Elodie Despouy
email: elodie.despouy@cnrs.fr

Jonathan Curot

Martin Deudon

Ludovic Gardy

Marie Denuelle

Jean-Christophe Sol

Jean-Albert Lotterie

Luc Valton

Emmanuel J Barbeau

A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP

Keywords: recognition memory, iEEG, hippocampus, perirhinal cortex ERP = Event Related Potential, iEEG = intracerebral EEG, MTL = Medial Temporal Lobes, minRTs = minimum Reaction Times, MVPA = Multivariate Pattern Analysis

One key item of information retrieved when surveying our visual world is whether or not objects are familiar. However, there is no consensus on the respective roles of medial temporal lobe structures, particularly the perirhinal cortex and hippocampus.

We considered whether the perirhinal cortex could support a fast recognition memory system independently from the hippocampus. We recorded the intracerebral EEG activity of epileptic patients while they were performing a fast visual recognition memory task, constraining them to use their quickest strategy. We performed ERP and classification analyses. The perirhinal cortex was, by far, the earliest region involved in recognition memory. This activity occurred before the first behavioral responses and was found to be related to reaction times, unlike the hippocampus.

Single-trial analyses showed that decoding power was equivalent in the perirhinal cortex and hippocampus but occurred much earlier in the perirhinal cortex. A critical finding was that recognition memory-related activity occurred in different frontal and parietal regions, including the supplementary motor area, before the hippocampus.

These results, based on ERP analyses, suggest that the human brain is equipped with a fast recognition memory system which may bypass the hippocampus and in which the perirhinal cortex plays a critical role.

INTRODUCTION

We invariably survey our visual world for subsumed information. One key item of information retrieved during this process is whether or not objects are familiar. The system backing this ability has a massive and detailed storage capacity [START_REF] Brady | Visual long-term memory has a massive storage capacity for object details[END_REF], can support very long-term memory (even for objects initially seen for only a few seconds [START_REF] Larzabal | Extremely long-term memory and familiarity after 12 years[END_REF])), and is also quite rapid as behavioral responses start to occur around 350-400 ms following presentation of the stimulus [START_REF] Besson | The speed of visual recognition memory[END_REF]. This shows that the ability to recognize familiar objects is a powerful natural cognitive system.

How the brain actually processes recognition memory is unclear. Two regions of the medial temporal lobes, namely the perirhinal cortex and the hippocampus, are critically involved in this ability, but their respective roles are contested. Lesions limited to the perirhinal cortex are sufficient to produce a severe loss in visual recognition memory tasks and this causes far more damage than that witnessed in any other single structure of the medial temporal lobes [START_REF] Meunier | Effects on visual recognition of combined and separate ablations of the entorhinal and perirhinal cortex in rhesus monkeys[END_REF][START_REF] Winters | Double dissociation between the effects of peri-postrhinal cortex and hippocampal lesions on tests of object recognition and spatial memory: heterogeneity of function within the temporal lobe[END_REF][START_REF] Bowles | Impaired familiarity with preserved recollection after anterior temporal-lobe resection that spares the hippocampus[END_REF]). In addition, the hippocampus is not needed to perform visual recognition memory tasks in patients with isolated lesions of this structure [START_REF] Aggleton | Amnesia and recognition memory: A re-analysis of psychometric data[END_REF][START_REF] Vargha-Khadem | Differential effects of early hippocampal pathology on episodic and semantic memory[END_REF][START_REF] Mayes | Relative sparing of item recognition memory in a patient with adult-onset damage limited to the hippocampus[END_REF][START_REF] Aggleton | Sparing of the familiarity component of recognition memory in a patient with hippocampal pathology[END_REF][START_REF] Barbeau | Visual recognition memory: A double anatomo-functional dissociation[END_REF][START_REF] Patai | Extent of hippocampal atrophy predicts degree of deficit in recall[END_REF][START_REF] Duzel | Brain activity evidence for recognition without recollection after early hippocampal damage[END_REF].

Contrasting results have, however, been reported in other apparently similar patients [START_REF] Manns | Recognition memory and the human hippocampus[END_REF]. To explain some of these conflicting results, some authors have suggested that the perirhinal cortex supports familiarity while the hippocampus is critical for recollection. However, intracerebral EEG (iEEG) recorded in the hippocampus of epileptic patients can be related to familiarity [START_REF] Merkow | The human hippocampus contributes to both the recollection and familiarity components of recognition memory[END_REF].

Likewise, familiarity neurons have been recorded in the hippocampus of human patients [START_REF] Rutishauser | Single-trial learning of novel stimuli by individual neurons of the human hippocampus-amygdala complex[END_REF], which contradicts the idea of a simple functional dichotomy between these structures.

This study adopts a completely novel approach to this debate. Given that the activity of the hippocampus is delayed by several tens of milliseconds compared to that of the perirhinal cortex [START_REF] Barbeau | Spatio temporal dynamics of face recognition[END_REF][START_REF] Mormann | Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe[END_REF][START_REF] Staresina | Memory signals are temporally dissociated in and across human hippocampus and perirhinal cortex[END_REF], we assumed that the perirhinal cortex could support a fast recognition memory system, bypassing the hippocampus. To identify this fast system, we used a specific recognition memory task requiring subjects to use their quickest strategy by applying a response deadline [START_REF] Besson | The speed of visual recognition memory[END_REF][START_REF] Besson | Fast, but not slow, familiarity is preserved in patients with amnestic mild cognitive impairment[END_REF][START_REF] Besson | From face processing to face recognition: Comparing three different processing levels[END_REF]. Two concurring hypotheses could be tested in this way. Firstly, the perirhinal cortex should show recognition memory activity before the quickest behavioral responses, while the hippocampus should show activity occurring too late to be related to these same behavioral responses. Secondly, the hypothesis of a fast recognition memory circuit bypassing the hippocampus also logically presupposes early activity related to recognition memory processes in brain regions other than the medial temporal lobes, to account for processes such as decision, confidence judgements and motor activity. We thus expected to observe early activity, i.e., earlier than that in the hippocampus, in the frontal [START_REF] Meunier | Effects of orbital frontal and anterior cingulate lesions on object and spatial memory in rhesus monkeys[END_REF][START_REF] Swick | Contributions of prefrontal cortex to recognition memory: electrophysiological and behavioral evidence[END_REF][START_REF] Bastin | Variability in the impairment of recognition memory in patients with frontal lobe lesions[END_REF]) and parietal lobes [START_REF] Gonzalez | Electrocorticography reveals the temporal dynamics of posterior parietal cortical activity during recognition memory decisions[END_REF][START_REF] Rutishauser | Single-neuron representation of memory strength and recognition confidence in left human posterior parietal cortex[END_REF].

MATERIALS AND METHODS

Patients

Fourteen patients with drug-refractory epilepsy were included in this study (6 women, median age: 34.5 years old, range: [21; 63], 8 right-handers and 2 ambidextrous). Fourteen healthy subjects were also included in order to compare behavioral performance (9 women, median age: 31 years old, range: [23; 66], all right-handers). Epileptic patients were admitted to the Epilepsy Monitoring Unit in Toulouse to identify their epileptic zone for possible subsequent resection. As noninvasive assessment failed to precisely indicate the spatial organization of the epileptogenic zone, these patients underwent depth electrode implantation using stereoelectroencephalography (SEEG). The placement of all electrodes was determined exclusively following clinical criteria. Each patient underwent specific implantation individually tailored to the seizure onset zone (i.e., patients did not systematically have electrodes implanted in the perirhinal cortex and hippocampus).

The depth electrodes had a diameter of 0.8 mm and contained from 8 to 18 platinum/iridium contacts, 2 mm long (Microdeep depth electrode, DIXI medical, France). Eight to 13 depth electrodes were implanted stereotaxically in each patient (Supplementary Table 1).

Prior to implantation, all patients underwent a high-resolution 3T T1-weighted volumetric MRI. After electrode implantation, they underwent a high-resolution CTscan. The pre-operative MRI and post-operative CT images were fused and normalized to the MNI brain atlas to pinpoint each contact exactly.

This study was approved by the local University Hospital Ethics Committee . Informed consent forms were signed for the implantation and use of EEG data for research purposes.

As we are aware that the speeded recognition memory task described below is highly demanding, we didn't propose it to patients whom we knew had cognitive difficulties (n=5). Some other patients were included but dropped out due to the difficulty of the task and, ultimately, weren't included in the final sample of patients (n=3). The performance of the group of patients ultimately included in the current study therefore does not reflect that of epileptic patients in general in such tasks.

Recordings

Intracerebral EEG activity was recorded using two synchronized 64-channel acquisition systems (SystemPlus Evolution, SD LTM 64 EXPRESS, Micromed, France) at a sampling rate of 256 Hz for patients 1 and 2 and either at 1024 or 2048 Hz for the others (respectively,463.3 Hz and 926.7 Hz; high pass-filter: 0.15 Hz). No patients had seizures in the 6 hours leading up to the recordings.

Experimental task

We used a visual recognition memory task, namely the Speed and Accuracy Boosting procedure (SAB), which required subjects to use their quickest strategy as assessed in previous studies [START_REF] Besson | The speed of visual recognition memory[END_REF][START_REF] Barragan-Jason | Fast and Famous: Looking for the Fastest Speed at Which a Face Can be Recognized[END_REF]. Each block began with an encoding phase during which 30 trial-specific stimuli (targets) were presented individually for at least 3s (self-paced) in the center of a grey screen.

Participants were explicitly instructed to remember all stimuli. We compared the duration of picture presentation during the encoding phase and found a mean ± SD of 1.39 ± 1.29 s for epileptic patients and 1.52 ± 1.24 s for healthy subjects (not significantly different). The encoding phase was followed by a distracting phase during which participants viewed a cartoon video with sound on for 3 minutes. The recognition memory phase followed, during which participants had to recognize the stimuli presented during the encoding phase, interspersed with 30 new stimuli (distractors) that they had never seen before. During this recognition phase, a short response deadline and an audio-feedback required subjects to answer as quickly and accurately as possible. Based on previous studies [START_REF] Besson | The speed of visual recognition memory[END_REF], we used a response deadline between 500 and 800 ms depending on the patient's cognitive ability. This did not change the minimal reaction time (Supplementary Table 1).

Responses were based on a go/no-go design. If a go response was made before the response deadline, a positive audio-feedback was played if the stimulus was a target (hit), and a negative one was played if it was a distractor (false alarm) (Fig. 1). If a no-go response was made, a positive audio-feedback was played if the stimulus was a distractor (correct rejection), and a negative one was played if the stimulus was a target (miss). Participants answered by raising their finger from an infrared response pad as quickly as possible. The SAB is demanding and required one or two short training sessions, which were not included in subsequent analyses. Patients underwent 7 to 10 SAB blocks.

Behavioral Performance Analyses

In order to evaluate the subject's performance, d prime (discrimination ability) was computed for each participant [START_REF] Rousselet | Is it an animal? Is it a human face? Fast processing in upright and inverted natural scenes[END_REF]. To estimate the minimal processing time required to recognize targets, the minimal behavioral reaction time (minRT) was computed by determining the latency at which correct go-responses (hits) started to significantly outnumber incorrect go-responses (false alarms) [START_REF] Besson | The speed of visual recognition memory[END_REF]. For each participant, we used 30 ms time bins and a Fisher's exact test (p < 0.05), followed by at least two significant consecutive bins.

ERP Processing

We used bipolar montage between adjacent macrocontacts to ensure that the signal recorded had a local origin [START_REF] Lachaux | Intracranial EEG and human brain mapping[END_REF]. After downsampling to 256 Hz, ERPs were computed off-line using the MATLAB toolbox EEGlab v12.0.2.6b [START_REF] Delorme | EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[END_REF]) between 0 and 800 ms with a pre-stimulus baseline of 200 ms. Preliminary visual inspection of the EEG as well as manual artefact rejection procedures were used to reject the interictal activity period (an average of 14% of all trials (range: [8%-22%]) were excluded across participants). This procedure decreases the risk of including ERPs modified by the patients' epilepsy. As ERP recordings in the perirhinal cortex and hippocampus have already been studied and published completely independently from the current study, by us (i.e., in other patient groups or clinical settings [START_REF] Barbeau | Spatio temporal dynamics of face recognition[END_REF][START_REF] Barbeau | Hippocampus duality: Memory and novelty detection are subserved by distinct mechanisms[END_REF]) or other groups [START_REF] Trautner | Recognition of famous faces in the medial temporal lobe: An invasive ERP study[END_REF], we were also able to compare these and verify that they were similar or showed similar differential latencies.

To precisely track the time course of visual recognition memory while limiting possible confounding issues due to multiple comparisons, we performed a paired two-tailed permutation test based on the tmax statistic [START_REF] Maris | Nonparametric statistical testing of EEG-and MEG-data[END_REF]. This analysis was used to compare ERPs for hits versus correct rejections.

Multivariate Pattern Analysis (MVPA)

MVPA was conducted on single-trial ERPs [START_REF] Cauchoix | The Neural Dynamics of Face Detection in the Wild Revealed by MVPA[END_REF]. A linear classifier was trained to discriminate between hits and correct rejections from singletrial individual time bins (4 ms) of the unfiltered iEEG signal across electrode subsets. Accurate measurements were derived by averaging the performance of the classifier over multiple random data splits. This type of decoding analysis characterizes temporal changes in the category signal. Each input (electrode potential) was normalized across trials in advance using normalization between 0 and 1. A linear support vector machine (SVM) classifier from liblinear [START_REF] Fan | LIBLINEAR: A Library for Large Linear Classification[END_REF]) was used as a classifier. The total number of hits and correct rejections was balanced in the database using random under sampling.

To avoid overfitting, we used a nested cross validation [START_REF] Pereira | Machine learning classifiers and fMRI: A tutorial overview[END_REF][START_REF] Granholm | A cross-validation scheme for machine learning algorithms in shotgun proteomics[END_REF]. We split our database into 3 inner folds and 3 outers folds.

The 3 inner folds were used to tune the C parameter of the model. Then the accuracy of the best model was evaluated on the remaining outer fold. This process was repeated until all outer folds were used as the test set. With such procedure, the algorithm is tested on the entire database.

This procedure was repeated 10 times for contact sub-sets of interest. A single measure of accuracy was obtained by averaging the classification performance over all repetitions. Chance levels were measured by performing the same analysis on permuted labels. This allowed the latency of recognition information to be estimated via a paired two-tailed permutation test based on the tmax statistic [START_REF] Maris | Nonparametric statistical testing of EEG-and MEG-data[END_REF], using a familiarity α-level of 0.05.

MVPA was computed on two adjacent contacts showing the highest amplitude in the perirhinal cortex and in the hippocampus (i.e., the same contacts that were used for the ERPs analyses, MNI coordinates available in Supplementary Table 2).

ERP Image

We explored trial-to-trial differences in event-related EEG epochs using ERPimage plots [START_REF] Delorme | Grand average ERP-image plotting and statistics: A method for comparing variability in event-related single-trial EEG activities across subjects and conditions[END_REF]. The brain activity for each trial was coded as a row of values in an ERP-image matrix. Then, row vectors for the single trials were stacked and smoothed vertically (moving vector smoothing = 30 for the perirhinal cortex and the hippocampus and 10 for the supplementary motor area), forming an ERP-image matrix of dimensions in terms of the number of epoch latencies x number of trials [START_REF] Delorme | Grand average ERP-image plotting and statistics: A method for comparing variability in event-related single-trial EEG activities across subjects and conditions[END_REF]. In order to relate cerebral activity with patient response, single trials were sorted by increasing reaction time latencies. We performed an analysis across patients by merging the different ERP images after amplitude normalization using Z-scoring and polarity reversal if needed (the main component of interest was usually negative in the perirhinal cortex and positive in the hippocampus in most patients as in previous studies [START_REF] Trautner | Recognition of famous faces in the medial temporal lobe: An invasive ERP study[END_REF][START_REF] Barbeau | Spatio temporal dynamics of face recognition[END_REF]; however, the position of the electrodes regarding the dipole can vary from patient to patient). These amplitudes normalized and polarity corrected ERPs were thus used for correlation analyses. As different patterns of correlations could be observed (see ERP image, Figure 4), two metrics were developed. In the perirhinal cortex, no correlation with corrected ERP onset was observed while a correlation with the length of the ERP was suggested. We thus computed the total time below 0 µV after the onset of the evoked response. We then averaged these durations per bin of 50 trials for all trials pooled across subjects for correlations with RTs (i.e., we created super trials to reduce the noise that may occur in a single trial [START_REF] Hebart | The representational dynamics of task and object processing in humans[END_REF]). In the supplementary motor area, a correlation with the corrected ERPs onset could be observed. We therefore used the onset of the evoked response as our metric and then averaged these onset latencies per bin of 20 trials as there were less trials overall, again pooled across subjects. Onset latencies of evoked responses were determined using the median rule method [START_REF] Letham | Statistically robust measurement of evoked response onset latencies[END_REF].

Other numbers of trials were tested for both areas without changing the results. A Pearson correlation coefficient was computed to assess the linear relationship between the evoked response duration and the reaction times in the perirhinal cortex or between the evoked response onset and the reaction time in the supplementary motor area. These same analyses were performed and are reported for the hippocampus for the sake of comparison, although no such correlation was observed. All correlations are reported in Supplementary Figure 1.

RESULTS

Behavioral Performance

A recognition memory task requiring subjects to use their quickest strategy was given to fourteen patients with drug-refractory epilepsy. To assess whether their performance was similar to that of healthy subjects, their ability to discriminate between targets and distractors (d') and their minimum reaction times (minRTs, i.e., the moment when the number of hits significantly outnumber the number of false alarms), were compared with those of fourteen healthy matched subjects. No difference was observed (d': median = 1.46 [0.92; 2.54] for epileptic patients and 1.46 [0.82; 2.84] for healthy subjects, Mann-Whitney test, U=93, p = 0.83; minRTs: median = 450 ms [390;510] for epileptic patients and 420 ms [390; 480] for healthy subjects, Mann-Whitney test, U=135, p = 0.08, Supplementary Fig. 2).

Dissociation Between the Perirhinal Cortex and the Hippocampus

We recorded typical and focal ERPs in the perirhinal cortex and the hippocampus of the patients [START_REF] Trautner | Recognition of famous faces in the medial temporal lobe: An invasive ERP study[END_REF][START_REF] Barbeau | Spatio temporal dynamics of face recognition[END_REF]) (see example in Fig. 2a). Differential activity between hits and correct rejections was recorded in these two structures. It occurred systematically earlier in the perirhinal cortex than in the hippocampus (median delay between the two structures = 273 ms, range = [137; 418] for 6 patients with electrodes, simultaneously in the two structures (2 with bilateral implantations), Wilcoxon test, W=0, p = 0.007, Fig. 2b).

We also used a multivariate pattern analysis (MVPA) [START_REF] Kamitani | Decoding the visual and subjective contents of the human brain[END_REF][START_REF] Cauchoix | The Neural Dynamics of Face Detection in the Wild Revealed by MVPA[END_REF] to investigate the information carried in each single trial to distinguish activity between hits and correct rejections. The MVPA was computed between hits and correct rejections on 2 adjacent contacts located in the perirhinal cortex (for 10 electrodes in 8 patients) and in the hippocampus (for 13 electrodes in 9 patients). Importantly, the decoding power recorded at the peak of maximum accuracy was similar between the perirhinal cortex and the hippocampus (median accuracy = 62%, Mann-Whitney test, U=88, p = 0.48) but occurred earlier in the perirhinal cortex compared to the hippocampus (median delay between both structures = 277 ms, Mann-Whitney test, U=0, p < 0.001) (Fig. 2c). Furthermore, the median decoding onset occurred 272 ms before the minRTs in the perirhinal cortex (range = [197; 417]), versus only 19 ms in the hippocampus (range = [-120; 68], Mann-Whitney test, U=150, p < 0.001; Fig. 2d). Critically, the decoding onset for MVPA was similar when applied only to the activity generated by the perirhinal cortex or the hippocampus or when applied simultaneously to activity from both the perirhinal cortex and the hippocampus (Mann-Whitney test, U=25, p = 0.50). The same finding was recorded when this analysis was carried out at the peak of maximum accuracy (U=35, p = 0.79). This implies that the interaction between these structures does not facilitate clearer differentiation of the activity related to hits and correct rejections, i.e., their contribution appears to be independent.

Spatio-Temporal Dynamics of Visual Recognition Memory Throughout the

Whole Brain

1450 contacts were recorded across the 14 patients and many areas of the brain displayed differential activity between hits and correct rejections, suggesting their involvement in recognition memory (Fig. 3a). In order to perform statistical comparisons, we selected a sub-set of brain regions according to the following criteria [START_REF] Barbeau | Spatio temporal dynamics of face recognition[END_REF]): (1) ERPs exhibiting similar waveforms and latencies in the same region; (2) being generated focally, as demonstrated by polarity reversal between adjacent contacts and/or steep voltage gradient and high voltage fluctuations between adjacent contacts; (3) presenting differential activity between hits and correct rejections; (4) reaching a minimum of 3 patients per region across our population of 14 patients to ensure consistency, with the exception of the supplementary motor area (SMA) due to the scarcity of electrode implantation in this structure. If several contacts in the same region matched these criteria, the contacts showing the earliest differential activity between hits and correct rejections were selected (this occurred for 7 electrode pairs and the mean difference between adjacent contacts was 13 ms (range: [8][9][10][11][12][13][14][15][16][17][18][19][20]). Contacts from both hemispheres were merged to increase power but are detailed in Supplementary Table 2.

A total of 74 bipolar contacts meeting these criteria were selected, corresponding to 10 different brain structures (Fig. 3b; MNI coordinates available in Supplementary Table 2 and details for each bipolar contact in Supplementary Fig. 3).

Looking at the onset of the differential activity between hits and correct rejections in each structure, we found that the perirhinal cortex was the first structure in the brain where a differential activity occurred. It also lasted the longest. It should be noted that electrodes were also regularly implanted in the posterior and middle ventral visual pathway but did not show differential activity, or only of late onset and inconsistently. Differential activity then occurred simultaneously in the supplementary motor area, the second fastest region after the perirhinal cortex, as well as in a number of frontal and parietal areas. The last structures in which differential activity was observed were other regions of the medial temporal lobes, such as the amygdala, hippocampus and temporal pole, as well as the anterior cingulum (Fig. 3c).

ERP Images

ERP images are plots of event-related single-trial activities ordered in this study in terms of increasing reaction times across patients (Fig. 4). In the present case, it reveals potential relationships between ERP components and reaction times. Different patterns could be observed for the perirhinal cortex, hippocampus and the supplementary motor area. The first negative potential of the ERP was time-locked to stimulus presentation in the perirhinal cortex, suggesting a partly exogenous response in this region, consistent with the idea that the perirhinal cortex processes sensory information and is strongly related to the visual ventral pathway. In this region, the duration of the first negative response correlated with reaction times. In the supplementary motor area in contrast, it was the onset of the negative response that appeared to be related to reaction times. In the hippocampus in contrast, no obvious relationship with reaction times could be observed. Complementary analyses are provided in Supplementary Figure 1.

DISCUSSION

In this study, we could identify a fast visual recognition memory system. The core component of this system is the perirhinal cortex. Moreover, the hippocampus is unlikely to play a role in this fast system.

Across the whole brain, the perirhinal cortex was the inceptive region displaying differential activity between hits and correction rejections in a specific recognition memory task. Such result is in accordance with many previous studies which have consistently shown an early differential activity of the perirhinal cortex or subhippocampal structures using intracerebral recordings by different groups and varying kinds of stimuli (Fernandez et al. 1999;[START_REF] Trautner | Recognition of famous faces in the medial temporal lobe: An invasive ERP study[END_REF][START_REF] Barbeau | Spatio temporal dynamics of face recognition[END_REF] or MEG [START_REF] Gonsalves | Memory strength and repetition suppression: multimodal imaging of medial temporal cortical contributions to recognition[END_REF]. The most notable and novel finding was that this differential activity reliably occurred much earlier (about 100 ms) than in any other brain region as assessed here through extensive recordings in the visual ventral stream as well as in the frontal and parietal lobes. This activity preceded behavioral responses and was found to be related to reaction times across patients.

The next fastest region was the supplementary motor area (SMA), whose activity correlated to reaction times. This region is involved in the motor planning and execution of the response [START_REF] Cona | Supplementary motor area as key structure for domain-general sequence processing: A unified account[END_REF]. These two regions thus defined a fast recognition memory system, which we could identify using a recognition memory task that required subjects to use their quickest strategy.

In contrast, the differential activity recorded in the hippocampus occurred remarkably late, about 270 ms after that of the perirhinal cortex, which strongly supports the idea that the perirhinal cortex is involved in a fast system while the hippocampus is involved in a different, much slower system. Furthermore, it appears that, in many instances, the differential activity in the hippocampus started too late to account for the fastest reaction times because the median difference between singletrial decoding onset and the minimum reaction time was about 20 ms. This means that, in some patients, the fastest behavioral responses occurred earlier than the onset of the differential activity in the hippocampus. The delay between the onset of the differential activity in the perirhinal cortex and the SMA was about 100 ms (Fig. 3c). As there is no reason, as far as we know, to suggest that the hippocampus is connected to the SMA by a faster route, this, in fact, indicates that much of the activity of the hippocampus occurs too late to be involved in most of the fastest behavioral responses. Importantly, decoding analyses indicated that the level of decoding was similar in both the perirhinal cortex and the hippocampus and that decoding involving both regions simultaneously did not increase decoding accuracy. This strongly suggests that the perirhinal cortex and the hippocampus make an independent contribution to recognition memory, albeit at different time frames.

Overall, it is highly unlikely that the hippocampus is involved in the fastest behavioral responses, as assessed in this study.

We initially predicted that the activity of the hippocampus occurred too late to account for the fastest behavioral responses. Hence, we should be able to identify differential activities in the frontal lobe areas occurring after the perirhinal cortex but before the hippocampus. The frontal lobes have been shown to be involved in recognition memory in various studies in humans and non-human primates [START_REF] Meunier | Effects of orbital frontal and anterior cingulate lesions on object and spatial memory in rhesus monkeys[END_REF][START_REF] Swick | Contributions of prefrontal cortex to recognition memory: electrophysiological and behavioral evidence[END_REF][START_REF] Bastin | Variability in the impairment of recognition memory in patients with frontal lobe lesions[END_REF]. We also expected to observe early activity in motor and sensory-motor areas in relation to behavioral responses, which in this study involved replying with movements of the fingers. This was indeed what we found. A number of areas in the frontal region, including the SMA, as well as the parietal lobes, in accordance with previous studies (~200-300 ms in [START_REF] Gonzalez | Electrocorticography reveals the temporal dynamics of posterior parietal cortical activity during recognition memory decisions[END_REF], displayed differential activity occurring earlier than that in the hippocampus. In fact, these frontal and parietal lobe areas also showed earlier differential activity than other medial temporal lobe areas, such as the amygdala and temporal pole, highlighting, within MTL structures, the specific and pivotal role of the perirhinal cortex in visual recognition memory. The participation of these frontal and parietal regions is also consistent with the concept of a fast recognition memory system activated before a second system related to hippocampal activity. In other words, it is consistent with a circuit of brain areas involved in fast recognition memory.

It may seem surprising that the delay between the perirhinal cortex and the hippocampus is so protracted. A simpler model could be that the perirhinal cortex initially processes familiarity, which would then be rapidly relayed to the hippocampus via the entorhinal cortex. The speeded recognition memory task we used may, in fact, have helped to show that these two structures belong to two completely different systems. This may not have been apparent in earlier electrophysiological studies in which no speed constraint was used [START_REF] Trautner | Recognition of famous faces in the medial temporal lobe: An invasive ERP study[END_REF][START_REF] Barbeau | Spatio temporal dynamics of face recognition[END_REF]. Overall, our findings reveal a two-stage model in which the perirhinal cortex is initially involved in the early detection of familiar objects followed by activation of other frontal and parietal cortical areas. This finding is consistent with the notion that the perirhinal cortex is the highest area in the hierarchy of the visual ventral stream whilst also being related to memory [START_REF] Ranganath | Two cortical systems for memory-guided behaviour[END_REF][START_REF] Tamura | Conversion of object identity to object-general semantic value in the primate temporal cortex[END_REF]. Essentially, this fast recognition memory system could have a strong ecological value, as this is the one potentially used to react at a glance. In our view, this perirhinal signal is automatic (i.e. the automatic result of ventral visual stream processes) and relatively cost-efficient compared to the computation necessary to retrieve more elaborate memories. In this sense, it may also differ from other signals required to solve more complex recognition memory tasks (e.g. Remember/Know or Process Dissociation Procedures).

Following this initial stage, the hippocampus and other MTL areas are activated, probably making a qualitatively different contribution to recognition memory as postulated in neurocomputational models [START_REF] Norman | Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach[END_REF], and possibly with greater confidence [START_REF] Rutishauser | Representation of retrieval confidence by single neurons in the human medial temporal lobe[END_REF]. These two stages may correspond to two fundamentally different cognitive entities: the fast system being related to an interaction with the world (i.e., processing of immediate relevant stimuli present in our environment), while the slower system would be related to the retrieval of other information from memory. The switch from attention paid to the external world to attention paid to the internal world may require major reorganization of the brain [START_REF] Brincat | Prefrontal Cortex Networks Shift from External to Internal Modes during Learning[END_REF][START_REF] Barbeau | Hippocampus duality: Memory and novelty detection are subserved by distinct mechanisms[END_REF]) and prove timeconsuming, hence the delay between the perirhinal cortex and hippocampus.

Most objects in this study were familiar in the sense that similar objects of the same basic level and same name had already been seen (by reference to the visual hierarchy of object processing). Our view, however, is that what subjects do when they have to recognize whether or not they've seen an object during the recognition stage is to recognize them at the exemplar (or individual item) level. This is perhaps most clear when we run the same type of recognition memory task using abstract pictures, many of which are similar. In this case, the minimum reaction times remain the same [START_REF] Besson | The speed of visual recognition memory[END_REF].

A question might be related to context integration taking place during the encoding stage. In our protocol, as in many others, the use of context during recognition is not particularly useful, as subjects remain in the same context than during the encoding stage (same room, same people in the room, external events minimized as much as possible, etc.). Time context is also reduced to a minimum as a mere sense of recency (rather than referring to a specific moment in time) is enough to recognize a stimulus as familiar in our experiment. Reference to context during memory tasks is often thought to depend on the hippocampus. The fact that the hippocampus doesn't seem to be necessary in our task seems to be in agreement with these interpretations. Overall, we think we assess in this study a very basic process underlying recognition memory.

Analyses in this study were limited to evoked responses and MVPA however, and the absence of an ERP signal in the hippocampus doesn't completely prove that this structure was not involved in fast recognition memory. Analyses of time frequencies [START_REF] Mormann | Phase/amplitude reset and theta-gamma interaction in the human medial temporal lobe during a continuous word recognition memory task[END_REF][START_REF] Gonzalez | Electrocorticography reveals the temporal dynamics of posterior parietal cortical activity during recognition memory decisions[END_REF][START_REF] Colgin | Rhythms of the hippocampal network[END_REF] or single neuron activity might indeed have revealed an earlier involvement of the hippocampus, although evidence is currently lacking to support such a hypothesis.

Activity in the gamma range could reveal differences between conditions that were not seen using averaged evoked responses, particularly if such activity is induced, as opposed to evoked [START_REF] Tallon-Baudry | Oscillatory gamma activity in humans and its role in object representation[END_REF]. Likewise, the association between the large and relatively slow components analyzed in this study and neuronal activity is not straightforward [START_REF] Buzsáki | The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes[END_REF]. It has also been suggested that the brain could process novelty even faster than shown in the current study [START_REF] Bunzeck | Reward Motivation Accelerates the Onset of Neural Novelty Signals in Humans to 85 Milliseconds[END_REF]). This magnetoencephalography study, however, did not allow analyzing clearly from which region, the frontal or temporal lobes, such rapid signal originated and whether this signal was related to conscious or unconscious forms or recognition. An early signal (250-300 ms) related to stimulus repetition has nonetheless been suggested to originate from the hippocampus [START_REF] Nahum | Rapid consolidation and the human hippocampus: Intracranial recordings confirm surface EEG[END_REF][START_REF] Raynal | Early signal from the hippocampus for memory encoding[END_REF], but this has been attributed to an encoding stage prior to consolidation rather than recognition per se. Complex, and early, interactions between the hippocampus and perirhinal cortex could also play a role in recognition processes [START_REF] Staresina | Memory signals are temporally dissociated in and across human hippocampus and perirhinal cortex[END_REF]. Therefore, overall, the current study supports the idea that rapid recognition memory does not require the hippocampus. However, future studies, possibly using novel experimental conditions [START_REF] Bunzeck | Contextual interaction between novelty and reward processing within the mesolimbic system[END_REF] or other EEG analyses, may reveal a more refined picture of the involvement of the hippocampus in the processes underlying fast recognition.

As we used a go/no-go task, some of the ERPs that we analyzed may have reflected motor preparation and movement, rather than familiarity and decision processes. It was the case, for example, in the SMA, where we found a strong correlation between the onset of single EEG components and reaction times. It is unlikely to be the case in the perirhinal cortex and hippocampus in contrast, as we never found any within subject correlations in these structures (although we usually had more than 100 trials per subject, suggesting it unlikely it is a problem of power).

Dedicated control tasks may help to disentangle this issue in future studies.

In this study, we intended to avoid reference to predominant familiarity/recollection dichotomy. We make no claim as to the cognitive processes used by our subjects in our fast recognition memory task, although there are indications that tasks relying on response deadlines mainly rely on familiarity [START_REF] Sauvage | Recognition memory: Adding a response deadline eliminates recollection but spares familiarity[END_REF][START_REF] Ranganath | Two cortical systems for memory-guided behaviour[END_REF] and that familiarity consistently occurs rapidly and before recollection [START_REF] Hintzman | Retrieval dynamics of recognition and frequency judgments: evidence for separate processes of familiarity and recall[END_REF][START_REF] Brown | Recognition memory: neuronal substrates of the judgement of prior occurrence[END_REF][START_REF] Brown | Recognition memory: what are the roles of the perirhinal cortex and hippocampus?[END_REF]. Indeed, as we have seen, familiarity signals or neurons can sometimes be identified in the hippocampus [START_REF] Rutishauser | Single-trial learning of novel stimuli by individual neurons of the human hippocampus-amygdala complex[END_REF][START_REF] Merkow | The human hippocampus contributes to both the recollection and familiarity components of recognition memory[END_REF], suggesting that familiarity may depend on different MTL structures. It has been suggested that familiarity may in fact be a poorly defined concept [START_REF] Hintzman | Research Strategy in the Study of Memory: Fads, Fallacies, and the Search for the "Coordinates of Truth[END_REF]. In this context, there have also been calls to abandon this, perhaps too simple, perirhinal/familiarity and hippocampus/recollection dichotomy [START_REF] Wixted | The medial temporal lobe and the attributes of memory[END_REF]. Ultimately, our approach, which departs from conventional theory, allowed us to identify a fast recognition memory system that may be more akin to neurophysiological brain systems.

Figure 1 -

 1 Figure 1 -Example of stimuli used during the encoding phase and illustration of the

Figure 2 -

 2 Figure 2 -Electrophysiological Activity Recorded in the Perirhinal Cortex and in the

Figure 3 -

 3 Figure 3 -Spatio-temporal dynamics of visual recognition memory throughout the

Figure 4 -

 4 Figure 4 -ERP images. Upper panel: single-trial ERP collapsed across subjects

ACKNOWLEDGEMENTS

This work was supported by the European Research Council, under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement N. 323711 (M4 project).

CONFLICT OF INTERESTS

ED is funded through a CIFRE Industrial Research Agreement (No. 2015(No. /1135) ) signed between Dixi Medical and the Centre de recherche Cerveau et cognition (CerCo). The remaining authors have no competing interests.


K T C A Peijnenburg
email: peijnenburg@science.uva.nl

O Kinne

Oldendorf / Luhe

E K Van Haastrecht

Ae C Fauvelot

Sagitta elegans and S. setosa are the two dominant chaetognaths in the North-East (NE) Atlantic. They are closely related and have a similar ecology and life history, but differ in distributional ranges. Sagitta setosa is a typical neritic species occurring exclusively above shelf regions, whereas S. elegans is a more oceanic species with a widespread distribution. We hypothesised that neritic species, because of smaller and more fragmented populations, would have been more vulnerable to population bottlenecks resulting from range contractions during Pleistocene glaciations than oceanic species. To test this hypothesis we compared mitochondrial Cytochrome Oxidase II DNA sequences of S. elegans and S. setosa from sampling locations across the NE Atlantic. Both species displayed very high levels of genetic diversity with unique haplotypes for every sequenced individual and an approximately three times higher level of nucleotide diversity in S. elegans (0.061) compared to S. setosa (0.021). Sagitta setosa mitochondrial DNA (mtDNA) haplotypes produced a star-like phylogeny and a uni-modal mismatch distribution indicative of a bottleneck followed by population expansion. In contrast, S. elegans had a deeper mtDNA phylogeny and a multi-modal mismatch distribution as would be expected from a more stable population. Neutrality tests indicated that assumptions of the standard neutral model were violated for both species and results from the McDonald-Kreitman test suggested that selection played a role in the evolution of their mitochondrial DNA. Congruent with these results, both species had much smaller effective population sizes estimated from genetic data when compared to census population sizes estimated from abundance data, with a factor of $10 8 -10 9 difference. Assuming that selective effects are comparable for the two species, we conclude that the difference in genetic signature can only be explained by contrasting demographic histories. Our data are consistent with the hypothesis that in the NE Atlantic, the neritic S. setosa has been more severely affected by population bottlenecks resulting from Pleistocene range shifts than the more oceanic S. elegans.

Introduction

Comparison of the population genetic composition of closely related species can be a powerful approach to investigate the importance of contrasting life history and ecological characteristics on a species' evolutionary history. Several studies have used this approach with marine fish (e.g., [START_REF] Arnaud | Mitochondrial DNA analysis of the genetic relationships among populations of scad mackerel (Decapterus macarellus,D. macrosoma, and D. russelli) in South-East Asia[END_REF][START_REF] Dudgeon | No apparent genetic basis to demographic differences in scarid fishes across continental shelf of the Great Barrier Reef[END_REF][START_REF] Fauvelot | Reductions in the mitochondrial DNA diversity of coral reef fish provide evidence of population bottlenecks resulting from Holocene sea-level change[END_REF], but to our knowledge only one study has used this approach with zooplankton [START_REF] Bucklin | Low mitochondrial diversity and small effective population sizes of the copepods Calanus finmarchicus and Nannocalanus minor: possible impact of climatic variation during recent glaciation[END_REF]. In that study the genetic composition of two planktonic copepods with different distributions, and different ecological and life history traits was compared, and results indicated that different levels of genetic diversity may be a legacy of contrasting effects of Pleistocene glaciations. Planktonic taxa are ideal for such comparative approaches because, by definition, they are unable to swim against major ocean currents (Van der Spoel and Heyman 1983) and hence, do not exhibit confounding differences in behaviour that often hamper comparisons of the impact of climatic shifts on a species' evolutionary history.

Chaetognaths are found in many marine habitats from coastal waters to the open seas. They are often highly abundant and are functionally very important in marine food webs [START_REF] Bone | Introduction and relationships of the group[END_REF]. Sagitta elegans Verrill, 1873 and S. setosa Mu¨ller, 1847 are the two dominant planktonic chaetognaths in the North-East (NE) Atlantic, and based on morphological and genetic evidence, appear to be very closely related. A recent revision of the phylum Chaetognatha placed the two species in the same genus 'Parasagitta' based on morphological similarities [START_REF] Bieri | Systematics of the Chaetognatha[END_REF], and a molecular phylogeny based on 28S ribosomal DNA sequences indicated a sister species relationship [START_REF] Telford | Evolution of 28S ribosomal DNA in chaetognaths: duplicate genes and molecular phylogeny[END_REF].

Sagitta elegans and S. setosa have similar ecological characteristics. They are both active predators on other zooplankton (mainly copepods) and demonstrate little dietary differentiation. Differences that have been observed mainly reflect differences in size (S. setosa is smaller and takes smaller prey) and prey availability associated with vertical distribution (S. setosa occurs higher in the water column, [START_REF] Øresland | Feeding of the chaetognaths Sagitta elegans and S. setosa at different seasons in Gullmarsfjorden, Sweden[END_REF][START_REF] Feigenbaum | Food and feeding behaviour[END_REF]. The two species also have similar life histories. Like all chaetognaths they are protandrous hermaphrodites (testes mature before the ovaries), and reproduction is with internal fertilisation occurring while eggs are in the ovary [START_REF] Pearre | Growth and reproduction[END_REF]. Cross-fertilisation is believed to be the rule in nature, although self-fertilisation has been observed in the laboratory [START_REF] Alvarin˜o | Chaetognaths[END_REF][START_REF] Dallot | Observations pre´liminaires sur la reproduction en e´levage du Chaetognathe planctonique Sagitta setosa Mu¨ller[END_REF]). Eggs of S. elegans and S. setosa are released in the sea and remain in the open water column [START_REF] Pearre | Growth and reproduction[END_REF]) and both species can lay several batches, sometimes over prolonged periods [START_REF] Dallot | Observations pre´liminaires sur la reproduction en e´levage du Chaetognathe planctonique Sagitta setosa Mu¨ller[END_REF][START_REF] Jakobsen | On the biology of elegans Verrill and Sagitta setosa J. Mu¨ller in Inner Oslofjord[END_REF]. This, together with evidence that different cohorts may be transported from elsewhere into a particular region, has made it difficult to estimate the number of breeding cycles per year. It has been suggested that S. elegans has between one and five, and S. setosa has between one and six breeding cycles in the NE Atlantic, with the number generally increasing southwards (reviewed in [START_REF] Pearre | Growth and reproduction[END_REF]. However, [START_REF] Jakobsen | On the biology of elegans Verrill and Sagitta setosa J. Mu¨ller in Inner Oslofjord[END_REF] and Øresland (1983; 1986) criticised Russell's (1932a, b) findings of multiple (five or six) breeding cycles in the English Channel and concluded that there is no conclusive evidence for more than a single generation per year, and that the number of cycles of S. elegans and S. setosa in different regions of the NE Atlantic should be regarded as an open question.

The main difference between S. elegans and S. setosa is related to their habitat preferences. Traditionally, the two species have been regarded as useful indicators of different water masses in the NE Atlantic, with S. elegans characterising the inflow of oceanic water and S. setosa characterising coastal water (e.g., [START_REF] Meek | On Sagitta elegans and Sagitta setosa from the Northumbrian Plankton, with a note on a trematode parasite[END_REF][START_REF] Russell | Hydrographical and biological conditions in the North Sea as indicated by plankton organisms[END_REF][START_REF] Fraser | The Chaetognatha and other zooplankton of the Scottish area and their value as biological indicators of hydrographic conditions[END_REF][START_REF] Bainbridge | Continuous plankton records: Contribution towards a plankton atlas of the North Atlantic and the North Sea. VII: Chaetognatha[END_REF]. This difference is also reflected in their distributions. Sagitta elegans has an extensive cold-water distribution, occurring in the upper 100-150 m in Arctic and Subarctic regions, and extending into the northern parts of the Atlantic and Pacific oceans (Fig. 1a, Alvarin˜o 1965;[START_REF] Pierrot-Bults | Distribution patterns in Chaetognatha[END_REF]. The species is sometimes referred to as 'distant neritic' because it usually has denser populations in nearshore areas [START_REF] Tokioka | Neritic and oceanic plankton[END_REF][START_REF] Pierrot-Bults | Macrozooplankton diversity: how much do we really know?[END_REF]. Sagitta setosa has a more restricted distribution with disjunct populations above continental shelf areas in the NE Atlantic, Mediterranean Sea, and Black Sea (Fig. 1a, Alvarin˜o 1965;[START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]). Thus, S. setosa can be regarded as a typically neritic species and S. elegans as a more oceanic one.

Neritic species, because of smaller and more fragmented populations are potentially more vulnerable to population bottlenecks resulting from climate-induced range shifts and compressions than more widely distributed oceanic species [START_REF] Angel | Biodiversity of the pelagic ocean[END_REF][START_REF] Grant | Shallow population histories in deep evolutionary lineages of marine fishes: Insights from sardines and anchovies and lessons for conservation[END_REF][START_REF] Arnaud | Mitochondrial DNA analysis of the genetic relationships among populations of scad mackerel (Decapterus macarellus,D. macrosoma, and D. russelli) in South-East Asia[END_REF]. In a recent study of mitochondrial diversity of S. setosa populations from the NE Atlantic, Mediterranean, and Black Sea [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF], it was shown that although levels of diversity were very high, there was significant evidence of population bottlenecks, particularly in the NE Atlantic and Black Sea. During Pleistocene glacials, the NE Atlantic was covered with land ice and polar desert [START_REF] Andersen | The ice age world[END_REF] and was therefore not habitable for marine plankton. Since S. elegans and S. setosa presently cooccur in the NE Atlantic, have a similar life history, and are closely related species, they provide a unique opportunity to compare the effect of differences in distributional range on the genetic impact of Pleistocene glaciations. Population size changes leave detectable patterns in the population genetic composition of species, which can be revealed by DNA sequencing and is called a genetic signature. For example, the distribution of nucleotide differences between all pairs of non-recombining sequence, such as mitochondrial DNA, is called a mismatch distribution. Population expansions produce a wave in this distribution creating a uni-modal pattern. By contrast, multi-modal distributions are expected for samples drawn from populations with constant population sizes [START_REF] Slatkin | Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations[END_REF][START_REF] Rogers | Population growth makes waves in the distribution of pairwise genetic differences[END_REF]. Also, the amount of genetic diversity within a population can provide information about the evolutionary effective population size and thus about a species' demographic history.

In this paper, we present DNA sequence data from the same mitochondrial fragment for S. elegans as for S. setosa (published in [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]) and compare the genetic signatures between these two species sampled across the NE Atlantic. We specifically test whether differences in genetic diversity are related to differences in population size of the two species and whether the genetic signatures differ, as would be expected if neritic species are more prone to bottlenecks than oceanic taxa.

Materials and methods

Sampling and molecular analysis

Individuals of S. elegans and S. setosa were collected from the NE Atlantic during two research expeditions on the Research Vessel 'Pelagia' of the Royal Netherlands Institute for Sea Research (NIOZ) in August-September 1999 (Cruise 64PE144) and September-October 2000 (Cruise 64PE169, Table 1). Samples from 11 locations were selected for analyses, eight included S. elegans and five included S. setosa (Fig. 1b, Table 1). Vertical hauls were taken from close to the bottom (except for station '99skag1' that was 450 m deep) to the surface using a ring net of 1 m in diameter and a 500 lm mesh size. Approximate relative abundances per haul are indicated in Table 1. These results are congruent with earlier reports that although both species co-occur in the NE Atlantic, S. elegans can be regarded as a species characteristic of the inflow of oceanic water and S. setosa as characteristic of coastal waters [START_REF] Russell | Hydrographical and biological conditions in the North Sea as indicated by plankton organisms[END_REF][START_REF] Bainbridge | Continuous plankton records: Contribution towards a plankton atlas of the North Atlantic and the North Sea. VII: Chaetognatha[END_REF]. All live chaetognaths were examined under a binocular microscope aboard ship. Only mature individuals in good condition that were clearly identified to species, and without visible gut contents or parasites, were individually stored in [START_REF] Alvarin˜o | Chaetognaths[END_REF][START_REF] Pierrot-Bults | Distribution patterns in Chaetognatha[END_REF][START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]). b Sampling localities of S. elegans and S. setosa from the North-East Atlantic (see alsoTable 1). Sample abbreviations used in a previous study by [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF] were N1=99skag1, N3=99gb1, N9=99sb3, N10=00atl1, andN11=00celt1 Table 1 Sagitta elegans and S. setosa sampling information, with sample abbreviations and regions (see also Fig. 1b) [START_REF] Holland | Cloning genes using the polymerase chain reaction[END_REF]). Molecular protocols are described in [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]. Thirty-seven sequences of the mitochondrial Cytochrome Oxidase II (COII) region of S. elegans were added to a previous dataset of 32 sequences of S. setosa from the NE Atlantic [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF], Table 1).

Data analysis

A 504 bp fragment was unambiguously aligned for all individuals of S. elegans and S. setosa (69 sequences in total) in DAMBE [START_REF] Xia | DAMBE: data analysis in molecular biology and evolution[END_REF]. DNA sequences were translated into amino acids using the invertebrate mitochondrial code to verify that these were protein coding genes, and not nuclear copies of mitochondrial DNA [START_REF] Bensasson | Mitochondrial pseudogenes: evolution's misplaces witnesses[END_REF]. We encountered two stop-codons and some gaps in S. elegans sequences, so we double-checked seven individuals by direct sequencing PCR products on an ABI 3100 Genetic Analyser (Applied Biosystems). We confirmed that the two substitutions resulting in stop-codons and all gaps were PCR/cloning artefacts, and estimated a PCR/cloning error rate of 0.004. For analyses, we used only sequences from a single clone for each individual and treated all gaps as missing data.

Because of high sequence variability, we examined the extent of mutational saturation (multiple substitutions at single nucleotide positions) in DAMBE by plotting the total number of transitions and transversions against [START_REF] Jukes | Evolution of protein molecules[END_REF] genetic distances for every pairwise sequence comparison and for all three codon positions. Sequence comparisons within S. setosa were not saturated, and slight saturation within S. elegans comparisons was observed for transitions at third codon positions only. Interspecific comparisons were saturated at all three codon positions.

Evolutionary relationships among all haplotypes were examined using a distance matrix approach in PAUP* [START_REF] Swofford | PAUP*, phylogenetic analysis using parsimony (*and other methods)[END_REF]. Uncorrected P-distances and corrected Maximum Likelihood distances (using the General Time Reversible Model [START_REF] Rodrı´guez | The general stochastic model of nucleotide substitution[END_REF]) with gammadistributed rates with shape parameter a=0.6647, as estimated in Modeltest, [START_REF] Posada | Modeltest: testing the model of DNA substitution[END_REF] were calculated for within and between species comparisons, respectively. The neighbour-joining (NJ) algorithm (Saitou and Nei 1987) was used to construct unrooted phylograms from distance matrices and the robustness of nodes was assessed with 1,000 NJ-bootstrap replicates.

We tested for population subdivision between different regions in the NE Atlantic (see Table 1) and between different years (1999 and 2000) for each species, using analyses of molecular variance (AMOVA, [START_REF] Excoffier | Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data[END_REF] as implemented in Arlequin [START_REF] Schneider | Arlequin ver. 2.000: a software for population genetics data analysis[END_REF]. The null hypothesis of genetic homogeneity among samples was tested using /-statistics (using uncorrected P-distances) and significance was assessed using 10,000 permutations.

To investigate levels of genetic diversity and the genetic signatures of S. elegans and S. setosa populations from the NE Atlantic, all sequences from different areas were pooled. Using DnaSP version 4.0 [START_REF] Rozas | DnaSP, DNA polymorphism analyses by the coalescent and other methods[END_REF], we calculated different estimators of genetic diversity, namely haplotype diversity (H, Nei 1987), nucleotide diversity (p, Nei 1987), and the average number of nucleotide differences between two sequences (k, [START_REF] Tajima | Evolutionary relationship of DNA sequences in finite populations[END_REF]. Additionally, the number of segregating sites (S), the number of mutations (g), and Watterson's theta (h w , based on S, Watterson 1975) were computed.

Estimates of genetic diversity were used to calculate effective population size assuming a neutral model of evolution [START_REF] Kimura | The neutral theory of molecular evolution[END_REF]. Under such a model, both p and h w are estimates of the population mutation rate; h = 2N e l (in the case of a mitochondrial gene in a diploid organism, where N e is the female effective population size (the number of breeding females in an idealised population, [START_REF] Nei | Molecular evolutionary genetics[END_REF], and l the mutation rate per generation). Hence, effective population size can be estimated if the mutation rate per generation is known. Note that in our case of hermaphroditic species, effective female population size is approximately equal to effective total population size when variance in reproductive success of males and females is similar. We used the conventional mutation rate of 2% divergence per million years for animal mitochondrial DNA [START_REF] Avise | Phylogeography. The history and formation of species[END_REF] and assumed a generation time of one year for both species (see Introduction).

We wished to compare our estimates of effective population size of S. elegans and S. setosa (based on genetic data) to estimated actual total population sizes. However, to our knowledge such estimations are unavailable for S. elegans and S. setosa. To roughly generate estimates we multiplied published data on average densities by our own estimates of the surface area and volume of known distributions. In the case of S. elegans, this included all marine habitats north of latitude 40°N, comprising the northern parts of the Atlantic and Pacific oceans and Arctic and Subarctic regions, to a maximum depth of 150 m (Fig. 1a). In the case of S. setosa, only the distribution area of the NE Atlantic population was considered because it is now isolated from populations in the Mediterranean and Black Sea [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]). Hence, this distribution comprised all neritic marine habitats between latitudes 45°N and 61°30¢N, and longitudes 12°W and 15°E, roughly ranging from the western Baltic to the continental seas of the NE Atlantic (Fig. 1a). Volumes were adjusted for varying water depths according to a grid of 0.5•0.5 degrees (H. van Aken, personal communication).

Demographic history of the two species was examined using mismatch distributions. For each species, we also computed two theoretical distributions using DnaSP, the first one as expected for an equilibrium population of constant population size, based on h w as estimated from the data, and the second one as expected under a sudden expansion model [START_REF] Rogers | Population growth makes waves in the distribution of pairwise genetic differences[END_REF]. This model considers a population that was formerly at equilibrium, but s generations ago suddenly expanded (assuming h 1 (after the expansion) to be infinite according to recommendations in DnaSP). To test whether one model fitted the observed data better than the other model, we calculated square deviations (SD) between frequencies of observed pairwise differences and their expectations under the two different models. We used a paired t-test to test for differences in SDs. If significant, we assumed that the model with the smaller sum of all SDs (SSD) best explained our data.

To test for neutrality we computed the commonly used Tajima's D statistic (Tajima 1989a) as well as the R 2 statistic (Ramos-Onsins and Rozas 2002), which use information from mutation frequencies. We chose the R 2 test because it was found to be the most powerful to reject neutrality in the case of population growth (Ramos-Onsins and Rozas 2002) and was suitable for our data. Using DnaSP, test statistics for each species were compared to their empirical distributions expected under the neutral model as generated by 10,000 coalescent simulations based on S. Additionally, the use of the same protein coding gene in two related species allowed us to specifically test for selection using a McDonald-Kreitman test [START_REF] Mcdonald | Adaptive protein evolution at the Adh locus in Drosophila[END_REF]. This test is based on a prediction from neutral theory that the ratio of replacement (non-synonymous) to silent (synonymous) fixed differences observed between species, should equal the ratio of replacement to silent polymorphisms within species. For instance, a significant excess of replacement fixations indicates positive divergent selection.

Results

General sequence characteristics

All 69 sequenced individuals of S. elegans and S. setosa represented unique haplotypes resulting in maximum haplotypic diversity. Nevertheless, we are confident that our sequences represent mitochondrial COII coding sequences, for the following three reasons: (1) we found a close match with the COII gene of the recently sequenced entire mitochondrial genome of the first representative of the phylum Chaetognatha, Paraspadella gotoi, by BLASTP searching Genbank (Accession Number YP054597, [START_REF] Helfenbein | The mitochondrial genome of Paraspadella gotoi is highly reduced and reveals that chaetognaths are a sister group to protostomes[END_REF]), (2) we did not encounter any double peaks, stopcodons, or gaps in direct sequences of the same fragment, and (3) restriction digests of PCR products did not show multiple banding patterns (data not shown). DNA sequences are deposited in Genbank under Accession Numbers AY585585-AY585616 (S. setosa from the NE Atlantic), AY585563, AY585564, and AY942657-AY942691 (S. elegans).

Most mutations were at third codon positions in both species (77.26% and 67.28% for S. elegans and S. setosa, respectively), but the relative proportions of transitions and transversions differed between the two species (57.52% and 91.36% transitions at third codon positions for S. elegans and S. setosa, respectively), reflecting saturation at third codon transitions observed for S. elegans only (see Material and Methods). Translation into amino acid sequences revealed that replacement mutations were common. From a total of 167 aligned amino acid positions, 17 were polymorphic in both species, 44 were polymorphic in S. elegans only, 17 in S. setosa only, 71 were fixed and shared between the two species, and 18 were fixed but different between the two species. These results suggest that similar regions of the COII fragment were conserved between the species, but sequences of S. elegans were much more variable compared to S. setosa, both at the nucleotide as well as the protein level.

Population structure

Uncorrected pairwise distances between haplotypes within S. elegans were larger than for S. setosa, ranging from 0.99% to 12.95% (average ± standard deviation, 6.30±2.74%) compared to a range of 0.20-5.56% (2.08±0.95%) for S. setosa. The average maximumlikelihood-corrected divergence between the two species was 77.70±3.45% (range 68.18-89.68%). Reconstructions of evolutionary relationships amongst haplotypes revealed no within-species spatial or temporal structuring of samples (Fig. 2). Similarly, AMOVA analyses revealed no significant within-species structuring (/ stvalues ranged from À0.02 to 0.004 between regions or between years for both species, all comparisons P>0.26). The unrooted NJ tree (Fig. 2) showed a starlike structure for S. setosa haplotypes (with the exception of a single deeper lineage from the German Bight, population sample '99gb1'). By contrast, the phylogeny of S. elegans haplotypes consisted of several well-supported subclades, indicating a deeper genealogical structure (Fig. 2).

Mitochondrial diversity and population size

Since we found no evidence of genetic differentiation in either species, all population samples within species could be grouped to estimate effective population sizes and conduct tests of selective neutrality and demographic history. The number of observed segregating sites (S) of S. elegans sequences was approximately twice that of S. setosa sequences (192 and 94, respectively), whereas nucleotide diversity (p) for S. elegans was about three times that for S. setosa (0.0612 and 0.0208, respectively, Table 2). Likewise, estimates of h w suggested a $2.6 times deeper genealogical structure in S. elegans compared to S. setosa (Table 2). Converting estimates of p and h w into effective population sizes of the two species resulted in a $2.6 to 3 times larger estimate for S. elegans than for S. setosa, assuming similar mutation rates for both species. Applying a standard mutation rate of 10 À8 mutations per generation, we estimated effective population sizes of 4.7-6.1•10 6 for S. elegans, and 2.1-2.3•10 6 for S. setosa (Table 2).

Estimates of effective population sizes were substantially smaller than our estimates of total population sizes based on the distribution and average abundances of the two species. Published estimates of the average density of S. elegans are 460 and 560 individuals per m 2 in the Celtic Sea [START_REF] Conway | Seasonal population structure, vertical distribution and migration of the chaetognath Sagitta elegans in the Celtic Sea[END_REF] and Arctic Ocean [START_REF] Choe | Seasonal vertical distribution and population dynamics of the chaetognath Parasagitta elegans in the water column and hyperbenthic zone of Conception Bay, Newfoundland[END_REF], respectively, and 1.4 and 6.8 individuals per m 3 in the SW North Sea [START_REF] Wimpenny | The distribution, breeding and feeding of some important plankton organisms of the South-West North Sea in 1934. I. Calanus finmarchius (Gunn), Sagitta setosa (J. Mu¨ller) and S. elegans (Verrill)[END_REF] and North Pacific [START_REF] Fager | Zooplankton species groups in the North Pacific[END_REF], respectively. Assuming an average abundance for S. elegans of 500/m 2 or 5/m 3 over its entire range, and an estimated distribution of 4.2•10 13 m 2 or 5.6•10 15 m 3 , we obtained an overall population size of 2.1•10 16 (based on area) or 2.8•10 16 (based on volume, Table 2). Compared to S. elegans, observed densities of S. setosa in the NE Atlantic were larger, $1,100/m 2 (Øresland 1983) and $55/m 3 (Wimpenny 1936). Using an overall average density of 1,000/ m 2 and 50/m 3 and an estimated distribution in the NE Atlantic of 1.0•10 12 m 2 or 6.6•10 13 m 3 , the overall population size of S. setosa was 1.0•10 15 (based on area) or 3.3•10 15 (based on volume, Table 2). Thus, both surface area and volume estimates of census population sizes were similar for both species, and S. elegans had an approximately 8-20 times larger census population size than S. setosa. Compared with effective population size estimates, our calculations of census population sizes were much larger, by a factor of 3.4-6.0•10 9 for S. elegans and 4.4-15.7•10 8 for S. setosa.

Demographic history and neutrality

Mismatch distributions differed strikingly between the two species (Fig. 3). The distribution of S. elegans was multi-modal with a mean of 29.87 pairwise differences and a distribution ranging from 5 to 62 differences. This is among the patterns that might be expected in a stable size population [START_REF] Slatkin | Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations[END_REF][START_REF] Rogers | Population growth makes waves in the distribution of pairwise genetic differences[END_REF]. Conversely, the mismatch distribution of S. setosa was less broad (between one and 28 pairwise differences and a mean of 10.41 differences) and had a uni-modal shape with the main peak at eight pairwise differences reflecting the star phylogeny found [START_REF] Wimpenny | The distribution, breeding and feeding of some important plankton organisms of the South-West North Sea in 1934. I. Calanus finmarchius (Gunn), Sagitta setosa (J. Mu¨ller) and S. elegans (Verrill)[END_REF], Fager and Mc-Gowan (1963), Øresland (1983), [START_REF] Conway | Seasonal population structure, vertical distribution and migration of the chaetognath Sagitta elegans in the Celtic Sea[END_REF] for this species (Fig. 2). This pattern is suggestive of a bottleneck followed by a sudden population expansion [START_REF] Slatkin | Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations[END_REF][START_REF] Rogers | Population growth makes waves in the distribution of pairwise genetic differences[END_REF]). An additional small peak was apparent at 24 differences, which resulted from the inclusion of a single representative of a deep clade (individual N3-3 in [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]). For S. elegans, no significant difference was found between the fit of the observed data to that expected under a stable population size model, or the sudden expansion model (paired t-test, df=58, P=0.98), whereas for S. setosa, the fit of the two models was significantly different (paired t-test, df=28, P=0.002). The lower SSD for the sudden expansion model indicated that this model provided the best fit to our data (Fig. 3). Based on this model, with parameters h 0 =3.51, h 1 =infinite, and s=6.9, we calculated that an expansion of the NE Atlantic population of S. setosa started approximately 685,000 generations ago (using the formula s=2ut, where u is the mutation rate of the entire sequence and t is the number of generations, Rogers and Harpending 1992).

The R 2 neutrality test resulted in a significant rejection of the neutral model for both species, with a lower R 2 value for S. setosa, which is typical of a recently expanded population (S. elegans: R 2 =0.0583, P<0.01; S. setosa: R 2 =0.0400, P<0.001). The commonly used Tajima's D test, however, did not reject neutrality for S. elegans, but did reject this for S. setosa (S. elegans: D=À1.315, P>0.10; S. setosa: D=À2.091, P<0.05). The McDonald-Kreitman test revealed a significantly higher ratio of replacement to silent fixed differences between species (35:38=0.921) compared to the ratio of replacement to silent polymorphisms within species (54:139=0.388), indicating positive selection of the COII gene during the evolution of these species (Fisher's exact test, P=0.003).

Discussion

Our study of mitochondrial COII sequences of two chaetognaths S. elegans and S. setosa from the NE Atlantic is one of the few studies that has examined genetic variation in zooplankton. High levels of genetic diversity combined with a lack of genetic structuring enabled us to contrast the two species' present genetic composition. The strength of this approach is that we compared the same gene for two systematically and Fig. 3 Mismatch distributions of pairwise sequence differences for Sagitta elegans (top) and S. setosa (bottom). Expected distributions under a model of constant population size and under a sudden population expansion model are indicated as computed in DnaSP. Sum of squared deviations (SSD) between the observed data and the two theoretical models are shown on the right as well as the P values of the paired t-test testing for a difference in fit between the two models and the observed data ecologically related species, which differed mainly in ranges.

Mitochondrial diversity

Sagitta elegans and S. setosa both possessed very high levels of genetic diversity with unique haplotypes for every sequenced individual and nucleotide diversities (p) of 0.061 and 0.021, respectively. This is surprising because COII is generally considered a conserved proteincoding gene (e.g., [START_REF] Simon | Evolution, weighting, and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved PCR primers[END_REF]. Compared to other studies of mitochondrial diversity in zooplankton, our estimates of p are among the highest ever reported. For example in pelagic crustaceans (copepods and euphausids) p values ranged from 0.002 to 0.018 [START_REF] Bucklin | Gene flow patterns of the euphausiid, Meganyctiphanes norvegica, in the NW Atlantic based on mtDNA sequences for cytochrome b and cytochrome oxidase I[END_REF][START_REF] Bucklin | Population genetics of drifting (Calanus spp.) and resident (Acartia clausi) plankton in Norwegian fjords[END_REF][START_REF] Bucklin | Low mitochondrial diversity and small effective population sizes of the copepods Calanus finmarchicus and Nannocalanus minor: possible impact of climatic variation during recent glaciation[END_REF][START_REF] Zane | Genetic differentiation in a pelagic crustacean (Meganyctiphanes norvegica: Euhausiacea) from the North East Atlantic and the Mediterranean Sea[END_REF][START_REF] Papadopoulos | Phylogeography of the calanoid copepods Calanus helgolandicus and C. euxinus suggests Pleistocene divergences between Atlantic, Mediterranean, and Black Sea populations[END_REF]. However, these studies are not strictly comparable because different mitochondrial genes were used, which may differ in mutation rate. High levels of genetic diversity are often explained by large population sizes [START_REF] Nei | Molecular evolutionary genetics[END_REF][START_REF] Avise | DNA fingerprints from hypervariable mitochondrial genotypes[END_REF] and the enormous population sizes of zooplankton, which are among the largest in the animal kingdom [START_REF] Bucklin | Low mitochondrial diversity and small effective population sizes of the copepods Calanus finmarchicus and Nannocalanus minor: possible impact of climatic variation during recent glaciation[END_REF][START_REF] Norris | Pelagic species diversity, biogeography, and evolution[END_REF] may explain the high levels of diversity that we found. A small fraction of the diversity may also be explained by PCR/cloning artefacts. For example, while assuming an error rate of 0.004 (see Materials and Methods) a maximum of 5% of the pairwise comparisons for S. setosa may have been between identical haplotypes, but none of the pairwise comparisons for S. elegans.

Based on p and k, which are corrected for sample size, mitochondrial diversity was about three times higher for S. elegans than for S. setosa. Comparing our results with COII variability in different populations of S. setosa from [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF] indicated lower estimates of nucleotide diversity of the NE Atlantic population of S. setosa (0.021) compared to Mediterranean populations (0.026 and 0.027) but higher estimates compared to the Black Sea population (0.015). Sagitta elegans displayed higher levels of nucleotide diversity (0.061) than any of the S. setosa populations. This finding is congruent with our estimates of a $8-20 times larger census population size of S. elegans compared to S. setosa.

Effective versus census population size

We found a large discrepancy between effective population sizes and our estimates of total population sizes (4.4•10 8 -6.0•10 9 -fold smaller effective than census population sizes). Even though both estimates depend on several assumptions that are likely to be violated (see below) and should therefore be interpreted with caution, the difference is striking and unlikely to be an artefact of the method. For instance, the estimation of effective population size depends on the assumption that the standard 2% clock is correct for the evolution of chaetognath mtDNA. In addition, estimates of effective population size depend on estimates of h, which assume an idealised population and a neutral model of evolution. The fact that our estimates of effective population size based on h w and p differ (Table 2) suggests that one or more assumptions of the neutral model are invalid (see also next section). However, there exists no alternative for the neutral model to estimate effective population sizes. The estimation of census population size for the two species is also very approximate because of the scarcity of data on species abundances in different seasons and across their entire geographical ranges.

Nevertheless, a large discrepancy between effective and census population size has commonly been observed in marine species [START_REF] Grant | Shallow population histories in deep evolutionary lineages of marine fishes: Insights from sardines and anchovies and lessons for conservation[END_REF][START_REF] Avise | Phylogeography. The history and formation of species[END_REF]. For zooplankton, large discrepancies were reported for Antarctic krill ($10 9 -fold difference, [START_REF] Zane | Molecular evidence for genetic subdivision of Antarctic krill (Euphausia superba Dana) populations[END_REF] and two North Atlantic copepods ($10 10 -fold difference, [START_REF] Bucklin | Low mitochondrial diversity and small effective population sizes of the copepods Calanus finmarchicus and Nannocalanus minor: possible impact of climatic variation during recent glaciation[END_REF]. Explanations for these discrepancies, none of which are mutually exclusive, have included: (1) a large variance in reproductive success among individuals, (2) overlapping generations, (3) extinction and recolonisations of local subpopulations, (4) historical fluctuations in population size, and (5) selective sweeps. The first explanation is very likely for species with high fecundities such as chaetognaths, resulting in huge variances in progeny numbers among families with consequent lowering of effective population size [START_REF] Hedgecock | Does variance in reproductive success limit effective population sizes of marine organisms?[END_REF][START_REF] Avise | DNA fingerprints from hypervariable mitochondrial genotypes[END_REF]. Overlapping generations may also be possible for S. elegans and S. setosa because it is known that both species can lay several batches of eggs and sometimes over prolonged periods [START_REF] Dallot | Observations pre´liminaires sur la reproduction en e´levage du Chaetognathe planctonique Sagitta setosa Mu¨ller[END_REF][START_REF] Jakobsen | On the biology of elegans Verrill and Sagitta setosa J. Mu¨ller in Inner Oslofjord[END_REF]. Even though we assumed a single generation per year for each species, the exact number of breeding cycles in different regions of the NE Atlantic is still a topic of discussion [START_REF] Pearre | Growth and reproduction[END_REF]. The third explanation of local subpopulations [START_REF] Slatkin | Gene flow in natural populations[END_REF]Whitlock andBarton 1997, Avise 2000) may be applicable to zooplankton populations as they are often patchily distributed. However, we found no evidence of population structuring over evolutionary timescales for the two chaetognaths in the NE Atlantic, which renders this possibility less likely. The fourth and fifth explanations of historical fluctuations in population size and selective sweeps have been hypothesised for many northern temperate species that are affected by Pleistocene glaciations (e.g., [START_REF] Hewitt | Some genetic consequences of ice ages, and their role in divergence and speciation[END_REF] and it has been shown that they can have significant effects on the genetic composition of even highly abundant marine species [START_REF] Avise | Current versus historical population sizes in vertebrate species with high gene flow: a comparison based on mitochondrial DNA lineages and inbreeding theory for neutral mutations[END_REF][START_REF] Grant | Shallow population histories in deep evolutionary lineages of marine fishes: Insights from sardines and anchovies and lessons for conservation[END_REF][START_REF] Bucklin | Low mitochondrial diversity and small effective population sizes of the copepods Calanus finmarchicus and Nannocalanus minor: possible impact of climatic variation during recent glaciation[END_REF]. However, the genetic signature of a population bottleneck followed by an expansion may be very similar to that of a selective sweep in a constant-sized population (Tajima 1989b;[START_REF] Donnelly | Coalescents and genealogical structure under neutrality[END_REF], making the two difficult to disentangle. Though results of different neutrality tests varied, the McDonald-Kreitman test, which was specifically designed to test for selection [START_REF] Mcdonald | Adaptive protein evolution at the Adh locus in Drosophila[END_REF][START_REF] Brookfield | Neutralism and selectionism face up to DNA data[END_REF][START_REF] Skibinski | DNA tests of neutral theory: applications in marine genetics[END_REF], highly significant evidence for positive selection on the COII gene during the evolution of S. elegans and S. setosa. It has been suggested, however, that the significance may be inflated because sequences of the two species are saturated with respect to each other (see [START_REF] Whittam | Neutral mutation hypothesis test[END_REF]. Thus, assumptions of the neutral model are violated for both species and may be explained by bottlenecks as well as directional selection.

Demographic history

The genetic signatures of S. elegans and S. setosa are strikingly different when depicted as phylograms (Fig. 2) and mismatch distributions (Fig. 3). Sagitta setosa had mtDNA haplotypes showing a star-like phylogeny and a uni-modal mismatch distribution, whereas S. elegans had a much deeper mtDNA phylogeny and a multimodal mismatch distribution. Although selection has probably played a significant role in determining the two species' present genetic composition, it is not a likely explanation for the difference in genetic signature between the two species because the two chaetognaths are biologically very similar. Assuming that selective effects have been the same during the evolutionary history of the two species, the deeper genealogical structure of S. elegans compared to S. setosa may be explained if: (1) S. elegans is a much older species than S. setosa, (2) genetically differentiated groups were sampled for S. elegans and not for S. setosa, and (3) S. elegans was less affected by bottlenecks compared to S. setosa. The first possibility of S. elegans being a much older species than S. setosa is unlikely because based on a molecular species phylogeny, [START_REF] Telford | Evolution of 28S ribosomal DNA in chaetognaths: duplicate genes and molecular phylogeny[END_REF] suggested a rapid, recent radiation of different groupings within the genus 'Sagitta', which included the grouping 'Parasagitta' to which both species belonged. The second possibility of genetically differentiated groups within S. elegans may be important in the light of three subspecies that have been described; one from the Arctic (S. elegans arctica Aurivillius, 1896), one from the Subarctic (S. elegans elegans Verrill, 1873), and another from the Baltic (S. balthica Ritter-Zahony, 1911). However, most authors consider these as local forms representing only variability in size, which may be a phenotypically plastic response to the water temperature in which they develop [START_REF] Fraser | The Chaetognatha and other zooplankton of the Scottish area and their value as biological indicators of hydrographic conditions[END_REF][START_REF] Alvarin˜o | Chaetognaths[END_REF][START_REF] Pierrot-Bults | Distribution patterns in Chaetognatha[END_REF]. Moreover, all our samples were taken from areas where only the variant 'elegans' had been reported and we did not observe any apparent differences in the sizes of sampled individuals. It may be possible though, that S. elegans is genetically structured over broader geographical scales than we sampled, and that divergent lineages have colonised the NE Atlantic through occasional dispersal. One earlier study using allozymes to study the genetic structure of S. elegans from the waters off Japan suggested subtle differentiation between populations, but results were contradictory [START_REF] Thuesen | Genetic variation in the planktonic chaetognaths Parasagitta elegans and Eukrohnia hamata[END_REF]. A more broad-scale genetic survey of S. elegans populations is necessary to reveal whether the entire species can be regarded as panmictic or not. Our lack of evidence for genetic structuring of populations in the NE Atlantic, however, taken together with other studies of so-called 'high gene flow' species occurring above the shelf in this region [START_REF] Borsa | Genetic structure of the flounders Platichthys flesus and P. stellatus at different geographic scales[END_REF][START_REF] Shaw | Subtle population structuring within a highly vagile marine invertebrate, the veined squid Loligo forbesi, demonstrated with microsatellite DNA markers[END_REF][START_REF] Hoarau | Population structure of plaice (Pleuronectes platessa L.) in northern Europe: microsatellites revealed large-scale spatial and temporal homogeneity[END_REF][START_REF] Hoarau | Population structure of plaice (Pleuronectes platessa L.) in northern Europe: a comparison of resolving power between microsatellites and mitochondrial DNA data[END_REF] suggest that population structuring in the NE Atlantic of S. elegans is unlikely to be the case.

The evidence suggests that the difference in genetic signature of S. elegans and S. setosa is most likely due to a difference in demographic history. We found a clear genetic signature of a population bottleneck followed by a sudden expansion for S. setosa, but not for S. elegans. Reconstructions of seawater temperatures and current patterns in the North Atlantic during the last glacial maximum (18,000 years ago) based on foraminiferal and coccolithophorid assemblages, show that northern temperate conditions were compressed and shifted southwards compared to present conditions [START_REF] Mcintyre | Glacial North Atlantic 18,000 years ago: a CLIMAP reconstruction[END_REF][START_REF] Angel | Zoogeography of the Atlantic Ocean[END_REF]). If we assume that distributional ranges of S. elegans and S. setosa were similar during warm periods (interglacials) of the Pleistocene compared to the present, then both species were severely affected during glacial periods, with drops in temperature exceeding 10°C [START_REF] Mcintyre | Glacial North Atlantic 18,000 years ago: a CLIMAP reconstruction[END_REF] and drops in sea level of at least 85 m (CLIMAP 1976) resulting in a complete covering with pack ice of the NE Atlantic shelf. Our results support the hypothesis that a neritic species such as S. setosa, which presently occurs only above continental shelf areas, is more affected by bottlenecks resulting from repeated glaciations compared to a more oceanic species with a wider distribution pattern such as S. elegans. This is presumably because the distribution of S. setosa was not just severely compressed, but ultimately completely displaced. By contrast, even though populations of S. elegans were also severely compressed, they were not completely displaced, and may have been buffered against bottlenecks because populations were larger, continuous, and more extensive. We estimated from our mitochondrial data that a major demographic expansion of the NE Atlantic population of S. setosa started approximately 685,000 generations ago, during the mid-Pleistocene (assuming one generation per year). However, such estimates should be regarded as very approximate because they are based on a simple demographic model, there is a large variance associated with estimates of s from a single locus, and estimates are highly reliant on the estimate of mutation rate and generation time of the organism.

In summary, our study of mitochondrial variation in two chaetognaths contributes to the existing literature of other superabundant and high gene flow species that long term evolutionary (effective) population sizes may be relatively small. The comparison of two species with different distribution patterns has shown that planktonic species may be differentially affected by geological events such as ice ages because of differences in ecolog-ical traits. Furthermore, our study adds to growing body of literature, suggesting that mitochondrial markers should not be regarded as strictly neutrally evolving (e.g., reviewed in [START_REF] Ballard | The incomplete natural history of mitochondria[END_REF]).

Fig. 1 a

 1 Fig. 1 a Distribution of Sagitta elegans and S. setosa shown by different hatching patterns (based on maps in Alvarin˜o[START_REF] Alvarin˜o | Chaetognaths[END_REF][START_REF] Pierrot-Bults | Distribution patterns in Chaetognatha[END_REF][START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]). b Sampling localities of S. elegans and S. setosa from the North-East Atlantic (see alsoTable 1). Sample abbreviations used in a previous study by[START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF] were N1=99skag1, N3=99gb1, N9=99sb3, N10=00atl1, and N11=00celt1

Table 2

 2 Genetic diversity estimators and estimated census and effective population sizes for Sagitta elegans and S. setosa

	Estimates of average densities
	of the two chaetognath species
	are based on results from

Acknowledgements We thank all crew of the research vessel Pelagia, R. ter Hofstede, and J.-H Schotveld for their help in taking plankton samples and C. ten Hallers-Tjabes for inviting KP, and H. van Aken for inviting KP and EH aboard their research cruises. H. van Aken is also thanked for his calculations of the areas and volumes of the distributions of the two chaetognaths. W. van Ginkel, P. Kuperus, and B. Voetdijk are acknowledged for laboratory assistance and J. van Arkel for his help with figures. We thank G. Lecaillon, P. Meirmans, A. Pierrot-Bults, and A. Southward for discussion. Comments of J. Breeuwer, M. Egas, M. Genner, P. Luttikhuizen, and S. Menken improved the manuscript. This research was funded by the Netherlands Organisation for Scientific Research (NWO) as part of the priority program 'Sustainable use and conservation of marine living resources' project number 885.100.02. Research was carried out in accordance with all laws and regulations of the Netherlands.