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The data for this study were collected in the austral summer of 2016, on board the BAS research ship RRS James Clark Ross38. Biological abundance data was taken from photographic images from Brasier et al.10 (see methodology within). The images were taken with a Shallow Underwater Camera System (SUCS), in transects of 10 photographs, 10 m apart. Replicant transects were separated by 100 m, at water depths of 500 m, 750 m and 1000 m10. Each photograph in the analysis was 0.51 m2 in area. In the analyses, ‘VME unknown’ was biological material unidentifiable to phylum, but distinguishable as VME taxa, e.g., branched or budding fragments which could be bryozoan or cnidarian species. The percentage cover of encrusting species was recorded by Brasier et al.10, as the number of individual colonies was not always possible to distinguish between colonising taxa10. Physical variables, such as substrate texture, were also observed from the SUCS images10.
Taxonomic resolution of the data originally collected was dependent upon the ability to distinguish the organism in the images10. For our analysis, the taxonomic hierarchy analysed related to the abundance level of that group, as lower abundance, finer scale taxonomic groupings would be zero-inflated so not able to be used within our methodology (cf. Milns et al.39). Taxonomic groupings included were Annelida, Arthropoda, Cnidaria, Echinodermata, Mollusca, Bryozoa, Actinopteri, Porifera and Echinodermata. The echinoderm taxa Ophiuroides and Euryalids also occurred in large enough numbers to be analysed as separate groups. The taxa in each of these groupings can be seen in the Brasier et al.10
The raw data (taxon ID from photographs) was highly zero-inflated (84.9% of entries), so data grouping was performed to capture the fine-scale (individual photographs at ∼cm scale) and large-scale (replicate transects at ∼km scale) ecology of the ecosystems. For the fine-scale network there were 527 samples (abundances from individual photographs) and twelve nodes. Four were physical nodes (Depth, Region, Substrate and Substrate Texture, Table 1), two were functions of the specimens present (percent encrusting), five were taxa classified to Phylum level (Arthopoda, Bryozoa, Porifera, Cnidaria and Echinodermata) and there was one bin-group (VME Unknown) Supplementary Table 2.
Table 1 Table of network properties for the two networks found. Chains are defined as series of dependencies containing more than two nodes. Link density is mean number of connections per node, connectance is the number of connections/number of nodes*(number of nodes-1).
Full size table

For the large-scale network, the data from all the photographs in each event (up to three replicate transects) was combined to form 21 samples with 16 nodes. For factor nodes (Region, Texture and Substrate) the modal variable was taken, taxa nodes were summed, and the mean was used for Percent Encrusting and Depth. Texture was excluded from analyses due to the high zero-count. Two nodes were functions of the specimens present (percent encrusting), ten taxa were analysed (Annelid, Arthropod, Cnidarian, Echinoderm, Mollusc, Bryozoan, Actinopteri, Ophiuroidea, Euryalida, Porifera) and one bin-group (VME Unknown) was used. The combination of the two networks enabled the investigation of both physical and biotic interactions across multiple spatial scales.
We chose these relatively coarse taxonomic groupings in order to maximise the statistical power of our analyses. This statistical power enabled us to reconstruct a complex network of dependencies, thus revealing the subtle relationships between different taxa. The coarse grouping is a limitation of this method, because the homogenisation of some diverse groups, such as echinoderms, groups together organisms with variable life modes and traits (and therefore differing relationships with other taxa). However, the coarse level of identification was necessary with this volume of data (over 500 photographs), and the patchiness and rarity of many taxa. Future studies involving a higher density of photographs will allow for genus or species level separation, and/or an analysis based upon functional traits classification.
Analysis
One approach to understanding how ecosystems function is to consider the ecosystem as a network (cf. Miln et al.38). Different taxa or groups of taxa are considered ‘nodes’ and their interactions are described as ‘connections’ (more usually known as ‘edges’ in Baysian networks), which link interacting taxa together. A node that the connections feed into are called ‘parents’. Work has centred on gene regulatory networks17, neural information flow networks, and with more recent applications to ecological and palaeoecological networks18,39,40,41. It is important to note that the structure produced by the BNIA reflects the associations caused by co-localisations (two taxa which both have a high abundance), not by a specific interaction, for example predation. By using BNIA, direct dependencies between taxa can be detected, minimising auto-correlation between two nodes. For example, if A depends on B which depends on C, there could be a correlation between A and C. However, this correlation would not represent an interaction or association between A and C, merely the two correlations between A and B and B and C. BNIA enables only the realised dependencies to be found, ensuring only actual interactions and associations are found.
Bayesian network inference was performed in Banjo16, The BNIA software used was Banjo v2.0.0, a publicly available Java based algorithm39,41. Banjo uses uniform priors, so boundaries for the different discretized groups were chosen to ensure even splitting between the groups. Discretized data were input into Banjo, which then generated a random network based on the input variables. A greedy search was then performed to find a more likely network than the random one generated. This search was repeated 10 million times for each set of input data and the most probable network was then output. The maximum number of parents was set to 3 to limit artefacts19.
The BNIA used require discrete data, which ensures data noise is masked, and only the relative densities of each taxon are important39. We split the data into three intervals; zero counts, low counts and high counts. Low counts consisted of counts below the median for the taxa group and high counts were counts over the median. Medians were used over means because for some groups the high counts were very high, and would result in a very small number of samples grouped in the highest interval (cf. Milns et al.39). A large amount of bins maintains the amount of information present in the dataset, while fewer bins provide more statistical power, and greater noise masking. Yu19 has shown that for ecological data sets three different bins is a good balance. Zero was treated as a separate entity because the presence of one individual is very different to a zero presence, in contrast to zero gene expression, for example. Data preparation for Banjo (grouping and discretization) was carried out in R42, as was the statistical analysis of the data. Further analysis of banjo outputs, when required, used the functional language Haskell43. The scripts are available on Github (github.com/egmitchell/bootstrap).
To minimise outliers bias, 100 samples were bootstrapped at 95% level by randomly selecting 95% of the total number of samples for each analysis20,43, and then finding the subsample network using Banjo. For each connection calculated, the probability of occurrence was calculated, and the resultant distributions analysed to find the number of Gaussian sub-distributions using normal mixture models44. This probability distribution was bimodal for each data set, which suggests that there were two distributions of connections, those with low probability of occurrence, and those with highly probable connections. The final network for each area was taken to be those connections which were highly probable. The threshold for being labelled ‘highly probable’ depended on the network (as determined by the normal mixture modelling analyses): 53% for fine-scale network and 51% for the large-scale network. The magnitude of the occurrence rate is indicated in the network by the width of the line depicting the connection.
The direction of the connection between nodes indicates which node (taxon) has a dependency on the other node (taxon). For each connection, the directionality was taken to be the direction that  occurred in the majority of bootstrapped networks. Where there was no majority (directional connections have a probability between 0.4 and 0.6), the connection was said to have bi-directionality, or indicated a mutual dependency.
The IS can be used to gauge the type and strength of the interaction between two nodes. If the IS = 1, this corresponds with a positive correlation. When node 1 is high, node 2 will be high. An IS of −1 corresponds to a negative correlation: High node 1 corresponds to a low node 2. An IS = 0 does not mean there is no correlation between the two nodes. IS = 0 means that the interaction is non-monotonic. Sometimes node 1 will be positively correlated with node 2, sometimes negatively. The mean IS for each connection was calculated for each sample area.
Contingency test filtering
In order to avoid Type I errors introduced by high zero counts, which is common in ecological data sets, we excluded rare taxa, which were found in under 33% of the grid cells. Note, that this method of exclusion could potentially mean that a taxon with high abundance in a very limited area is excluded from analyses. To further guard against Type I errors, we also used a method of contingency test filtering that removed from consideration a connection between two variables whose joint distribution showed no evidence of deviation from the distribution expected from their combined marginal distributions (chi-squared tests, p  > 0.25)39. This threshold was used to ensure no chance of removing truly dependent dependencies, so that only artefacts such as those found between high zero counts were removed from consideration. These links were provided to the BNIA to exclude from consideration.
Inference
Inferring how one node (taxa or physical variable) is likely to change given another node being in a given state is done by calculating the probability of node A being in a given state given node B is in a given state. All nodes that have dependencies between A and B and so are included in the calculation:

$${mathrm{P}}left( {{mathrm{A|B}}} right) = mathop {sum}limits_{n = 1}^{n = N – 1} {frac{{mathop {prod }nolimits_{s = 1}^{s = S} {mathrm{P}}({mathrm{B}}_n|A_{n + 1})}}{{mathop {sum}nolimits_{m = 1}^N {P(B|A_m)P(A_m)} }}}$$

The N are the total number of nodes in the chain and n and m are the indices for the chain of nodes of length N connecting the first and end nodes. S are the number of discrete states for each node, which are indexed s. In order to infer the likely change of one taxon’s (A) abundance on another’s abundance (B), the probabilities of all taxa that occur in the network between A and B have to be taken into account. For example, for the probability that B is in a Zero abundance state given A is in a high abundance state, and given that B are connected through a dependency of B on C and C on A, the probability is calculated as follows: the probabilities of C existing in all states is calculated given a High abundance state for A, and then the probabilities for B existing in a Zero state is calculated for each state of C and then summed together to give the probability of B in Zero given A in high. To work out the inferred change in B given a change in A from High to Zero would involve taking the difference between the probabilities for each state of B given A is high with each state of B given A is low. For the code used to generate these inferred probabilities, please see ref. 45.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Our results show that a species’ distribution—measured as change in mean center of biomass and change in northern and southern range extent—depends on the species relationship with oceanographic versus static environmental variables. In the fall, species whose distributions are most influenced by bottom salinity have shifted mean centroids of biomass significantly more than species whose distributions are most influenced by depth or substrate (nonparametric pairwise Wilcoxon test p = 0.00007 and p = 0.0013, Fig. 1a). Species whose distributions are most influenced by bottom temperature have shifted mean centroids of biomass significantly more than species whose distributions are most influenced by depth (nonparametric pairwise Wilcoxon test p = 0.0083, Fig. 1a) and shifted the northern and southern extent of their biomass weighted ranges significantly more than species whose distributions are most influenced by depth and substrate (nonparametric pairwise Wilcoxon test p = 0.00018 and p = 0.014, Fig. 1c and nonparametric pairwise Wilcoxon test p = 0.0038 and p = 0.024, Fig. 1d). Percentage change in biomass weighted range size did not depend on the strongest predictor variable for a species distribution (Fig. 1b). These patterns were not as strong in the spring (see Supplemental Fig. 1).
Fig. 1: Historic distribution shifts versus the most important predictor variable for a species distribution in the fall.

Shifts in mean centroid of biomass from first 5 years to last 5 years in kilometers (a), percentage change in biomass range size from first 5 years to last 5 years in meters squared (b). Change in northern (c) and southern (d) extent of the biomass weighted range from first 5 years to last 5 years (n = 93). Brackets and numbers represent p-value. Whiskers represent 1.5* interquartile range. Box represents interquartile range as distance between first and third quartiles. Line represents median, red point represents mean, and black points represent outliers (outside of 1.5*IQR).

Full size image

Our results show that in the spring and fall, pelagic species’ distributions are primarily influenced by ocean temperature and depth, while demersal species’ distributions are predominately influenced by ocean temperature and substrate, and benthic species’ distributions are influenced by substrate (Fig. 2).
Fig. 2: Strongest predictor variable for each species group in the fall.

Percentage of each species group that had substrate, depth, bottom temperature, and salinity as the strongest predictor variable in terms of deviance explained for the entire time series (n = 93). Results were similar across seasons (see Supplemental Fig. 2).

Full size image

Pelagic species have shifted mean center of biomass significantly more over the historic time period compared with benthic species in the fall and significantly more than demersal species in the spring (nonparametric pairwise Wilcoxon test p = 0.039 and p = 0.045, Figs. 3a and 4a). Similarly, pelagic species have expanded their range size significantly more than demersal species and have shifted the northern extent of their range boundary significantly more than demersal species in the spring (nonparametric pairwise Wilcoxon test p = 0.024 and p = 0.028, Fig. 4c, d).
Fig. 3: Historic distribution shifts for three species types in the fall.

Fall shifts in southern mean centroid (a), percentage change in range size (b), shifts in northern range boundary (c), and southern range boundary (d) from first 5 years and last 5 years in latitudinal degrees for each species group (n = 93). Brackets and numbers represent p-value. Whiskers represent 1.5* interquartile range. Box represents interquartile range as distance between first and third quartiles. Line represents median, red point represents mean, and black points represent outliers (outside of 1.5*IQR).

Full size image

Fig. 4: Historic distribution shifts for three species types in the spring.

Spring shifts in southern mean centroid (a), percentage change in range size (b), shifts in northern range boundary (c), and southern range boundary (d) from first 5 years and last 5 years in latitudinal degrees for each species group (n = 91). Brackets and numbers represent p-value. Whiskers represent 1.5* interquartile range. Box represents interquartile range as distance between first and third quartiles. Line represents median, red point represents mean, and black points represent outliers (outside of 1.5*IQR).

Full size image

These results indicate that benthic species, more influenced by substrate than pelagic species, have retained their historical distributions in response to climate change, while pelagic species have shifted drastically. Exemplars of benthic fish that have retained their distributions include American plaice (Hippoglossoides platessoides), witch flounder (Glyptocephalus cynoglossus), and winter flounder (Pseudopleuronectes americanus) (Fig. 5a). These species distributions are strongly influenced by benthic substrate (Supplemental Data 1 and 2). Exemplars of pelagic fish that have shifted their distributions include rough scad (Trachurus lathami), Atlantic menhaden (Brevoortia tyrannus), and round herring (Etrumeus teres) (Fig. 5b). These species distributions are strongly influenced by bottom temperature and salinity (Supplemental Data 1 and 2).
Fig. 5: Changes in the biomass weighted mean centroid of species distributions in the Fall.

Biomass weighted mean centroids were calculated for two time periods: time period 1 (1986–1990) and time period 2 (2014–2018). Benthic species associated with bottom substrate retain their historical distributions (a) whereas pelagic species have shifted distributions (b). Species were selected to show extreme shifts and extreme retentions of distributions, and all shifts can be found in Supplemental Data 1 and 2.

Full size image

By linking the historic evidence of species distribution shifts with their relationship with bottom temperature, bottom salinity, and benthic substrate, we have identified a broad generalization on how species with specific life history traits may be influenced by future climate change. Recent work suggests that pelagic species may shift farther under climate change compared with benthic invertebrates14, and recent studies have included sediment type as a constraining variable in projections of future species distributions15. In conjunction with this work, our research highlights that these strong shifts have already occurred historically, and they are influenced by the strength of a pelagic fish species’ relationship with bottom temperature or salinity, compared with benthic species, which are more influenced by substrate. Given the importance of bottom substrate on benthic species distributions, we may see shifts in population dynamics, such as abundance and productivity as temperatures warm instead of geographic shifts in distributions. For, increased ocean warming that may go beyond their preferred thermal envelope is expected in the Northeast LME and Mid Atlantic9. If affinity for substrate type keeps benthic species in regions that become too warm, these species may find themselves in suboptimal conditions and will not be able to relocate as easily as pelagic species.
Moreover, we examine demersal species that are influenced by both bottom temperature and substrate and have shifted moderately compared to benthic or pelagic species. Research in the North Sea suggests that demersal species are shifting to deeper waters, which may suggest an interaction between bottom temperature and depth that requires further research16. Research in the Northeast LME has linked the shrinking spatial distribution of cusk, a demersal species, to a combination of ocean warming and the ensuing fragmentation of suitable bottom habitat17, suggesting another interaction requiring future examination. As an intermediate case, these species may be the most unpredictable under climate change, and thus fisheries management will have to consider the varying nature of these species’ distribution shifts.
These results provide historical evidence of pelagic species shifting distributions while benthic species remain more associated with their preferred substrate, which is most likely a result of the functional differences between these types of species. For example, the recruitment success of Atlantic menhaden (Brevoortia tyrannus), a pelagic schooling species we identified to shift historical distributions, is strongly related to ocean temperatures and larger climate dynamics such as the Atlantic Multidecadal Oscillation which influences temperatures and salinity18. The suitable habitat and migration timing of mackerel (Scomber scombrus), a pelagic schooling species, has been linked to changes in ocean temperatures and multidecadal variability19, relying on temperature cues for their seasonal migrations. Research in the North Sea and Baltic Sea suggest that the northward shift of anchovies (Engraulis encrasicolus) and sardines (Sardina pilchardus), two pelagic species, is strongly linked to temperature20. Benthic species, on the other hand, rely on structured biotic habitats, such as marshes, coral reefs, and submerged aquatic vegetation as well as abiotic sediment for their survival21. The importance of abiotic sediment stems from the productivity of these habitats, as they usually contain high levels of detritus, microbes, and microinvertebrates22.
It is important to identify the limitations of the approaches used in this study. The original species-CPUE data were collected from North Carolina to the Gulf of Maine, meaning we sampled a realized niche of the studied species. In comparison, the fundamental niche of the species may extend beyond our study area, in both the southern and northern directions. This study examined the role of bottom temperature, salinity, depth and substrate on species distributions, but was unable to examine the potential effects of fishing pressure, interspecific interactions, demographic changes, population sizes, or larval dispersal on species distributions and abundance23. While research has demonstrated that simple area-weighted center of distributions can be biased, we attempted to account for this by using several metrics of distribution (area occupied, northern, and southern extents of ranges)24.
Despite these limitations, we expect our results will be valuable for fisheries managers as they anticipate the likelihood of species distribution shifts in their management areas. While current work has examined the role of temperature in determining species shifts, our work highlights the importance of examining the differential role of other static ecological variables on determining a species likelihood of shifting distributions under climate change. By uncovering the differential effects of certain habitat constraints on pelagic versus demersal and benthic species, we can begin to understand why certain species have shifted dramatically over the last thirty years, while others have retained their historical distributions. These results highlight the need for stock assessments and future species distribution models that include the functional differences among species as well as the environmental variables that constrain their distributions in order to more appropriately understand the potential impacts of climate change on their future distributions. Only then can we understand how future fisheries will be impacted by the ecological effects of climate change. More

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