UNIVERSIDADE DOS AÇORES DEPARTAMENTO DE OCEANOGRAPHIA E PESCAS 2017-2018

THE IMPACTS OF LONGLINES ON DEEPSEA SPONGES IN THE

Heather Alice Cyr

Promoter/Supervisor: Dr. Christopher Kim Pham

Master thesis submitted for the partial fulfillment of the title of Master of Science in Marine Biodiversity and Conservation within the International Master of Science in Marine Biodiversity and Conservation EMBC+

`No data can be taken out of this work without prior approval of the thesis promoter´

`I hereby confirm that I have independently composed this Master thesis and that no other than the indicated aid and sources have been used. This work has not been presented to any other examination board.´

07 August 2018, ______

´Executive Summary

Deep-sea sponge aggregations are known to be hotspots of biodiversity, providing key ecosystem functions and services. However, increased susceptibility through pressures is a major threat to marine ecosystems worldwide, and has led to their classification as Vulnerable Marine Ecosystems (VMEs) under FAO guidelines. Previous studies have quantified the impact of , with longlines as a recommended alternative due to their significantly reduced impact in VMEs. Yet their specific impacts are largely unknown and suspected to be greater in areas with enhanced habitat complexity. Therefore the objective of this study was to quantify the impact of long-line fishing on sponge grounds found within the Azores (NE Atlantic) by (1) quantifying bycatch through observer records and (2) quantifying in situ damages through video footage collected on a case study Azorean fishing ground, Condor . Analysis of the bycatch data collected by the observers, included an assessment of different gear types on the bycatch rates of sponges. In addition, the spatial distributions of fishing sets and bycatch was mapped with ESRI ArcGIS in order to identify potential critical areas. Our results suggest low bycatch rates overall (0.07 sponge 1000 hooks-1) and the potential for stone-stone designs to generate more bycatch than other gear designs. Analysis of video footage demonstrated further damage by longline activities, with an average 1 out 4 individuals impacted 1000m-2 of seafloor surveyed. The application of generalized additive models (GAMs) and Kernel Density Estimations (KDEs) were used to observe distributions of in situ damages according to sponge occurrences, impact, and derelict gear. The environmental variables included in the models were: depth, slope, bathymetric position index (bpi), eastness and northness. Depth was found to be the most determinant predictor in detecting the presence of sponges, gear, or impact. As expected, the highest levels of impact was found in areas where sponge abundance and lost fishing gear was more abundant. Finally, we combined previous data with photograph records from fisheries observers to classify sponges most subjected to longline damage and incidental mortality. The most susceptible sponges to incidental mortality and abrasive damages were found to have larger tridimensional aspects to their morphologies, with the rock sponge Leiodermatium as the most critical example, though we acknowledge limitations to our methodology in assessing smaller sponges.

Abstract

Deep-sea sponge grounds are known as reservoirs of biodiversity with high functional value. In recent years, international interest has emerged regarding the conservation of sponge grounds as Vulnerable Marine Ecosystems (VMEs). Deep-sea sponge aggregations are threatened worldwide from destructive bottom fishing practices and there is an urgent need for the development of more environmentally sustainable practices. The objective of this study was to quantify the impact of longline fishing on sponge grounds found within the Azores archipelago, including both bycatch mortality and in situ damages. Using data collected by fisheries observers and analysis of video footage on a case study fishing ground, the results suggest a reduced impact of longline on deep-water sponges. Longlines impacted mostly sponges with a higher structural complexities morphologies especially from lithistid types. The overarching goal here is to provide the necessary information to enhance management and conservation of this VME and to provide an evidence-based threat evaluation under FAO guidelines.

Keywords: Porifera, sponge aggregations, Vulnerable Marine Ecosystems (VMEs), Deep-sea, Conservation, Condor Seamount, demersal longline fisheries, FAO

Introduction

Deep-sea sponges: ancient animals with many functions

In deep-sea ecosystems, Porifera or sponge-dominated habitats are recognized as ancient “reservoirs of biodiversity” (Hogg, 2010), providing extensive functional importance to marine ecosystems (Bell, 2008). Porifera are suspected to be one of the first, if not the oldest animals in existence, dating over 635 million years (Maloof et al., 2010). One of their main ecological importances stems from their ability to process nutrients. Sponges keep the ecosystem’s energetic budget in balance, a role of considerable value in the face of climate change, nutrient loading, and fishing pressure (Griffiths et al., 2017). Their filtration capacities and microbial intake provide indispensable linkages in benthic-pelagic coupling, where up to 95% of bacteria are consumed and reprocessed alongside valuable nutrients utilized by phytoplankton such as silica and nitrogen, in addition to dissolved organic matter (DOM) and dissolved organic carbon (DOC) (Reiswig 1975, Pile & Young 2006, Yahel et al. 2007). In an otherwise oligotrophic, “marine desert”, their recycling efficiencies replicate gross primary production rates and provides sustenance for adjacent habitats such as coral reefs to thrive (Diaz & Rützler, 2001; Yahel et al., 2003; Hadas et al., 2006; Gibson, 2011; de Goeij et al., 2013).

In addition to their nutrient cycling capacity, sponges provide complex substrates along a relatively uniform seafloor, forming “habitat islands” in the deep-sea and encouraging species-rich epi-faunal communities (Beaulieu, 2001). This habitat association extends to commercially valued fish such as the Rockfish, Sebastes sp., whose juvenile stages are often linked to sponge grounds (Collie et al., 1997; Freese & Wing, 2003; Hogg, 2010). Even the dead stalks of Glass sponges (Hexactinellids) at 4000m depth have been documented in the involvement of reproductive strategies and development of community offspring like fish and echinoderms; serving as egg depots, spawning grounds, and refuge from predators (Konecki & Targett, 1989; Barthel, 1997; Beaulieu, 2001; Purser et al., 2016).

The imminent threat of fisheries

As sponge grounds are associated with higher biodiversity and abundance, the removal of sponges from an ecosystem has a negative impact on faunal composition (Beazley et al., 2013). Trawling is known for its undeniable, devastating consequences worldwide. Disturbances to the seafloor and the removal of vital habitat-building organisms changes benthic communities regardless of the trawl type (Kaiser et al. 1998). A study by Wassenburg et al., (2002) found trawls can reduce 13.5% of sponge biomass in one year, and with this reduction of megabenthos comes changes of species compositions. Other studies have demonstrated that with only 13 trawls, up to 90% of the benthos is removed and recovery rates would be minimal for slow growing species (Pitcher et al., 2000). Although studies have looked into recovery rates, thirteen years after trawling the average density of large sponges was still 32% lower (Malecha & Heifetz, 2017.) As there is no sustainable way to trawl (Watling 2013), global alternatives are needed. Longline fisheries have demonstrated a potential to fulfill the fishing niche of trawls with a significantly reduced impact in VMEs (Pham et al., 2014). The true cost of longline use is not yet known. There are associated risks in regards to conservational species of interest. Prior studies have linked longlines to the mortalities of elasmobranchs (e.g., Gallagher et al. 2014), seabirds (e.g., Anderson et al., 2011) and turtles (e.g., Lewison et al., 2004). Even with reduced impact, a study in the Azores with cold water corals showed that bottom longline gear selected larger and more complex epibenthic structures in higher frequency (Pham et al., 2014). Further assessments are necessary in determining the scale of these removals in regards to VMEs. The ultimate impacts will depend on resistance, recruitment, growth of survivors, and active immigration (Hiddink et al., 2017). While it is known sponges ultimately contribute to the diversity and ecological connectivity in deep water systems, their biological nature and ecological function is largely still unknown, which makes it all the more threatened before being truly understood (Hogg 2010).

Our study

As research confirming the importance of sponge aggregations continues to grow, their habitat dynamics and life histories largely remain a mystery. SponGES (http://www.deepseasponges.org/) is a multidisciplinary collaboration of many projects funded by the European Union´s Horizon 2020 research and innovation programme under grant agreement No 679849. Its goal is to elucidate what is known about deep-sea sponge ground ecosystems in the North Atlantic and aid in their conservation and resource management. In aiding their ecological function we sustain these economic and ecological services from which we ultimately benefit from as a society. The underlying principle of VMEs is that their loss could not be compensated for, nor replicated by other ecosystems. Although attempts are being made to quantify the value of these systems to society (Ressurreição & Giacomello, 2013), their inherent value is being acknowledged by policy, which enforces their management.

The Azores (NE Atlantic) is a leading example in marine conservation and policy effort. The presence of cold-water coral and sponge are common features of (Morato 2010a; 2010b) and play a role in the vitality of Azorean fisheries stocks (Pham et al., 2015). The ban (Council Regulation (EC) No. 1811/2004) and a regulation to protect deep-water coral reefs from fishing in a large area of the Azorean EEZ (Council Regulation (EC) No. 1568/2005) were established in acknowledgement of the regional importance and ecological sensitivity of these areas before they were officially declared VMEs.

Therefore, our objective is to quantify the impact of long-line fishing on sponge grounds found within the Azores (NE Atlantic) by (1) quantifying bycatch through fisheries observer records and (2) quantifying in situ damages through video footage collected on a case study Azorean fishing ground, Condor seamount. This study aims to provide key information to support and enhance the management and conservation of deep-sea sponge VMEs. Therefore the principles herein coincide with UN General Assembly (UNGA) Resolution 61/105, as a FAO declared Vulnerable Marine Ecosystem (VME) (ICES/FAO 2009).

Materials & Methods

Study area: The Azores The Azorean archipelago is a Portuguese maritime territory consisting of nine islands situated along the Mid-Atlantic Ridge (MAR) off the coast of (Fig.1). It contains one of the largest exclusive economic zones (EEZs) in the European Union, encompassing a marine surface area of nearly one million square kilometers where trawling is prohibited (Regulation (EC) no.1568/2005). As Morato et al. (2008) reported, the Azores is a major seamount region where approximately 3.3 seamounts are found per 1000km2, and accounts for 0.4% of the world´s seamounts (SAUP, 2005). It is generally characterized by a high frequency of deep waters averaging 3000m and lacks a continental shelf (Menezes et al., 2003). Given the region´s devotion to seamount conservation, , and the presence of Vulnerable Marine Ecosystems (VMEs) (see Introduction), the Azores is a uniquely suited study area to measure the impacts of small-scale (i.e. handline and longline types) on seamount biotopes such as sponge grounds.

Figure 1. The Azorean Archipelago. Nine Portuguese islands (shown in black) dispersed along the Mid- Atlantic Ridge (see inset above) in the North (37-40°N, 25-32°W). Fisheries data was collected from two observers aboard longliner vessels throughout the region in 2010-2011 and 2017. In- situ video footage was taken from Condor seamount (∆) located 17 kilometers southwest of Faial. Rapid shifts in bathymetry represented in color scheme (0-5000m).

1. Quantifying sponge bycatch a. By catch data collection from commercial vessels Similar to Pham et al. (2014), two observers on-board fishing vessels collected data on epibenthic bycatch from handline and two types of longline (stone-buoy, stone-stone) activities (Fig.2). A total of 657 data sets were collected throughout the archipelago between 2010 and 2011 (452 sets), and 2017 (205 sets). Collected organisms were preserved for later identification, morphological classification, and laboratory analysis with partner projects. For the intent of our study, we documented all Porifera, excluding encrusting types. The species and size of fish catch, geographical position, gears used, number of hooks and lines, discards and sponge bycatch are an example of the recorded data. An example of a completed observer record can be found in supplementary material (Supplementary Fig.S1). All data was entered by observers into the fisheries observer database for later extraction. “Pedra-Boia” (Stone-Buoy)

“Pedra-Pedra” (Stone-Stone)

Linha de mão” (Handline)

Figure 2. Types of demersal fishing gear used in Azores. The two types of longline (stone- buoy and stone-stone) are operated similarly and share the same general features. The hooks of the stone stone-buoy are slightly elevated, at an angle from the seabed. This method is used most commonly for the red seabream. The stone-stone design, used for the common seabream and blackbelly rosefish rests one stone across from another as the hooks are suspended across. The handline is suspended vertically with hooks off the sides. This method is used to catch a wide vartiey (i.e. Blackspot seabream and Wreckfish). Adapted from Pinho & Menezes (2003). Images: www.horta.uac.pt/projectos/cepropesca/artes2_EN.htm

b. Bycatch classification and morphological characteristic Epibenthic organisms were separated between sponges, corals or rocks. In the 2017 data set, sponges were photographed upon a centimeter quadrat or ruler for size reference (Supplementary Fig.S2a), resulting in 102 photographs. In the computer laboratory, sponges were first assigned a morphology according to adaptations from previous work (Supplementary Fig.S.2b) and the state of condition was also assessed (intact, damaged or fragmented). Intact specimens were deemed to be entirely present with no visible damage from line, while damaged samples shown visible signs of damage from the line and fragments were pieces which came from a presumed to be larger specimen. Photographs were measured electronically using ImageJ software (Schneider et al., 2012). The procedure as follows: metric references presented in photos were used as a calibration tool in the ImageJ program. Images were zoomed into at the fullest extent possible while ensuring the entire specimen was accounted for in the field of view. Sponges were then measured according to: length (longest side), width (lesser side), and height (distance from ground), when the angle was available. When referring to size the following classification was used: small (<10cm), medium (10-20cm), or large (>20cm). Photos were sent to SponGES taxonomists for further classification and finalized taxonomic data was updated to the lowest feasible taxonomic resolution possible using WORMS (World´s Register of Marine Species). c. Data analysis Spatial distributions of gear use, bycatch rates and fishing sets were created using the fisheries observer database and ESRI ARCGIS (10.2.). Total number of hooks was equivalent to the number of hooks multiplied by the number of lines. Bycatch rate was expressed as the number of bycatch items (i.e. sponges) per one thousand hooks. To offset any vessel or line drift occurring from current, the initial and final coordinates where the line was deployed and 푙푎푡(푖푛푖푡푖푎푙)+푙푎푡(푓푖푛푎푙) 푙표푛(푖푛푖푡푖푎푙)+푙표푛(푓푖푛푎푙) retrieved were used to calculate midpoints where: , ; 2 2 the result was used as the final latitude and longitude for bycatch rates. Data was plotted onto maps using a 10x10km grids according to gear type and fishing sets to demonstrate if the incidence of bycatch was related to single or multiple events. Differences between bycatch rate were tested among gear types using the Kruskal-Wallis one-way ANOVA.

2. Quantifying in situ damages

In situ video analysis To assess the level of impact caused by longline fisheries on deep-water sponge habitats, in situ damages were classified by examining the physical condition of sponge habitats along with the occurrence of derelict longline gear on case study site: Condor Seamount. Condor Seamount (Fig.3) is located approximately 17km southwest of the island of Faial (Fig.1). As described in Tempera et al. (2013), Condor is a large linear volcanic ridge (LVR), extending 35km long, 23km wide across the base, and rising more than 1800m from the seafloor, with depths ranging from 185m to 2003m. As a shallower seamount, it is known as a traditional fishing ground frequently used by handline and bottom longliner gears down to a depth of 600m (Menezes et al., 2013). Since 2010 a fishing moratorium has been in place, favoring Condor as an observatory for marine research (Morato et al., 2010). The presence of Vulnerable Marine Ecosystems (VMEs) such as deep-sea coral gardens and sponge aggregations on Condor has added to its conservational status and research importance (Tempera et al., 2012; Braga- Henriques et al., 2013).

Figure 3. In situ video analysis on Condor summit. Damaged sponges and lost longline gears were annotated using 15 hours of video footage from the H004 transect ( ) across Condor summit, situated at an average of 300m depth. The route measured 14.42km across from start to end and ranged 192 – 551m in depth. Other transects shown here are from MIDAS deep sea mining demonstration and are not included in this study. The video footage analyzed here was collected across the summit (~300meters) as part of the MIDAS project (http://www.eu-midas.net/) with a towed-camera “HD Hopper” developed by the Royal Netherlands Institute for Sea Research (NIOZ). The linear transect (H004) crossed the entire summit, resulting in a 14.42 km linear transect and approximately 15 hours of video footage. The geographic position of HD Hopper was recorded by USBL systems (Ultra Short Baseline). Video annotation and data processing was made using Ocean Floor Observation Protocol (OFOP). As a first step, the navigational data were filtered for outliers and smoothed with a moving average to remove false loops. Video footage was synchronized to the navigation file and annotated using Ocean Floor Observation Protocol (OFOP). As lasers moved across transect, any sponges in horizontal alignment were annotated, classified by morphology (Supplementary Fig.S2.b) and physical condition (Supplementary Table S1.). Using the 30cm space between lasers as a frame of reference, sponges smaller than 20cm were excluded as it was not possible to consistently assess their physical condition. Encrusting sponges were also not included in the analysis due to their lower vulnerability to bottom fishing. To enhance taxonomic identification, several morphologies (Supplementary Table S.5) were added as sub- categories to the “Massive” morphotype, using the previous work of Pereira (2013) and de Carvalho (2013) in the region. Screenshots from video footage were sent to SponGES taxonomists for identification when possible. In case of longline presence, start and end times were documented according to the visibility in the frame and alignment to lasers. Litter items were also observed as part of an ongoing effort to quantify the amount of litter in the area. The estimation of the surveyed area was developed by GEOMAR specialists using the methodology outlined in Schoening et al. (2015). On average, the field of view was 10m2 (±5.35).

Generalized Additive Models (GAM) We used GAM to assess the role of abiotic factors (depth, rugosity, slope, roughness, eastness, northness, and bathymetry position index (bpi)) on the abundance of sponges, derelict fishing gear and damaged sponges. The resulting zero inflated count data from video analysis implied that we had to use a negative binomial distribution and a log-link function (R package mgcv). Individual sponge morphologies (massive, globular, etc.), individual impacts (entanglement, dislodging, fragment), and individual gear types (monofilament, weight) did not fit tested over- dispersion distribution models (Poisson, quasi-Poisson, negative binomial) with values >1.2. Therefore, response variables were defined and tested as follows: total sponges (all sponges annotated), impacted sponges (all damages annotated), and derelict gear (all gears annotated). The following predictor variables were included in the initial models: depth, bathymetric position index (bpi), roughness, slope, eastness, and northness. Collinearity was revealed between roughness and slope. As a result, only roughness was considered in the model. Model selection followed 5 distinct backward steps based on Akaike information criterion (AIC) values and covariate significance (Supplementary Table S2). The final model for total sponges included depth and northness as the best fit according to lowest AIC value and covariate significance and were as follows: Total sponges ~β + s(depth) + s(northness) + ε; Impacted sponges ~β + s(depth) + s(northness) + ε; Derelict gear ~β + s(depth) + s(northness) + ε; where β is an overall intercept, s is an isotropic smoothing function (thin-plate regression spline), and ε is an error term (Supplementary Table S3). Total deviance explained by the final models were 17.9%, 23.5%, and 17.8% with an associated dispersion of 0.93, 0.97, and 1.16 for total sponges, impacted sponges and derelict gear respectively. Graphical representation of the residuals did not suggest clear violation of the assumptions (Supplementary Fig.S3). The field of view was used in conjunction with the Haversine formula (Fig.4) to create sampling units. Continuous annotation allows defining different sizes of sampling units according to the objectives and data available. The resolution of our bathymetry permitted a lower resolution (100m2), but due to overdispersion and spatial autocorrelation, units were tested in cumulative 100m2 increments (i.e. 200m2, 300m2, 400m2, etc. ) until the optimal sampling unit was found. The final selection, 1000m2, resulted in 529 sampling units total. Sampling units were tested for spatial autocorrelation using variograms.

푙푎푡 − 푙푎푡 푙표푛 − 푙표푛 푑 = 2푅푠푖푛−1 {√푠푖푛2 ( 2 1) + 푐표푠푙푎푡 푐표푠푙푎푡 푠푖푛2 ( 2 1) } 2 1 2 2

Figure 4. Haversine Equation, formula used to calculate distance between geographic coordinates. Distance was used with cumulative area from frame of view in creating sampling units of 1000m2; d=distance; R=radius of earth (~6371km); lat1=initial latitude; lat2=final latitude; lon1=initial longitude; lon2=final longitude;

Kernel Density Estimation (KDE)

Kernel densities (Silverman 1986) served as additional visual aids in estimating the unknown probability of response variables: total sponges, impacted sponges and derelict gear across Condor summit. This was done using the spatial analyst tool from ArcGIS® software by Esri. Procedural information is outlined here: (http://pro.arcgis.com/en/pro-app/tool-reference/spatial- analyst/how-kernel-density-works.htm), where the bandwidth and density of the linear feature (i.e. transect), was determined according to the methods outline in Silverman (1986) and Chen (2017).

Results

2. Quantifying bycatch from commercial fleet The same general area was fished in both datasets, mainly on seamounts (Fig.4). Longline gear types varied in number between years, with the stone-buoy art accounting for 85% of the gear used in the 2010-11 and 31% in 2017 (Table S4.) The longline stone-stone (PP) was deployed more often in 2017 than in 2010-2011 (Fig.4). Handlines were used on the central areas of the seamount in 2010-2011 and closer to land in 2017.

Bycatch rate Out of the total 657 sets, 14% were affiliated with sponge bycatch (Supplementary Table S4.). Handline gears exhibited almost no associated bycatch (1-2%) with only 2 incidences of a single sponge caught in the total 135 sets. The bycatch rate varied significantly among gear types (Kruskal-Wallis; p<0.001) (Fig. 5), though the total amount of data available for each gear was variable (Table S4.). The average rate for the stone-stone method (n=74) was highest with 0.2 individuals caught 1000 hooks-1 (CI 0.19). Longlines overall had a considerably low rate of bycatch 0.07 1000 hooks-1 (CI 0.02). The stone-stone gear caught a maximum of 10 individuals in one fishing event, while the stone-buoy caught 7 and the handline 1. (Table S4.).

Figure 4. Deep-sea fisheries gear used in 2010-2011 and 2017 throughout Azores (figures in black). Distributions of gears shown included: Longline PB (Longline Stone-Buoy), Longline PP (Longline Stone-Stone), and handline arts.

Figure 5. Bycatch rate measured in number of sponges caught per thousand hooks according to gear type. (Kruskal-Wallis One-way ANOVA; H=63.59 p<0.001).

Bycatch rates were highest on seamount locations (Fig.6), with fishing sets being distributed throughout. The stone-buoy gear was frequently used (11-17 sets) in several 10km2 areas where bycatch was also documented. (Fig.6). The stone-buoy record showed several locations centrally placed on the seamount with several low rate bycatch events (<1 individual 1000 hooks-1), coinciding to 1-2 sets of data. The stone-stone method mirrored this, with several locations of intermediate bycatch rates (1-3 individuals 1000 hooks-1) coinciding to single fishing events. One incidence of a high bycatch (7-10 individuals 1000 hooks-1) was recorded for the stone-stone gear and attributed to a single data set (Fig.6) (Supplementary Table S6.). Longline Stone Buoy

Longline Stone-Stone

Figure 6. Spatial Distribution of deep-sea sponge coinciding fishing sets (top) and bycatch rates (bottom) throughout the Azores. Fishing effort data was extracted from observer records to reveal patterns in bycatch locations. Bycatch rates were estimated as number of organisms 1000 hooks-1. Maps were generated using ArcGIS® software by ESRI.

Sponge bycatch classification Sponge bycatch was diverse in shape and size (Supplementary Fig.S4). Massive sponges were caught most often (Fig.7; Supplementary Table S5.) and were the largest types on average ~23cm (±9). The small size class (<10cm) consisted of branching, pedunculated, and tubular morphologies. These morphologies came in multiple sizes and accounted for 35% of the record.

Figure 7. Frequency of morphologies represented in 102 bycatch photos from longline fishing activities throughout the Azores in 2017.

The largest sponge was flabellate (91cm), the smallest tubular (5cm). Globular, staked, and papillate types were seldom recorded (<3%). The flagelliform type was not observed. The majority of sponges were intact (74%), 8% were damaged and 18% were fragments. Fragments came in various sizes from small (<10cm) to large (>20cm). The ratio of intact sponges to fragments according to size can be seen in Figure 8. Taxonomic inquiry of the 102 sponges revealed 9 orders and 15 families, with 8 specimens declared unknown (Table 1.) Petrosidae and Macandrewidae families were most abundant, while the order “Lithistida” specimens were also prevalent.

Intact Fragment

Figure 8. Size distributions of sponge bycatch measured with ImageJ using photos taken by observers on board longline fishing vessels throughout the Azores in 2017. Measurements include length, width and height. Data for intact specimens and fragments are shown.

Table 1. Taxonomy results from sponge bycatch photos taken by observers on longline vessels during 2017 in the Azores.

Class Order Family n° of sponges Demospongiae Axinellida Axinellidae 4 Raspailiidae 1 Dictyoceratida Irciniidae 1 Haplosclerida Chalinidae 1 Petrosiidae 22 Lithistida

- 19 Polymastiida

Polymastiidae 1 Suberitida Stylocordylidae 1 Tetractinellida Azoricidae 8 Corallistidae 2 Geodiidae 1

Macandrewiidae 14 Phymaraphiniidae 1

Vulcanellidae 1 Unknown 9 Hexactinellida Amphidiscosida Pheronematidae 5 Lyssacinosida Rossellidae 3

Unknown 8 Total 102

In-situ video damages Altogether 1923 sponges (>20cm) and 222 longline encounters were annotated at depths ranging from 192 – 551m across Condor summit. Approximately 66% of all sponges were found to be intact without visible damage, 13% fragmented and 2.3% entangled (Supplementary Table S1). It was not possible to identify the condition of approximately 7% of size appropriate sponges. Damages according to morphotypes were documented (Supplementary Table S5.). The majority of sponges (>75%) were a massive type, flabellate and pedunculate varieties. Massive, lamellate sponges (n=222) like Leiodermatium, were the least intact (16%), most fragmented (31%), dislodged (9.5%), entangled (5%), or dead (22%). Overall gear presence was higher in shallower waters (200-300m) and decreased with depth (Fig.9; Fig. S5.). The sampling units (n=529) demonstrated on average approximately 1 out of 4 sponges is damaged and ~ 0.5 gears are encountered per 1000m2. Non-fishing gear items were encountered at an average rate of 0.05 per 1000m2.

Generalized Additive Models (GAM) using 1000m2 sampling units GAMs revealed patterns according to depth for all response variables and also in northness for derelict gear (Fig.7). Higher presences of sponges, impacts and gears were shown at 250m and 325m depths and declined with increasing depth.

a) Total sponges

b) Impacted sponges

c) Derelict gear

Figure 9. Generalized Additive Models created from in-situ video analysis on case study fishing ground Condor seamount Azores. Models for response variables: a) Total sponges, b) Impacted sponges, c) Derelict gear, were examined for patterns according to depth (top model) and northness (bottom model). Dotted line represents confidence interval.

Kernel Density Estimation (KDE) using 1000m2 sampling units Kernel densities revealed the estimated locations of the highest concentrations of total sponges, impacted sponges, gears and entanglements (Fig.10). Approximately 1 of every 4 sponges was impacted in high concentration areas at the beginning and end of transects with approximately 3-4 sponges per. Derelict gear and entanglements had approximately twice the likelihood of occurring at the beginning of the transect that at the later portion. a) Total sponges

b) Impacted sponges

c) Derelict gear

d) Entanglements

Figure 10. Kernel Density Estimation (KDE) was used to identify patterns between a) total sponges, b) impacted sponges, c) derelict gear and d) entanglements.

Discussion

Fishing location, gear operation and bycatch rates As expected, fishing effort in the Azores took place almost exclusively on seamounts, reflecting the importance of these areas for fisheries within the region (Morato et al., 2008a; Morato et al., 2010; Colaço et al., 2013). (PAB) was shown to be a hotspot of fishing activity; however, there was a surprising lack of biological data available for this area since most studies focus on tectonic activity and bathymetry (e.g., Cannat et al., 1999; Escartin et al., 2001; Miranda et al., 2014;). Previous observation of the site has been linked to elasmobranch aggregations (Sobral, 2013; Gargan et al., 2017), cephalopods (Goncalves, 1991), seabream populations (Stockley et al., 2005), rare fish occurrences (Afonso et al., 2013), and demersal fish (Parra et al., 2017; Pham et al., 2015). Given the frequency of fishing activity and previous work within this setting, PAB merits more biological research attention. The inshore handline proved to be the most sustainable gear type relative to sponge bycatch, supporting the general concept that reduced fishing effort improves the sustainability of the method (Pauly et al., 2002). While the longline record of sponge bycatch was minimal overall (0.07 sponges /1000 hooks), even the lowest of bycatch rates may threaten populations within as little as 20 years’ time (Lewison & Crowder, 2003). Our data suggested bycatch might be significantly higher with the stone-stone design, revealing the need to compile more information on this gear type. Data availability was sporadic for gear types (not covering the same locations/depths, which are confounding factors that limit our capability of identifying differences between gear type) and thereby limited analysis between longline designs. According to Pinho & Menezes (2003), the stone-stone design is less commonly used, and the data set reflected this with scarce documentation for this gear type in 2010-2011. On the contrary, in 2017 the stone-stone design was used more frequently. This may be attributed to different target species for that particular year like the common seabream (Pargus pargus) and blackbelly rosefish “Boca negra” (Helicolenus dactylopterus) (Pinho & Menezes, 2003). The details of the catch record were not analyzed to confirm differences in targeted catch according to gear type. In terms of sustainability, although bycatch is low, it is still documented, often coinciding with only one single fishing event. Depth plays an important factor in determining sponge assemblages and this has been documented throughout the region (Pereira, 2013; Carvalho, 2013; Ramos et al., 2016). Therefore rates may be lower because sponges are not occurring in large assemblages in a given area or within the depth range of fishing effort. Pitcher et al. (2010) acknowledged this factor when observing trawling impacts on megabenthos. However, other factors of benthic habitat should be considered like geological characteristics of the seafloor and sensitivity of the species present (Malecha & Heifetz, 2017; Yoklavich et al., 2018). Sponges occur in variable assemblages and densities and could be heterogeneously dispersed thereby reducing the probability of encounter. Yoklavich et al. (2018), noticed sponge abundances to be predictably higher where rocky outcrops were observed. Bycatch records hold value as incidental “occupancy surveys” (Royle & Nichols, 2003). These small-scale sampling campaigns can be used to monitor presence-absence and distribution of Porifera occurrences and absences. The power of such a tool would grow over time and be used in mapped distributions for important VME indicator species. However, it is important to take into account that catchability might change depending on a wide number of factors such as weather, fishers, catch of fish, surrounding substrate type and bathymetry, etc.

Data analysis with predictive models Longline activity was most apparent on the shallower portions of Condor summit (200-350m) and faded with depth. As revealed by Generalized Additive Models (GAM), depth was found to be the key determinant in gear presence, impact and sponge aggregations. As depth is an important factor in shaping fish distributions and abundance in the Azores (Pham et al. 2015; Parra et al. 2017), the highest presence of fishing gears was suspected to occur where the most fishing effort took place. This study supports that this area coincides with the depth range of 200-400m, as reported by Menezes (2003). Although northness was included in the final GAM, it was not found to be a discernible predictor in all cases. A relationship between sponge cover and northness was found in our data, where peaks of sponge abundances occur in the transitional slope of shallow to deeper water facing north, a feature which may be relative to benthic currents (Guinan et al., 2009). Substrate type would have been a valuable piece of information to include in the testing of response variables as sponge diversity for this area has been associated to substrate type (Pereira, 2013). Consequently, it is not surprising that patterns of impact are seen in the larger (>20cm) sponge communities at these shallower depths due to higher total sponge presence and increased the likelihood of a longline encounter. Entanglements were analyzed as the most representative sign of longline impact, KDE provided insight that entanglements have a higher probability to take place in shallower areas, again, coinciding with the prominent number of gear and sponges documented in that area. It should be noted that longline estimates here are interpreted as “encounters” and not a count number of lines since the field of view most often did not allow such perspective. Even still, density estimations and litter observations are in accordance with previous findings for European seamounts (Pham et al., 2014) and Condor summit (Pham et al., 2013). In general, monofilaments are abundant, glass is the most detected non-gear item, and hardly any plastic is observed. In analyzing patterns of distribution for response variables, zero- inflated count data proved to be a challenge often faced in ecological surveys (Martin et al., 2005). However, using negative binomial regression allowed for more flexibility with overdispersed data. The Akaike information criterion (AIC) allowed for the selection of the most appropriate model according to the number of predictors involved (Akaike, 1981). It is acknowledged that our data may contain “false zeros” where our object of interest (lines, sponges, impact) was present, but not detected (Martin et al., 2005). Ultimately, increasing our sampling unit area from 100m2 to 1000m2 helped to achieve the best fit for identification of response variable distributions.

Bottom longline impacts on sponge communities The results of this study demonstrate a wide variety of sponges exist in the Azores and are subjected to be impacted by bottom longlines. Analysis of bycatch photos reflected the work of Pereira (2013). Taxonomic results were similar in both studies and contained several VME indicator genera as proposed by OCEANA (i.e. Phakellia sp., Stylocordyla sp., Pheronema carpenteri., Asconema sp., and Leiodermatium sp.) (OCEANA 2016). The comparison of the two studies demonstrated that “Lithistida” (genera Leiodermatium and Macandrewia) and Haplosclerida (genus Petrosia) were most frequently caught. This is likely due to their larger, complex morphologies, which have been previously associated with longline selectivity in VMEs (Pham et al., 2014). Smaller types (<20cm) such as Stylocordyla and Pheronema are also VME representatives and should not be taken for granted even if caught in lower frequencies. The comparison of results from bycatch photos and in situ observation of damages reiterates the aforementioned, large sponges of the same genera were impacted. Taxonomic resolution was low for this section of the study due to the nature of video analysis. The combination of the two methodologies is useful, however, as the video analysis is likely biased to larger sizes and bycatch photos. According to the known depth distribution provided for Condor (Pereira, 2013), VME types likely to be present, but not accounted for likely include Axinella, Phakelia, Asconema, Stylocordyla. It is unlikely that Pheronema was excluded since the surveyed area did not extend to its known depth range (704-1082m). In general, as demonstrated previously in Pereira (2013) the summit may contain areas that are dominated by small sponges that are unaccounted for here. It is important to keep in mind future alternatives for the quantitative assessment of these VME indicators, since as the bycatch record demonstrates they are also subjected to mortalities. Longline impacts on reef-forming Lithistids The assessment of in situ damages unveiled Leiodermatium lithistid communities to be the most impacted and least intact species measured along Condor summit. Maldonado et al. (2015), also observed severely deteriorated aggregations of massive reefs (>1m high) along Stone Sponge Seamount (SSS) in the Mediterranean, where 60-90% were structurally broken down with heavy sediment accumulation. Except for a few fishing lines, there was a lack of evidence linking the demersal fishing fleet to these impacts as no activity was observed during the 4-year study. This demonstrates a need for better insight as to the natural condition and tolerances of these sponges. Limited ecological data is presented for these lithistids while most studies have focused on the distribution and phylogeny (e.g., Kelly-Borges & Pomponi 1994; Carvalho, 2013; Schuster et al. 2015 ;) or biotechnology (e.g., Miller et al., 2018; Wright et al., 2017). An attempt was made to account for these potential impacts nonetheless. Similar to Maldonado et al., (2015) at times fishing gear was not present, but impacted communities were observed. It is presumed the damages originated from longlines given the number of gear encounters across the summit. However, we can exclude that part of the individuals fragmented could have resulted from natural disturbance. Fragmentation was accompanied by a combination of the following: epibionts, discoloration or heavy sedimentation (Wulff 2006; Bo et al. 2014; Bell et al., 2015b). It is plausible that impacted or fragmented sponges would be less resilient to overgrowth or that their functioning would be reduced. The response and recovery of sponges to impact is mostly unknown and likely to vary depending upon the morphology and structural complexity of the sponge (Wulff 2006). Wulff, (2006) found that within 1-3 weeks after disturbance activity, some sponge fragments were capable reattached to substrates. Assuming the moratorium on fishing in Condor seamount was respected by the fishers, any displacement we have observed took place almost a decade ago. Leiodermatium may have the ability to reattach after slight disturbances, but it does not seem to be the case with larger dislodged reef formations. Bell et al. (2015b) examined sponge response to sedimentation and demonstrated some sponges adapt to sedimentation or modify behavior in response to burial. Leiodermatium can occur under heavily silted conditions, but this may be different between species and identification of this type is awaiting spicule analysis. From in situ observation, sedimentation seems to indicate a deteriorating or fatal state which usually coincides with a loss of color or epibionts, linked the high microbial activity associated with this sponge (Maldonado et al., 2016). Although Rooper et al. (2011) estimated recovery rates for some sponges to be up to 80% after 20 years, Wulff (2006) acknowledged, that with increased structural complexity comes a lessened ability to regenerate from damage, as seen in this study. Given the observatory status of Condor, it serves as an ideal setting to examine the potential for this sponge´s recovery and to also enhance the knowledge of its ecological function and value as it is known to build massive reefs and has an ancient lineage (Maldonado et al., 2015). The data used in this study demonstrates that bottom longlines have negatively impacted Leiodermatium communities on a large scale as demonstrated through bycatch records and in situ analysis. The ecological and evolutionary characteristics associated to Leiodermatium as well as its susceptibility to fisheries makes it a key genus in identifying Vulnerable Marine Ecosystems (VMEs) consistent with FAO (2009) guidelines under UNGA resolution 61/105. It is suspected to be one of several important VME genera existing throughout the Azorean archipelago. As deep- sea sponges are reservoirs of biodiversity with numerous roles of high functional value (see Introduction), their removal and destruction carry a threat to ecosystem homeostasis. One of the

most obvious functions lost with biomass removal is habitat provision. Additionally, biological structure and complexity itself are known to provide a direct food supply, larval recruitment surface, and refuge from predation for a wide range of species (Wulff, 2006; Taylor et al., 2007; Buhl‐Mortensen et al., 2010; Maldonado, 2016). Even dead skeletons offer value. The tridimensional structure of dead lithistids hosts new lithistid recruits and aids population maintenance (Maldonado, 2015). The stalks of dead glass sponges have been found to be necessary for the multiyear brooding process of rare octopods (Purser et al., 2016). Yet most details behind the reproductive processes of deep-sea sponges and their interaction with the reproductive stages of surrounding fauna are mostly unknown (Maldonado 2016). Given the defining nature of VME species as slow growing with long, unpredictable life histories (FAO, 2009) the repetitive removal of considerable biomass among the bycatch record deserves strategic attention. Longlines are known to be a far more sustainable alternative to trawls (Pham et al., 2014), yet their selectivity for larger biomass sponges and coral will be detrimental to ecosystem functioning and associated communities in a largely unpredictable way. Future works are recommended from the observations of this study i) ecological surveys of frequently used fishing area, Princess Alice Bank ii) further examination of differences in catch rate and incidental mortality between bottom longline gears iii) creation of FAO identification poster for deep-sea sponges of the Azores. iv) studies regarding life history characteristics and recovery of deep-sea sponges

Conclusion

The results of this study indicate a wide variety of potential VME species exist on seamounts throughout the Azorean archipelago. In spite of low bycatch rates, sponges of larger sizes, occurring at fishing depths are particularly subjected to damages and mortality from bottom longline fishing activities. The tridimensional structure of a massive reef-forming lithistid, Leiodermatium, was found to be particularly susceptible to the impact of longline gears. Given the lack of data regarding its recovery ability and reproductive capacity, Leiodermatium and other massive lithistids are recommended as a priority for conservational attention and strategy consistent with UNGA resolution 61/105.

Acknowledgements

SponGES has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 679849. Thank you to the sponGES taxonomists who helped with identification (Joana Xavier, Francisca de Carvalho, Raquel Pereira) and to those sponGES team members in Horta for their contribution (Ana Colaco, Marie Creemers). Thank you to University Pierre and Marie Curie University for the thesis mobility grant. Thank you to the EMBC+ staff and students for their dedication and motivation to make these last two years meaningful and rewarding.

I am forever grateful to my supervisor Christopher K. Pham, for the opportunity to join him in this project and for all of his patience, helpfulness, and encouragement along the way. Phoebe D. Chappell and Michele Guidone from my time at URI, for writing recommendation letters any time I asked, and encouraging me to continue on.

To my best friends Krissy, Moby & Volker, my lovely Linda+Carol, Madre, Uncle G, Mem, Aunty Jan, stistah + co, Nichole, Annelore & Gerhard, Hill, Kel, Ty, Christine, and Ms.June; And all of my other family and friends, near or afar. You are always in my heart.

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