples collected at different spatial distances over 300 000 m 2 . The role of environmental variability in driving patterns in β-diversity and species turnover was dependent on the abundances of ecosystem engineers and spatial connectivity among seascapes as shown by the variance explained by the interaction terms in the variance partitioning. Our results highlight the interdependence between space, environment and species interactions in driving community assembly. Given that most models aiming to explain β-diversity variation only consider abiotic factors, our findings call for the incorporation of biotic interactions into these models and we argue that this is essential for understanding resilience to environmental change. The degree of interdependence between environment, space and biotic interactions in driving ecological assembly is pivotal to our understanding of how biodiversity will respond to predicted changes in habitats, environment and key species distributions.

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Multi-scale data on intertidal macrobenthic biodiversity and environmental features in three New Zealand harbours

Cited by 2 publications

References 12 publications

Inclusion of biotic variables improves predictions of environmental niche models

Inclusion of biotic variables improves predictions of environmental niche models

Coupled effects of environment, space and ecological engineering on seafloor beta‐diversity

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