migratory behaviour whilst at sea (Kristensen et al., 2018; *Lança et al.*, 2014; Righton et al., 2016). In addition, certain gear types (e.g., trawls and static nets), can catch fish within both the demersal and pelagic water zones (He et al., 2021; Borges et al., 2008). Higher detectability from certain gear types were, however, observed. For example, shad were mainly caught by demersal mobile gear and static gear, salmonids static and pelagic mobile gear types, and *P. flesus* and *O.eperlanus* were largely captured by demersal and benthic mobile gear. These results broadly match the water column habitat occupancy these species are known to occupy and existing literature (ICES, 2005; Wilson and Veneranta, 2019). As it is thought that lampreys detach from their host upon capture (Elliott et al., 2021; Halliday, 1991), bycatch is likely to be minimal for these species.

To improve understanding of bycatch risk on a spatial level, fishing intensity by gear type should be mapped, ideally using Vessel Monitoring System data and overlaying it with the predicted distributions (Elliott et al., 2018; Quenmerais-Amice et al., 2020). Unfortunately, this information was not available to us. Additionally, since the fisheries observer data we had access to is only a small sample of the existing French fishing activity (Comou et al., 2015), and it does not include other countries fishing effort, results would be biased.

## 4.4. MPA relevance

To improve protection of diadromous fish at sea, numerous mechanisms could be implemented. For example, identifying areas of high bycatch risk with measures to limit bycatch within these areas, improving diadromous fish migratory pathways, and spatial protection measures (Verhelst et al., 2021; Wilson and Veneranta, 2019). Few MPAs within European waters protect marine fish and instead focus on protecting seabed features with a few exceptions (e.g., protected seabirds and marine mammals). Therefore the only protection measure that exists for vulnerable marine fish is through the removal of targeted fishing (Dureuil et al., 2018; Probst et al., 2021; Stratoudakis et al., 2016). There is, however, increasing evidence that MPAs may have positive impacts on fish populations if appropriate management measures are implemented (Davies et al., 2021; Moland et al., 2013; Probst et al., 2021; Worm et al., 2006). The value of protected areas for highly migratory species has been questioned, but there is evidence that MPAs may have positive impacts on migratory species (Pendoley et al., 2014; Takashina and Mougi, 2014).

Our results show relatively high core area presence of protected diadromous fish within Habitat Directive MPAs. Most marine Sites of Community Importance have not been designated to protect Habitat Directive listed diadromous fish. Nonetheless, our results highlight the value of these MPAs for diadromous fish protection. Specific management measures limiting threats (e.g., limiting access to gear types with higher probability of capture) to diadromous fish within these areas, would be of benefit to their protection (Domisch et al., 2019; Stratoudakis et al., 2016). Detailed analysis of Sites of Community Importance and other categories of designated MPAs should be undertaken to evaluate their potential to protect diadromous fish.

## 4.5. Conclusion

Understanding the spatial distribution of species and their habitats is essential for effective management and conservation (Halpern et al., 2015; Worm et al., 2009, 2005). Much research has been carried out on the conservation of freshwater diadromous fish (Drouineau et al., 2018; Merg et al., 2020; Verhelst et al., 2021). Very little research has been dedicated to the value of marine and estuary habitats for the conservation of these species and their risk of bycatch (Feunteun, 2002; Flitcroft et al., 2019; Lin et al., 2017). Here we provide an insight into the distribution and bycatch of diadromous fish at sea.

Despite diadromous fish vulnerability, targeted fishing within estuaries for numerous species remains (Aprahanian and Walker, 2008; Castelnau, 2000; Feunteun, 2002; Kappel, 2005; Stratoudakis et al., 2020). Given the very coastal distribution of these species, more detailed analysis into such catch data (small fishing vessels targeting diadromous fish) is required. Smaller vessels and artisanal fisheries are still largely overlooked, regardless of their potential threat to diadromous fish (Beaulaton et al., 2008; Castelnau, 2000; Stratoudakis et al., 2016). Having a better understanding of fishing pressure, combined with the outputs of the distributions and gear capture from these models, could help improve understanding of fishing impacts on diadromous fish.

Finally, it has been well acknowledged that although a large impedes for the designation of MPAs has been undertaken in the last two decades (Probst et al., 2021; Worm et al., 2006). The implementation of management measures has been slow to put in place (Dureuil et al., 2018; Probst et al., 2021; Stratoudakis et al., 2016). Furthermore, little has been undertaken to protect diadromous fish within marine ecosystems (Dureuil et al., 2018; Probst et al., 2021; Stratoudakis et al., 2016). We highlight the value of Habitat Directive MPAs that have the jurisdiction to protect diadromous fish. Limiting access of fisheries with a higher probability of capture to such areas could provide additional protection for diadromous fish and their habitats.

## United references

Almeida, 1996; Elliott and Dewailly, 1995; Worm et al., 2005; Aprahanian et al., 1998; Freyhof and Brooks, 2011; Lassalle, 2021; Legrand et al., 2020; Nieto et al., 2015.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

The data will be made available as part of the data in brief

## Acknowledgements

We thank the funders of the project Management of Diadromous Fish in their Environment, OFB-INRAE-Institute Agro-UPPA. We are extremely grateful to all those who were involved in collecting and compiling the fisheries-dependent and fisheries-independent data and their funders. We are also grateful to IFREMER and the French marine fisheries and aquaculture administration (DPMA) for access to their data. Finally, thanks the reviewers for their valuable contribution.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pocean.2022.102924.

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Domisch, S., Friedrichs, M., Hein, T., Borgwardt, F., Wettig, A., Jahnig, S.C., Langhans, S.D., 2018. Spatially explicit species distribution models: A missed opportunity in conservation planning? Divers. Distrib. 25, 758-769. https://doi.org/10.1111/ddi.12891.

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Accurately predicting rare and poorly detectable species
habitat for spatial protection

<table><tr><td>Journal:</td><td>Journal of Applied Ecology</td></tr><tr><td>Manuscript ID</td><td>JAPPL-2023-00091.R2</td></tr><tr><td>Manuscript Type:</td><td>Research Article</td></tr><tr><td>Key-words:</td><td>conservation planning, diadromous fish, poorly detectable species, protected areas, rare species, spatial protection, Species distribution models</td></tr></table>

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CMAP_Afallax_006_230929.Rmd

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Confidential Review copy
# 1 ABSTRACT

21. With the loss of biodiversity worldwide, understanding species distribution is

3 essential for species management, but modelling the distribution of rare and poorly

4 detectable species can be challenging because of data gaps and observer biases.

5 Over- or under-predictions are frequent, leading to uncertainty in spatial

6 management measures, particularly for highly mobile and data-poor species.

7

82. Here we developed a ‘Combined Model for Accurate Prediction’ to accurately

9 predict the distribution of data-poor and rare species. This modelling framework

10 aims to improve the accuracy of both predicted ‘core’ and ‘unsuitable’ habitats, to

11 help support managers with spatial protection measures. We tested the combined

12 modelling approach on 11 data-poor and rare diadromous fish during their at-sea

13 life history phase and used the combined model to analyse the adequacy of existing

14 Marine Protected Areas (MPAs) for these fish.

15

163. The combined modelling approach modelled both ‘core’ and ‘unsuitable’ habitats

17 with high accuracy. Of the seven diadromous species modelled, most MPAs

18 designated to protect diadromous fish are outside their core habitats. Furthermore,

19 when their core habitat was within an MPA, only 50% of this area was designated to

20 protect them. These results highlight inadequate protection of the existing

21 networks of MPAs for protected and threatened species.

22

234. Synthesis and applications. Being able to accurately model species distribution is

24 critical to reliable and transparent biodiversity and conservation assessments. By

25 modelling accurate ‘core’ and ‘unsuitable’ habitats with models that minimise
26 omission and commission rates respectively, conservation measures could be

27 targeted in specific spatial areas that maximise the protection of rare and poorly

28 detected species. This method therefore helps minimise impacts on stakeholders,

29 whilst providing managers with increased confidence in the model predictions.

30

31 Keywords: conservation planning, diadromous fish, poorly detectable species,

32 protected areas, rare species, spatial protection, Species distribution models.

33

341. Introduction

35 Worldwide, the loss of biodiversity has become a global concern. Protected areas

36 and harvest control measures have become the primary methods to conserve

37 species (Guillera-Arroita, 2017; Robinson et al., 2011; Sofaer et al., 2019). To

38 adequately protect species, an accurate understanding of their distribution is

39 imperative (Breiner et al., 2015; Robinson et al., 2011). Yet frequently this

40 information is missing for rare species whose population and distribution have been

41 reduced (Rabinowitz et al, 1981; IUCN, 2012), and data-poor species with too little

42 information to determine their exploitation status (Bean et al., 2012; Berkson &

43 Thorson, 2015; Breiner et al., 2015; Lomba et al., 2010). Adding to the problem,

44 with increasing demands on our resources, species management can often be

45 inadequate because of resource conflict (Halpern & Warner, 2003; Higginson, 2017;

46 Hilborn, 2007; Wauchope et al., 2022). Minimising displacement of anthropogenic

47 activities can reduce potential spatial conflicts and costs related to species

48 management (Guisan & Zimmermann, 2000; Halpern & Warner, 2003; Hilborn,

49 2007; Lopes et al., 2019). To identify potential conflict areas, determining the

50 distribution of the species is critical. For example, Species Distribution Models
51 (SDMs) can help understand key migratory corridors, outline protected areas to

52 balance endangered species protection and commercial harvest needs, or other

53 anthropogenic activity (e.g., Guisan et al., 2013; Halpern et al., 2008; Vicente et

54 al., 2011; White et al., 2012).

55

56 To be able to understand the habitat, distribution, or ecological niche of species,

57 an array of models exists (Guisan & Zimmermann, 2000; Jeliazkov et al., 2022;

58 Norberg et al., 2019). Certain models may be more suitable for certain data types

59 and analyses (Araújo et al., 2019; Guillera-Arroita et al., 2015; Sofaer et al., 2019).

60 The different types of models include regression models, regression trees,

61 discriminate analyses, and presence-only models, etc. (Elith et al., 2006; Guisan &

62 Thuiller, 2005; Guisan & Zimmermann, 2000; Liu et al., 2019). Hierarchical models

63 are increasingly used to integrate several sources of heterogeneous data, account

64 for imperfect detection and spatial autocorrelation into the models, or for

65 multispecies distribution modelling (Domisch et al., 2018; Guillera-Arroita, 2017;

66 Isaac et al., 2020; Jeliazkov et al., 2022).

67

68 Despite the importance of understanding rare and data-poor species distribution,

69 modelling their distributions is not easy (Belmont et al., 2022; Liu et al., 2011;

70 Lomba et al., 2010). Firstly, data on the presence of rare species is often sparse in

71 space and time, or incomplete, often because of low detectability due to

72 unsuitable surveys or catch methods (Jeliazkov et al., 2022; Lomba et al., 2010;

73 Sofaer et al., 2019). Secondly, such data are often collected by different surveys

74 and are therefore difficult to synthesize (Araújo & Guisan, 2006; Isaac et al., 2020;

75 Jeliazkov et al., 2022; Robinson et al., 2011). Finally, assessment of the accuracy
<table>
  <tr>
    <td>76</td>
    <td>of data-poor models can be challenging due to over-dispersion, zero-inflation and model overfitting (Breiner et al., 2015; Jeliazkov et al., 2022; Sofiaer et al., 2019).</td>
  </tr>
  <tr>
    <td>77</td>
    <td>Presence only models have been used to deal with unbalanced data, but disregarding absence points can lead to loss of information, including where the species has not been observed and sampling effort (Crisfield et al., 2024; Guillera-Arroita, 2017). Few models are therefore adapted to model rare species distribution (Belmont et al., 2022; Lomba et al., 2010; Robinson et al., 2011).</td>
  </tr>
  <tr>
    <td>78</td>
    <td>Adding to the problem of modelling rare species, is that most metrics used to evaluate the model will vary according to the sample size, and the ratio of presences (e.g., Area Under the Curve (AUC), Kappa, True Skill Statistic (TSS), specificity and specificity) (Allouche et al., 2006; Bean et al., 2012; Freeman & Moisen, 2008; Liu et al., 2005).</td>
  </tr>
  <tr>
    <td>79</td>
    <td>Since the use of different models can lead to different outcomes (Elith et al., 2006; Guisan & Thuiller, 2005; Loiselle et al., 2003), Ensemble models (averaging of several models) or Ensemble of small models (fitting a larger number of small bivariate models) are increasingly used to stabilise the inference of the predictions (Araújo & New, 2007; Breiner et al., 2018; Lomba et al., 2010). Nonetheless, Ensemble models use the mean or weighted mean outputs of the different models (Araújo & New, 2007; Breiner et al., 2015; Lomba et al., 2010), rather than reducing their ‘uncertainty’ (false absences and false presences). Ensemble of small models are slightly better suited since they fit several small models and therefore reduce variance arising from sparse data (Erickson & Smith, 2023). They do not, however, overcome the biases which occur with very small sample sizes (increased variance) and can lead to overfitting (Erickson & Smith, 2023; Jeliazkov</td>
  </tr>
</table>
101 et al., 2022). Furthermore, for increased ensemble model accuracy, it is

102 recommended that a large number of models are used (>10) (Liu et al., 2019),

103 which can be computationally demanding when using hierarchical models (Breiner

104 et al., 2018).

105

106 Committee Averaging transforms Ensemble predictions into binary predictions (1

107 for presence and 0 for absence) using a threshold before averaging them. This

108 method can help improve understanding of both predicted presence ('core'

109 habitats) and absence ('unsuitable' habitats) (Amirkhiz et al., 2021; Araújo & New,

110 2007; Hao et al., 2019). Nonetheless, this method does not simultaneously optimise

111 the accurate predictions of 'core' and 'unsuitable' habitats. We therefore

112 developed a 'Combined Model for Accurate Predictions' (CMAP) methodological

113 framework, designed to address these challenges. The approach first consists of

114 selecting two sets of optimised models to predict 'core' and 'unsuitable' habitats

115 separately. Then in a second step, combine them to produce a single synthetic map

116 of species distribution containing both 'core' and 'unsuitable' habitats, but also a

117 'grey zone' where the probability of presence is more uncertain. Optimising

118 predictions of both core and unsuitable habitats is especially important when

119 implementing measures to protect species of concern whilst minimising impacts on

120 stakeholders (Cochrane et al., 2011; Loiselle et al., 2003; Villero et al., 2017).

121

122 We tested out our framework on 11 rare and data-poor diadromous fish (the allis

123 shad (*Alosa alosa*), twait shad (*Alosa fallax*), Mediterranean twaite shad (*Alosa*

124 agone), the European eel (*Anguilla anguilla*), the European flounder (*Platichthys*

125 flesus), the river (*Lampetra fluviatilis*) and sea lamprey (*Petromyzon marinus*),
126 Atlantic salmon (*Salmo salar*), sea trout (*Salmo trutta*), the thinlip mullet (*Chelon ramada*) and smelt (*Osmerus eperlanus*)), during their at-sea migration within the southern North Sea, the English Channel and the Bay of Biscay and French Mediterranean waters. The CMAP outputs were then compared to an Ensemble model.

131

132 Diadromous fish are a group of highly threatened species which migrate long distances between their freshwater and marine environments and are therefore subject to terrestrial, freshwater and marine pressures (Lassalle et al., 2008; Legrand et al., 2020; Limburg & Waldman, 2009; Verhelst et al., 2021). Very little is known about their at-sea life history stages. They are particularly vulnerable because of their obligatory coastal habitat occupancy (Elliott, et al., 2023a; Kappel, 2005) which is subject to high anthropogenic pressures (Halpern et al., 2015; Korpinen et al., 2021; Lotze et al., 2006). We hypothesised that because of the lack of information on diadromous fish at-sea, and no present model on their distribution at the time of designation, many Marine Protected Areas (MPAs) designated to protect these species would not sufficiently protect them. To test this hypothesis, we used the CMAP approach to 1) evaluate the relevance of existing MPAs for diadromous fish, and 2) explore the overlap between those MPAs which are specifically designated to protect diadromous fish, and their predicted 'core' habitats.

147
## 1482. Method

### 1482.1. The CMAP method

150 The CMAP framework has six main steps summarised within Fig. 1. Steps 1-2 are
151 general to all SDMs. Steps 3-6 are specific to the CMAP framework.

152 Step 1: Identification, implementation, and selection of candidate SDMs

153 As is required before any SDM methodology, a review and trial of the most
154 adequate model needs to be explored (Fig. 1.1.a). For CMAPs only presence
155 absence models can be used. An abundance of literature exists that discusses and
156 compares the performance of different model types which depend on the
157 objectives (Guisan et al., 2013; Robinson et al., 2011), and the data itself (e.g.,
158 spatial distribution, structure (patchiness), the number of sources of data,
159 observation bias, the number of presences and ratio of presence to absence,
160 imperfect detection, environmental biases, etc.). Hence, as highlighted by
161 Dormann et al., (2008), Soley-Guardia et al., (2024) and Zurell et al., (2020), care
162 should be taken in the selection and tuning of robust models (Fig 1.1.a-b.),
163 considering the underlying principles, and the evaluation of performance metrics
164 and uncertainty (Fig. 1.2). Jeliazkov et al., (2022) provide a comprehensive review
165 of methods to assess rare species and the types of models that can best fit them.
166 Since rare SDMs frequently need to be appropriate to incorporate potential data
167 gaps, errors, and biases (Araújo et al., 2019; Erickson & Smith, 2023; Norberg et
168 al., 2019; Simmonds et al., 2020; Fig. 1.1.b and 1.4), we advocate models that can
169 consider spatial autocorrelation (Commander et al., 2022; Dormann et al., 2007;
170 Norberg et al., 2019).
171
172 The selected models to be used in the CMAP approach must have the same spatial extent and resolution for comparability (Fig. 3.a-b). The spatial scale at which to predict the species distribution depends on the objectives of the study, the spatial scale of the study and the data itself (Commander et al., 2022; Elith & Leathwick, 2009; Soley-Guardia et al., 2024). For example, for conservation planning purposes smaller scales are generally required whereas global change studies tend to be larger scale (Elith & Leathwick, 2009; Engler et al., 2004). The resolution should, however, not be smaller than the sampling unit (Commander et al., 2022). The grain should be consistent with predictor variables, but this is not always possible (Elith & Leathwick, 2009; Robinson et al., 2011). When undertaking SDM for rare species, where there is presence and absence in the same grid, presence is usually defined for that grid. Larger grids can therefore enable models to be undertaken for rare species where there are few presences, albeit at a coarse scale (e.g., Engler et al., 2004; Elliott et al., 2023a).

186
187 For the objectives of identifying core and unsuitable areas for protection, we advocate focusing on minimising false presences and absences and maximising true presence and absence predictions (Fig 1.4-1.5). Overfitting should be avoided by reducing unnecessary predictor variables (Fielding & Bell, 1997; Guisan & Thuiller, 2005; Soley-Guardia et al., 2024), limiting model complexity, and cross-validation where possible (Kohavi, 1995).

193
194 For habitat variable selection, backwards stepwise selection of non-collinear variables (Dormann et al., 2008, 2013) is often the chosen method. For example, using the sample size corrected Akaike's Information Criterion (AICc) for regular
197 statistical models, or Bayesian Information Criterion (BICc) which are adapted for
198 data which has heterogeneity (Brewer et al., 2016; Dormann et al., 2013;

199 Soley-Guardia et al., 2024). Correcting for sample size is important to avoid
200 problems of overfitting which can occur with rarer species (Breiner et al., 2015).

201 For hierarchical models, the Deviance Information Criterion (DIC) or Widely
202 Applicable AIC which penalises the model for overfitting is often used (Brewer et
203 al., 2016; Spiegelhalter et al., 2002; Watanabe, 2010). A general rule is that the
204 number of presences should be ten times larger than the number of predictors used
205 for the model. However, this will depend upon the objectives of the study, the
206 spatial distribution of the species (e.g., generalist versus specialist), the proportion
207 of zeros in the dataset, and the type of data (Guillera-Arroita et al., 2015;

208 Jeliazkov et al., 2022). For distribution model comparison purposes, the different
209 modelling techniques need to have the same environmental variables much like
210 Ensemble modelling requires.

211

212 Step 2: Select a common model threshold for predictions and evaluation

213 Following the implementation and selection of the models, a threshold needs to be
214 identified to evaluate the models. The equivalent and same threshold should be
215 used to decide what probability of presence equates to the presence and absence
216 of core and unsuitable habitats for the different models, as is undertaken in
217 Committee Averaging (Fig. 1.2 and Fig. 1.5.a-c; Amirkhiz et al., 2021; Araújo &
218 New, 2007; Jiménez-Valverde & Lobo, 2007). The selected threshold will depend
219 on the prevalence of the data, the objectives of the study, and a decision requiring
220 the trade-off between omission (false absences) and commission errors (false
221 presences) given their negative correlation (Freeman & Moisen, 2008; Guillera-
222 Arroita et al., 2015; Liu et al., 2005; Robinson et al., 2011). Receiver Operating Curves (ROC) can be used to select thresholds (Freeman & Moisen, 2008). For rarer species literature has recommended that maximum sensitivity and specificity (MaxSens+Spec), or ReqSpec (a threshold to give the highest possible sensitivity) is used (Freeman & Moisen, 2008). These metrics can, however, overestimate the prevalence of rare species, for this reason, these metrics were not used to set thresholds (Freeman & Moisen, 2008).

229

230 Step 3: Ensure models meet initial evaluation selection criteria

231 We initially used two metrics (AUC and Percent Correctly Classified, PCC), in addition to the overall percent of predicted true presences (TP/(TP+TA+FP+FA)) to assess the models. The AUC score was used as a threshold-independent metric, whilst the PCC was used as a threshold-dependent metric which maximises the accuracy of the model (Allouche et al., 2006; Bean et al., 2012). The additional metric of ≥1% true presences was added since most metrics are biased with very low sample sizes (Allouche et al., 2006; Liu et al., 2005; Table S2-S3). If the model has an AUC and a PCC (Fig. 2) of <0.7 or predicts with ≤1% true presences, we deemed the model too weak for prediction and discarded it (Fig. 1.3.b). The metric cut-off value of 0.7 was used to maximise the informative character of results (Hosmer et al., 2013; Lomba et al., 2010; Manel et al., 2001).

242

243 If the model does not converge, or has a lower AUC and PCC of 0.7, either a larger grid or a reduction in zeros can be trialled for all models (Fig. 1.3.b). A finer grid cell limits the number of available observations within each cell and with low occurring species, this therefore increases the difficulty in model fitting by
247 increasing the proportion of zeros (Elliott, et al., 2023a; Keil et al., 2013; Pointin et al., 2018). Zeros should only be removed where it is known that the species does not exist and/or never occurs (Aarts et al., 2012). Subsequently, if only one model successfully predicts the species' presence and absence, this model should be used, and the threshold for this model used to determine core and uncertain areas (Fig. 1.3.a).

253

254 *Step 4: Identify the most accurate models to predict 'core' and 'unsuitable' habitats*

255

256 Once the models were selected and converged, we used the Positive Prediction Value (PPV) and Negative Prediction Value (NPV) to indicate 'core' and 'unsuitable' model prediction accuracy (Fig. 1.4-5).

257
258
259

260

<figure><img src="image_184.png" /></figure>
261 **Fig. 1.** Model selection criteria to implement CMAP for 'core', 'uncertain' and
262 'unsuitable' species habitat.

263

264 These two metrics control the proportion of predicted presences and absences
265 (respectively) that are observed (Fig. 2; Fielding and Bell, 1997). If there are no
266 differences between the model PPV or NPV, the most accurate model or the model
267 which meets potential data biases and error issues should be used (Fig. 1.5.a;
268 Simmonds, et al., 2020; Jeliazkov et al., 2022). For example, the model with the
269 least false presences or absences, or if there is no difference, the model which
270 incorporates more detail (e.g., models which account for over-dispersion, spatial-
271 autocorrelation, imperfect detection, etc.). If differences in PPV and NPVs are
272 observed, the model with the highest PPV should be selected to predict the 'core'
273 habitats (at the selected threshold; Fig. 1.5.b), and the model with the highest
274 NPV should be used to predict the 'unsuitable' habitats (at the selected threshold;
275 Fig. 1.5.c).

276

277 Sensitivity and Specificity can be checked to observe the proportion of observed
278 presences and absences that are correctly predicted (Fig. 2; Fielding and Bell,
279 1997). Furthermore, this metric can quickly indicate when data is insufficiently
280 balanced for the model (refer to Table S3). This separate analysis of presence and
281 absence predictions ensures that the selected models have sufficiently high
282 predictive performance despite the low proportion of presences in the dataset.

283 Care should, however, be taken since, when the modelled data has few presences,
284 the model may show a high metric predictive value, but under-predicts presences
285 since it does not consider false presences and false absences (respectively) and can
286 lead to under- or over-prediction (Fig. 2).

287

288 Step 6: Map the CMAP.

289 To map ‘core’ habitats, the model with the highest PPV should be used (Fig. 1.5.b),
290 selecting cells with prediction values greater than the given threshold value (Fig.

291 1.5.a-1.6.a; Fig. 3.c). For ‘unsuitable’ habitats, the model with the highest NPV
292 should be used selecting cells with prediction values less than or equal to (≤) the
293 given threshold value (Fig. 1.5.c-1.6.c; Fig. 3.e). Cells with conflicting predictions
294 from both models (selected by both or neither model) should be classified as
295 ‘uncertain’ habitats (Fig. 1.6.b; Fig. 3.d).

296
<table><thead><tr><th colspan="2"></th><th colspan="2">Observed</th><th rowspan="2">PPV =<br>TP/(TP+FP)</th></tr><tr><th colspan="2"></th><th>1 (P)</th><th>0 (N)</th></tr></thead><tbody><tr><th rowspan="2">Predicted</th><th>1 (PP)</th><td>TP</td><td>FP</td><td rowspan="2">NPV =<br>TA (TA +FA)</td></tr><tr><th>0 (PA)</th><td>FA</td><td>TA</td></tr><tr><th colspan="2"></th><td colspan="2">Sensitivity =<br>TP/(TP+FA)</td><td>PCC =<br>(TP+TA) /<br>(TP+TA+FP+FA)</td></tr></tbody></table>

Fig. 2. Confusion matrix used to evaluate the predictive accuracy of presence-absence models and metrics used to measure predictive accuracy at a set threshold.

298 Absence, PPV = Positive Predicted Value, NPV = Negative Predictive Value, PCC = Percent Correctly Classified (modified from Fielding and Bell, 1997).

299 threshold. TP = True Presences, FP = False Presence, FA = False Absence, TA = True

300 Absence, PPV = Positive Predicted Value, NPV = Negative Predictive Value, PCC =

301 Percent Correctly Classified (modified from Fielding and Bell, 1997).

302
303

a. Model prediction with the highest PPV

<figure><img src="image_185.png" /></figure>

c. 'Core' habitat areas

<figure><img src="image_186.png" /></figure>

d. 'Uncertain' habitat areas

<figure><img src="image_187.png" /></figure>

f. CMAP map

<figure><img src="image_188.png" /></figure>

b. Model prediction with the highest NPV

<figure><img src="image_189.png" /></figure>

e. 'Unsuitable' habitat areas

<figure><img src="image_190.png" /></figure>

304

305 Fig. 3. Combined Model for Accurate Prediction mapping process using *Alosa fallax* as an example species and a threshold value of 0.4 to distinguish core habitats.

306

307

15
## 3082.2. CMAP implementation on diadromous fish

309 To test the CMAP framework on diadromous fish at-sea, fisheries-dependent and -independent data were collated within eastern Atlantic and Mediterranean waters.

310 Scientific bottom trawl surveys which are undertaken in the same location on a yearly to biannual basis were extracted from International Council for the Exploration of the Sea (ICES) Database of Trawl Surveys (DATRAS) portal

311 (https://www.ices.dk/data/data-portals/Pages/DATRAS.aspx) and French

312 Metropolitan surveys not submitted to ICES DATRAS were obtained from the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) national scientific

313 surveys (https://campagnes.flotteoceanographique.fr/campaign). Fisheries-dependent data came from French fisheries observer data (ObsMer data; Cornou et

314 al., 2015; Elliott et al., 2023b). Each species had varying quantities of presences, 315 absences, and spatial distribution, but the same spatial area was chosen for CMAP 316 comparative purposes (Fig S1; Table S3). As a result of presence-absence imbalance 317 and uneven sampling in space and time, only data from 2003 when the ObsMer 318 program began were used (74% of the data came from the ObsMer dataset). For a 319 full list of surveys locations and dates see Elliott et al., (2023b) Table S1.

320 321 322 323 324 325 Prior to model analysis data cleaning was undertaken to standardise the surveys 326 and extract relevant data to perform statistical analysis (refer to Elliott et al., 327 2023a). Much of this data cleaning included errors in species identification which 328 were verified by experts in the field and through peer-reviewed scientific literature 329 (Elliott et al., 2023a; Soley-Guardia et al., 2024). To improve presence absence 330 imbalance all surveys, and gear types, without diadromous fish presence were 331 deleted before modelling (Figure S1; Allouche et al., 2006; Fielding & Bell, 1997).

332
333 Depths outside the ranges the species are observed within, were removed. To avoid

334 targeted fishing bias, all species that were targeted were removed: 1 A. anguilla; 8

335 C. ramada; and 57 P. flesus. It should also be noted that the habitat of these three

336 species are not predictors of the habitat of the other diadromous fish modelled.

337 Only survey data within ICES division 4.c (southern North Sea), 7.d-e (the Channel),

338 7.h (Celtic Sea) and 8.a-c (Bay of Biscay) and the Mediterranean waters bordering

339 France) were considered, to be able to undertake models at a fine enough

340 resolution (0.06x0.06 decimal degrees) for MPA analysis.

341

342.3. Environmental predictor variables

343 Depth (m), distance from the coast (km), sediment type (categorical), salinity

344 (PSU%) and Net Primary Production (NetPP; Mol m⁻³s⁻¹) were considered as potential

345 predictors of the presence of diadromous fish (Table S1; and details of the methods

346 and variable selection in Elliott et al., 2023a). For temporal variables (NetPP and

347 salinity), mean monthly and yearly (2003-2019) data were extracted and averaged

348 (Table S1). To select the model of best fit, variables which were not collinear

349 (Pearson's correlation coefficient < 0.5 or > -0.5 and a Variance Inflation Factor <

350 2; Dormann et al., 2013) were kept. The model with the lowest DIC was selected

351 (Elliott et al., 2023a). Since the environmental variables from the same source

352 were not available at the same resolution within the Atlantic and the

353 Mediterranean, C. ramada distribution in the Mediterranean was modelled

354 separately from the Atlantic, but at the same resolution (Table S1).

355
3562.4. Species distribution models used for CMAP

357 Three spatially explicit hierarchical models were run independently for each
358 species: 1) a Binomial (BN) Intrinsic Conditional Autoregressive (iCAR) model, 2) a
359 Zero-Inflated Binomial (ZIB) iCAR model; and 3) a Site Occupancy (SO) iCAR model.
360 The hierarchical structure of these models has the advantage of separating the
361 ecological process (here, the habitat suitability model, that is the spatial
362 distribution of the probability of presence/absence) from the observation process
363 (see more details in Elliott et al., 2023b). The fundamental concept is that the
364 presence/absence spatial field is considered as unobserved (latent) and modelled
365 on a spatial grid including spatial autocorrelation. The latent field of probability of
366 presence is modelled on a grid as a function of environmental predictors and by
367 explicitly accounting for spatial autocorrelation. The spatial autocorrelation P<sub>j</sub> is
368 modelled at the scale of the grid cell as an iCAR random effect:

369 (1) P<sub>j</sub> ~ Normal (μ<sub>j</sub>, σ<sub>j</sub><sup>Vp</sup>/√n<sub>j</sub>)

370 with μⱼ the mean of the random effects calculated over the nⱼ cells considered in
371 the neighbour of cell j (nⱼ is the number of the neighbours which is 8 in most cases,
372 and <8 when the cell is on the boundary of the spatial domain), Vp the variance of
373 the spatial random effect.

374

375 In the BNiCAR model, the multiple detection events y<sub>j,i</sub> (data 0/1) associated to the
376 same grid cell j are modelled as mutually independent Bernoulli process
377 (conditionally upon the latent probability of presence/absence in each grid cell j as
378 describe above):

379 (2) y<sub>j,i</sub> ~ Bernoulli (θ<sub>j</sub>)
380 with $\theta_j$ the probability of presence (the habitat suitability) within grid cell j

381 that is considered as a latent (unobserved) variable. The latent field of $\theta_j$ is

382 modelled in the logit scale as a function of environmental predictors $X_j$, with $\beta$ a

383 vector of fixed effect describing how much the environmental predictor contributes

384 to the suitability process and the spatial random effect $P_j$.

385 (3) $\text{logit}(\theta_j) = X_j\beta + P_j$

386 It is worth noting that in this model, all the multiple detection events $y_{j,i}$

387 associated with the same grid cell j are associated with the same latent probability

388 of presence and then with the same environmental variable defined at the scale of

389 the grid cell j. Also, the detectability is not explicitly modelled.

390

391 The ZIBiCAR and the SOiCAR use an additional random variable to separate the

392 habitat suitability model from the observation process (Dormann et al., 2007;

393 Elliott et al., 2023a; Latimer et al., 2006; MacKenzie et al., 2002). In both models,

394 conditionally upon the latent probability of presence/absence in each grid cell j,

395 the multiple detection events $y_{j,i}$ in the grid cell j are modelled as mutually

396 independent two steps Bernoulli process with detectability $\delta_{j,i}$ associated with the

397 detection event i:

398 (4) $y_{j,i} \sim \text{Bernoulli}(z_{j,i} \delta_{j,i})$

399 with

400 (5) $z_{j,i} \sim \text{Bernoulli}(\theta_{j,i})$

401 and
$$402 \quad (6) \quad \operatorname{logit}(\delta_{j,i}) = W_{j,i}\gamma$$

where $\delta_{j,i}$ denotes the probability of detecting the species at site $i$ within cell $j$. By contrast with the BNiCAR model, in SOiCAR and ZIBiCAR models the habitat suitability model considers the effect of environmental covariate at the scale of the event $i$ within the grid cell $j$:

$$407 \quad (7) \quad \operatorname{logit}(\theta_{j,i}) = X_{j,i}\beta + P_j$$

In the SOiCAR model, the detectability associated with the fishing operation $i$ is modelled by considering an effect of the gear type associated with observation $i$. $W_{j,i}$ is the raw vector of the design matrix, and $\gamma$ is the vector of the gear effects (modelled as fixed effects). The ZIBiCAR is a simplified version of the SOiCAR where the detectability is considered homogeneous across space and fishing operations with no effect of the gear type (so $\operatorname{logit}(\delta_{j,i})$ in eq. (6) is a constant fixed effect).

415

All SDMs were undertaken using the 'hSDM' package (Vieilledent et al. 2014) in R version 3.6.3 (R Code Team 2018). The prior distribution for the variance of the spatial random effect followed a uniform distribution with an upper boundary of variance of 8 which corresponds to the number of neighbours of the cell in most cases. The effect of the detectability from gear (Eq. 6) was modelled as fixed effects drawn from a weekly informative Gaussian prior centred at zero with a fixed standard deviation of 2 (Gelman et al., 2008; Northrup & Gerber, 2018). Each model was run with 50 000 Markov chain Monte Carlo (MCMC) iterations, five MCMC simulations and a burn-in phase of 50 000 iterations.
425

426 For comparative purposes, two separate types of Ensemble models were

427 implemented. One, using the mean probability of the presence of the three hSDM

428 models, and the second by using a bounding box to highlight consensus between

429 models (Araújo & New, 2007). For both Ensemble models, Committee Averaging

430 was implemented using the same thresholds used to determine the confusion

431 matrices and the threshold-dependent metrics. An Ensemble of small models was

432 not tested because of computational time. The confusion matrices and model

433 metrics of all five predictions were compared.

434

### 435.5. Model Evaluation

436 For the case of this dataset, a threshold of 0.4 was used for all models and species

437 as a compromise between the rarity of the species and ensuring the accuracy of

438 the predictions (minimising both false positives and false negatives) (Elliott et al.,

439 2023a; Liu et al., 2005). This threshold was selected following examination of

440 various thresholds and plotting PPV against the thresholds (Liu et al., 2005). Our

441 objective was to have one threshold for all species and 0.4 appeared to be the best

442 compromise between predicted presence and absence (Manel et al., 2001).

443 Furthermore, the same thresholds are required for both the ‘core’ and ‘unsuitable’

444 habitats to be able to combine the model and ensure predicted areas are not lost

445 or duplicated with differing results. MaxSens+Spec or RecSpec were not used

446 because we found these thresholds tended to over-predict the species, leading to

447 higher false presences. Unfortunately, because of the low diadromous fish

448 presence, cross-validation was not possible since splitting the data into

449 learning/validation samples led to insufficient presence in the learning dataset.
450

451 To map uncertainty in the probability of the presence of each species, we used the

452 mean coefficient of variation from the posterior distribution of spatial predictions

453 of habitat suitability (variable θ in the model) (Lambert et al., 2020; Latimer et

454 al., 2006; Winship et al., 2020). Since the CMAP combines the outputs from two

455 separate models, the uncertainty values for the ‘core’ habitat (Fig. 4.c) and

456 ‘unsuitable’ values (Fig. 4.c and 4.e) were used respectively. Because the objective

457 of this study was to maximise protection of diadromous fish, where the two models

458 used had conflicting predictions of presence (Uncertain habitats; Fig. 1.6.b; Fig.

459 3.d), Uncertain values from the ‘core’ habitat (Fig. 3.c; Fig. 4.a) were used to map

460 uncertainty for ‘uncertain’ habitats (Fig. 4.d).
<figure><img src="image_191.png" /><figcaption>Fig 4. The uncertainty mapping process for the CMAP using *Alosa fallax* as an example species.</figcaption></figure>

464

23

Confidential Review copy
### 465.2.6. The role of MPAs for diadromous fish

466 Information on MPAs and whether the MPA-listed diadromous fish to be protected

467 were obtained from the OSPAR conventions website on MPAs

468 (https://mpa.ospar.org/home-ospar/mpa-datasheets/) and the EU Natura 2000

469 website (https://natura2000.eea.europa.eu/) when the MPA was designated under

470 the Habitat Directive (92/43/EEC). Four French Marine Nature Parks had not been

471 submitted to the OSPAR MPA database at the time of analysis (2022) and were

472 therefore analysed separately (Bassin d'Arcachon, Estuaires Picards et de la mer

473 d'Opale, Iroise et Golfe du Lion). For MPAs that overlapped each other, data from

474 the larger MPA were considered. In all cases, this did not decrease the area of

475 MPAs designated to protect the diadromous fish.

476

477 From the CMAP, 'core' habitats were selected and intersected with MPAs. For each

478 species modelled, the percent of diadromous fish core habitats within MPAs, the

479 core habitats within MPAs which specifically protect diadromous, and the areas

480 which protect diadromous fish out with their core habitats were calculated (Table

481 1).

482

### 483.3. Results

#### 484.1. CMAP assessment

485 Using the selected grid resolution, seven of the eleven species were modelled. Too

486 few salmonids (S. salar = 55, S. trutta = 63), and lampreys (L. fluviatilis = 38, P.

487 marinus = 49) were present to model their distribution at the scale of this study

488 (refer to Elliott et al., 2023a).

489
490 For the CMAP, the models with the highest prediction accuracy for both PPV and
491 NPV were used respectively, giving an overall higher accuracy than would have
492 been obtained from using the models separately, or from an Ensemble averaging
493 approach (Table S2-S3; Fig. 5). The BNiCAR model was used to predict ‘core’
494 habitats (prediction >0.4) since it consistently had the highest PPV scores of the
495 three models, with the lowest false presences for all species given their varying
496 number of presence and absences. On average the SO and the ZIBiCAR model had
497 the same level of NPVs and the lowest false absences. For *O. eperlanus* and *A.
498 anguilla*, there were too few presences relative to absences for the BNiCAR model
499 to predict sufficiently well (Table S2). For these species, the SOiCAR and the
500 ZIBiCAR model were therefore used. For core and unsuitable habitats, spatial
501 uncertainty was low (Fig. S2). However, within the ‘uncertain’ habitats, prediction
502 uncertainty was higher. Sensitivity and specificity metrics results were more
503 variable between the results since these metrics consider TP and FA, and FP and TA
504 respectively (Fig. 2).

505
506 The Ensemble models' PPV was consistently lower than that of the BNiCAR and
507 CMAP but higher than the SO or ZIBiCAR. Equally, the Ensemble models' NPV was
508 on average marginally less than the CMAP, SO or ZIBiCAR but more than the BNiCAR
509 (Fig. 5; Table S3). For *A. anguilla* and *O. eperlanus*, the PPVs for the CMAP were
510 not higher than the Ensemble model since the CMAP did not use the BNiCAR due to
511 low true predicted presence (≤1%; Table S1). From the predicted distributions,
512 larger areas of core habitats were predicted using the Ensemble model relative to
513 the CMAP due to the averaging of the three SDMs being used (Fig. 6 and Fig. S3).
514 When using Araujo & New (2007) bounding box approach, areas where all three
515 models agreed in probability of presence, were less than the other models,

516 including the CMAP and the averaging Ensemble approach (Fig. 6 and Fig. S4). It is

517 therefore likely that this method under-predicts the species' actual distribution.

518 When selecting just one of the three SDMs predicting presence in the bounding box

519 approach, there appears to be a slight over-prediction relative to the CMAP

520 approach (Fig. 6 and Fig. S4).

521
<figure><img src="image_192.png" /></figure>

522

523 **Fig 5.** Bar graph for the binomial (BN), site occupancy (SO), zero-inflated binomial (ZIB) iCAR models, Ensemble model, and the CMAP Positive Predicted Values (PPVs), Negative Predictive Value (NPVs), sensitivity, and specificity, highlighting the choice of models and difference between commission and omission rates.

524

525

27
## 5263.2. The role of MPAs for diadromous fish

89 MPAs were within the modelled area. On average 55% of diadromous fish core

habitat was within MPAs. However, of these core habitats within MPAs, only 51%

were designated with specific measures to protect the species. Furthermore, 62%

of MPAs which specifically protect diadromous fish, are out with their modelled

core habitats (Fig. 5; Table 1).

For Habitat Directive and OSPAR-listed species (the three shads and A. anguilla),

the MPA network provides relatively good coverage. For example, for A. alosa and

A. fallax, 61% and 72% of the core habitats are within MPAs. A. agone, however,

has low coverage (<30%). Although A. anguilla has 69% of its core habitats within

MPAs, only 9% of these core habitats have specific measures to protect them. For

all the listed species, a large proportion of the MPAs which have been designated to

protect these species is located outside their core habitats (>50%; Fig. 5; Table 1).

For species which are not listed, the results are more mixed. For example, most of

O. eperlanus core habitats are within MPAs (71%), but none of these MPAs are

currently designated to protect this species. C. ramada is relatively well protected

(97%) in Atlantic waters, but little protection for this species currently exists within

the Mediterranean (Fig. 5; Table 1).
<figure><img src="image_193.png" /><figcaption>Fig. 6. Diadromous fish CMAP probability of presence within (dark red) and outside (dark blue tones) Marine Protected Areas (MPAs). Dark red cells are larger than the gridded cells to ensure smaller core habitats within MPAs are visible. The yellow shaded area represents ‘uncertain’ habitats, where the models do not agree (BNiCAR <0.4 and SOiCAR>0.4). Turquoise lines represent MPAs designated to protect the diadromous fish, black lines represent MPAs not specifically designated to protect them.</figcaption></figure>
Table 1. The proportion of Marine Protected Areas (MPAs) of relevance to 'core' diadromous fish (DF) habitats and whether they were designated to protect them. Grey-shaded cells highlight diadromous fish with Habitat Directive or OSPAR measures of protection.

<table><thead><tr><th>Species</th><th>Number of MPAs with specific protection measures</th><th>% core habitat area that are within the MPAs</th><th>% core habitat area within MPAs which are designated to protect DF</th><th>% of non-core habitat area within MPAs which are designated to protect DF</th></tr></thead><tbody><tr><td><i>A. alosa</i></td><td>24</td><td>61</td><td>94</td><td>56</td></tr><tr><td><i>A. fallax</i></td><td>29</td><td>72</td><td>100</td><td>60</td></tr><tr><td><i>A. agone</i></td><td>8</td><td>27</td><td>90</td><td>94</td></tr><tr><td><i>A. anguilla</i></td><td>5</td><td>69</td><td>9</td><td>51</td></tr><tr><td><i>C. ramada Atlantic</i></td><td>4</td><td>72</td><td>97</td><td>48</td></tr><tr><td><i>C. ramada Mediterranean</i></td><td>2</td><td>24</td><td>0</td><td>90</td></tr><tr><td><i>P. flesus</i></td><td>4</td><td>40</td><td>15</td><td>50</td></tr><tr><td><i>O. eperlanus</i></td><td>4</td><td>71</td><td>0</td><td>48</td></tr></tbody></table>

Confidential Review copy

30
5584. Discussion

559 Our results show that the CMAP framework was able to predict at a fine scale and
560 with high accuracy the ‘core’ and ‘unsuitable’ habitats of a range of rare species
561 with contrasted levels of information and different types of spatial distributions.

562 The CMAP enabled us to minimise Type I errors (predicting a presence when there
563 is no presence - commission) and Type II errors (failure to predict the presence of a
564 species when it is present - omission) (Loiselle et al., 2003), whilst providing
565 information on the confidence of the assessment. Minimising Type I and II errors
566 and providing confidence in assessment can support managers’ confidence in using
567 models for rare species protection and minimise erroneous conservation measures
568 being implemented (Araújo et al., 2019; Domisch et al., 2018; Loiselle et al., 2003;
569 Maxwell & Jennings, 2005). Ensuring the accuracy of predictions and avoiding over-
570 or under-predictions, may also help ensure stakeholders respect proposed
571 protection measures (Orofino et al., 2023; Sofaer et al., 2019).

572

573 Unfortunately, due to a lack of data on rare species, spatial protective measures
574 are often put in place with little understanding of the species distribution and
575 habitat (“the rare species paradox”) (Araújo et al., 2019; Lomba et al., 2010). The
576 latter can lead to inefficient spatial protection measures (Araújo et al., 2019;
577 Vicente et al., 2011; Wauchope et al., 2022) and resource conflict (Probst et al.,
578 2021). Here we developed a method whereby such problems can be reduced, and
579 the protection of the species can be maximised.

580
5814.1. CMAP assessment

582 Of the seven species which were modelled using the CMAP approach, the accuracy
583 of the models (PPV and NPV) was overall higher than the individual or Ensemble
584 models as a result of using the model with the highest PPV for predicted presences
585 and using the model with the highest NPV for absences (albeit marginal). Given the
586 CMAP process selects models with the least false presence and false absences to
587 predict core and unsuitable habitats, over-prediction of core areas is minimised.

588
589 For all case studies, the BNiCAR model was the best model for predicting ‘core’
590 habitats (highest PPV) since the model grids the data beforehand. The latter, thus,
591 reduces the number of absences (i.e., where there was a presence and an absence
592 in a grid the grid only uses the presences). Care should be taken with this model
593 because it does not explicitly consider imperfect detection from the different gear
594 types and may therefore underestimate the probability of presence, which could
595 lead to missing key areas as highlighted by the ‘uncertain’ habitats and lower NPV
596 (Guillera-Arroita, 2017; MacKenzie et al., 2002). By contrast, the SOiCAR and the
597 ZIBiCAR models slightly over-predicted presences (higher false presence), because
598 of their ability to incorporate detectability. Both the SOiCAR and the ZIBiCAR had
599 relatively similar NPV results and slightly better NPV scores than the BNiCAR. The
600 SOiCAR model was, used to predict ‘unsuitable’ habitats since it is more
601 informative than the ZIBiCAR, given its ability to consider imperfect gear detection
602 (Simmonds et al., 2020). In the case of our data, three separate models that
603 incorporate imperfect detection should have been undertaken with this dataset
604 because it provides a more transparent representation of uncertainty, taking into
605 account false absences, and the differing abilities of the different gear to detect
606 the species (Guillera-Arroita, 2017).

607

608 The PPV metric reflects the proportion of actual presences that are in the core
609 habitat, which can therefore give managers an increased confidence that this core
610 habitat is the correct area to protect. To provide managers with confidence in
611 ensuring the protection of species, two elements are needed, first that we are sure
612 that the species are present within the core habitat (the PPV). Second, that outside
613 this core habitat, the species is rarely present (the NPV), and therefore they are
614 not 'missing' key areas to protect. The CMAP framework enables us to achieve
615 these objectives. The CMAP, also indicates uncertain habitats where the species
616 might be present, but the area is not a top priority habitat to protect because of
617 the lower probability of presence with actual few presences. Since there are more
618 presences in the uncertain habitats relative to unsuitable habitats, if the
619 management measures in the core habitat are not sufficient, then measures could
620 be put in place to start protecting uncertain habitats.

621

622 The framework was tested on seven species with contrasted level of available
623 information and different types of spatial distributions. We were therefore able to
624 assess how the CMAP behaves through a range of case studies and extract some
625 generic results about the sensitivity of our modelling framework to the data input.
626 Specifically, our results show how when there are sufficient presences and
627 inversely absences in an area, the CMAP can accurately predict the presence and
628 absence of a species respectively. However, when presences are more dispersed
629 with absences in the same area, then uncertain areas are mapped. When
630 comparing these areas with the Ensemble model, these areas are predicted as the
631 species being present or absent, highlighting how the CMAP framework reduces
632 over- or under-predictions.

633
634 Since the ensemble model averages the three models, the ‘core’ and ‘unsuitable’
635 habitats were predicted with less accuracy than that of the CMAP. Norberg et al.,
636 (2019) also compared a variety of models, including hierarchical models, to
637 ensemble models using the five best-performing models, and found that spatially
638 explicit hierarchical models outperformed ensemble models. Another method used
639 to improve the certainty of ensemble models is through employing bounding boxes.
640 These are useful methods to show consensus between models, especially when
641 numerous models are used. However, when fewer models are tested, as was the
642 case here, this can lead to under-prediction as observed when only selecting areas
643 where all three models agreed. For larger ensemble models, a consensus method
644 can be implemented where half the models can be used to predict the presence of
645 a species (Araújo & New, 2007). With a smaller dataset, an ensemble of small
646 species should be trialled and compared to this approach. Given the computational
647 intensity of this approach, it was not possible to test here (Breiner et al., 2018).

648
649 Using the CMAP, numerous SDMs can be trialled and applied. As discussed within
650 section 2.1 Step 1 of the methods, the choice of the SDMs and thresholds used will
651 depend upon the dataset, its biases and the objectives of the study (Crisfield et
652 al., 2024; Guisan et al., 2013; Jeliazkov et al., 2022; Robinson et al., 2011). A
653 trade-off between omission and commission rate does need to be made in
654 conservation and management plans (Jeliazkov et al., 2022; Liu et al., 2005;
655 Loiselle et al., 2003). By using the uncertainty maps in line with the CMAP maps,
656 areas of higher and lower confidence in species predicted presence and absence
657 can be understood and taken into consideration when implementing species
658 management measures.

659

660 Due to the very low prevalence of these species, cross-validation was not possible
661 in this study, cross-validation should be considered to test for overfitting. A
662 multispecies model was not implemented here since it was deemed that the
663 species do not belong to the same community (Belmont et al., 2022). Further, we
664 wanted to test the efficiency of the method to model species with varying ratios of
665 presence and biases. Ideally, life history trait variations and seasonal-specific SDMs
666 should be undertaken (Belmont et al., 2022; Elliott et al., 2020; Frans et al., 2018).
667 It should also be noted that factors affecting species distribution can occur at
668 different spatial and temporal scales. Multi-scale and -occupancy status
669 distribution models may capture different dimensions of a species distribution and
670 reduce uncertainty (Guillera-Arroita, 2017; Jeliazkov et al., 2022; Vicente et al.,
671 2011). Within the present study, the presence data was too sparse to undertake
672 such analyses.

673

674.2. The role of MPAs for diadromous fish

675 Results show that numerous MPAs have not been designated to protect diadromous
676 fish, despite diadromous fish declines in recent years and IUCN national red-list
677 evaluations (Lassalle et al., 2008; Limburg and Waldman, 2009; Freyhof & Brooks,
678 2011; IUCN, 2022). When using the model outputs to evaluate MPAs, most of them
679 are relatively well located (close to the coast) given diadromous fish fresh-water
<table>
  <tr>
    <td>680</td>
    <td>requirements, but not all MPAs effectively cover the habitat continuum toward transitional water (Elliott, et al., 2023a). Furthermore, numerous MPAs that have been specifically designated to protect these species fall out of the predicted core habitats. This is concerning since it demonstrates the inadequacy and shortfall of MPAs (Araújo et al., 2019; Sundblad et al., 2011; Thomas et al., 2014) for these species. Given the rarity of diadromous fish and the sparse knowledge of their distribution at sea, the outputs from these SDMs provide a guide for subsequent field surveys (Sofaer et al., 2019).</td>
  </tr>
  <tr>
    <td>688</td>
    <td></td>
  </tr>
  <tr>
    <td>689</td>
    <td>Protected areas are recognised as not being the ‘panacea’ for species protection, but rather a component of species protection measures (Claudet, 2018; Game et al., 2009). For MPAs to be effective, prior knowledge of the species and their habitat, is essential, in addition to the implementation of effective management and control measures (Birkmanis et al., 2020; Guisan et al., 2013; Halpern &amp; Warner, 2003; Takashina &amp; Mougi, 2014). Under-protection of a species habitat may lead to difficulties in preserving a viable population (Guillera-Arroita et al., 2015; Guisan et al., 2013). Here we highlight how the existing MPA network could protect key functional habitats of protected and threatened diadromous fish during their at-sea life-history stages, should managers implement measures to protect these species. With more presence data, future studies should investigate applying this method to temporal data for multistate modelling, with uncertainty, and forecast modelling to investigate the impact of climate change (Guillera-Arroita, 2017; Guisan et al., 2013; Sofaer et al., 2019). Due to the low presence of the species modelled here over the course of the year and across years, this was not possible to investigate here. To help managers with the challenging priorities of</td>
  </tr>
</table>
705 setting trade-off management measures to protect species, whilst still ensuring
706 economic activities can persist, pressures can be overlayed onto the predictions to
707 help prioritise areas of protection and conservation efforts (e.g., Halpern et al.,
708 2008; Vicente et al., 2011; Villero et al., 2017).

709

### 710.3. Conclusion

711 The CMAP methodology presented here applies to a wide range of species and
712 circumstances in wildlife conservation management. The method not only enables
713 'core' and 'unsuitable' habitats for rare and data-poor species to be modelled with
714 high accuracy and confidence for managers but also demonstrates how this
715 methodology can improve the adequacy of an MPA network, should measures to
716 protect the species be considered (Guisan et al., 2013). Overestimating prevalence
717 could lead to missing conservation targets, whereas underestimation of prevalence
718 to protect the species of concern may lead to more costly targets (Commander et
719 al., 2022; Guillera-Arroita et al., 2015; Guisan et al., 2013; Sofaer et al., 2019).
720 Being able to accurately model species distribution is critical to reliable and
721 transparent biodiversity and conservation assessments (Araújo et al., 2019; Guisan
722 et al., 2013; Sofaer et al., 2019), and may be able to contribute to population
723 viability (Guillera-Arroita et al., 2015; Vicente et al., 2011). Finally, our study
724 highlights how existing MPAs could potentially protect data-deficient, and rare
725 species which are currently lacking in protection.

726

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Supporting information

<figure><img src="image_194.png" /><figcaption>Fig. S1. Diadromous fish presence (blue dots) and absence (light grey dots) data used to model their distribution (2003-2019). Dark grey lines indicate the International Council for the Exploration of the Sea (ICES) statistical divisions.</figcaption></figure>
<figure><img src="image_195.png" /><figcaption>**Fig. S2.** Diadromous fish Combined Model for Accurate Prediction (CMAP) uncertainty maps.</figcaption></figure>
<figure><img src="image_196.png" /></figure>

Fig. S3. Diadromous fish ensemble model probability of presence within (dark red) and out with (dark blue tones) Marine Protected Areas (MPAs). Dark red cells are larger than the gridded cells to ensure smaller core areas within MPAs are visible. Black lines represent MPAs without specific management measures, turquoise lines represent MPAs with specific management measures to protect diadromous fish.
<figure><img src="image_197.png" /></figure>

Fig. S4. Diadromous fish ensemble model employing bounding boxes from the three models (Araújo & New, 2007). Dark blue = where all three models are in consensus, medium blue where only one model predicted the presence of the species, and light blue = "unsuitable areas". Black lines represent MPAs without specific management measures, turquoise lines represent MPAs with specific management measures to protect diadromous fish.
<table><caption>Table S1. Environmental predictors used to analyse diadromous fish distribution.</caption><thead><tr><th>Variable</th><th>Unit</th><th>Description / Source</th></tr></thead><tbody><tr><td>Depth</td><td>Meters (m)</td><td>Bathymetry data downloaded from National Aeronautics and Space Administration (NASA) Earth Observations project (http://neo.sci.gsfc.nasa.gov) with a 0.04 × 0.04 grid cell resolution by using the General Bathymetric Chart of the Oceans (GEBCO) grid.</td></tr><tr><td>Distance to coast</td><td>Kilometers (km)</td><td>Calculated from a continent shape file</td></tr><tr><td>Sediment</td><td>Categorical</td><td>Broad-scale seabed habitat types (EUSeaMap) downloaded from EMODnet. Four classes were used according to grain size: mud (≤2mm), sand (> 2 and≤4 mm), coarse grain (> 4 mm and 64 mm), and rock (> 64 mm).</td></tr><tr><td>Salinity</td><td>Practical salinity unit (PSU%)</td><td>The salt content of sea water as measured on the practical salinity scale.<br>The mean monthly and yearly (2003-2019) salinity data was modelled at 0.04 × 0.04 decimal degree resolution from Copernicus (ERSEM, 2020).<br>Modelled at 0.04 × 0.04 decimal degree resolution from Teruzzi et al., (2021) for the Mediterranean Sea.</td></tr><tr><td>Net primary production</td><td>Mole concentration of per meters cubed per second (mol m<sup>-3</sup> s<sup>-1</sup>)</td><td>The excess of gross primary production by autotrophs, over the rate at which the autotrophs themselves respire.<br>The mean monthly and yearly (2003-2019) NetPP data was modelled at 0.04 × 0.04 decimal degree resolution from Copernicus (ECMWF, 2020).<br>Modelled at 0.04 × 0.04 decimal degree resolution modelled from Teruzzi et al., (2021) for the Mediterranean Sea.</td></tr></tbody></table>
<table><caption>Table S2. Diadromous fish Binomial (BN), Zero Inflated Binomial (ZIB), Site Occupancy (SO) Intrinsic Conditional Autoregressive Model (iCAR), Ensemble and Combined Model for Accurate Predictions (CMAP) CMAP confusion matrices.</caption><thead><tr><th rowspan="2">Species</th><th colspan="2">BN iCAR</th><th colspan="2">ZIB iCAR</th><th colspan="2">SO iCAR<br>Observed</th><th colspan="2">Ensemble</th><th colspan="2">CMAP</th></tr><tr><th>1</th><th>0</th><th>1</th><th>0</th><th>1</th><th>0</th><th>1</th><th>0</th><th>1</th><th>0</th></tr></thead><tbody><tr><td>A. alosa</td><td>1</td><td>212</td><td>51</td><td>316</td><td>619</td><td>325</td><td>699</td><td>296</td><td>419</td><td>212</td><td>51</td></tr><tr><td></td><td>0</td><td>203</td><td>5691</td><td>99</td><td>5123</td><td>90</td><td>5043</td><td>119</td><td>5323</td><td>90</td><td>5043</td></tr><tr><td>Uncertain</td><td colspan="10"></td><td>113</td><td>648</td></tr><tr><td>A. fallax</td><td>1</td><td>253</td><td>68</td><td>476</td><td>980</td><td>463</td><td>1004</td><td>354</td><td>402</td><td>253</td><td>68</td></tr><tr><td></td><td>0</td><td>321</td><td>5858</td><td>98</td><td>4946</td><td>111</td><td>4922</td><td>220</td><td>5524</td><td>111</td><td>4922</td></tr><tr><td>Uncertain</td><td colspan="10"></td><td>210</td><td>936</td></tr><tr><td>A. agone</td><td>1</td><td>57</td><td>21</td><td>69</td><td>79</td><td>76</td><td>95</td><td>68</td><td>70</td><td>57</td><td>21</td></tr><tr><td></td><td>0</td><td>30</td><td>259</td><td>18</td><td>201</td><td>11</td><td>185</td><td>19</td><td>210</td><td>11</td><td>185</td></tr><tr><td>Uncertain</td><td colspan="10"></td><td>19</td><td>74</td></tr><tr><td>A. anguilla</td><td>1</td><td>6</td><td>1</td><td>20</td><td>15</td><td>26</td><td>19</td><td>13</td><td>6</td><td>26</td><td>19</td></tr><tr><td></td><td>0</td><td>54</td><td>4451</td><td>40</td><td>4437</td><td>34</td><td>4433</td><td>47</td><td>4446</td><td>34</td><td>4433</td></tr><tr><td>Uncertain</td><td colspan="10"></td><td>0</td><td>0</td></tr><tr><td>C. ramada<br>(Atlantic)</td><td>1</td><td>260</td><td>85</td><td>269</td><td>96</td><td>317</td><td>306</td><td>288</td><td>146</td><td>260</td><td>85</td></tr><tr><td></td><td>0</td><td>147</td><td>3526</td><td>138</td><td>3515</td><td>90</td><td>4268</td><td>119</td><td>3465</td><td>90</td><td>3305</td></tr><tr><td>Uncertain</td><td colspan="10"></td><td>57</td><td>221</td></tr><tr><td>C. ramada<br>(Mediterranean)</td><td>1</td><td>31</td><td>14</td><td>44</td><td>65</td><td>44</td><td>65</td><td>43</td><td>48</td><td>31</td><td>14</td></tr><tr><td></td><td>0</td><td>19</td><td>298</td><td>6</td><td>247</td><td>6</td><td>247</td><td>7</td><td>264</td><td>6</td><td>247</td></tr><tr><td>Uncertain</td><td colspan="10"></td><td>13</td><td>51</td></tr><tr><td>P. flesus</td><td>1</td><td>674</td><td>180</td><td>747</td><td>540</td><td>755</td><td>518</td><td>741</td><td>454</td><td>674</td><td>180</td></tr><tr><td></td><td>0</td><td>178</td><td>4926</td><td>105</td><td>4566</td><td>97</td><td>4591</td><td>111</td><td>4652</td><td>97</td><td>4591</td></tr><tr><td>Uncertain</td><td colspan="10"></td><td>81</td><td>335</td></tr><tr><td>O. eperlanus</td><td>1</td><td>44</td><td>3</td><td>56</td><td>27</td><td>54</td><td>23</td><td>53</td><td>16</td><td>54</td><td>23</td></tr><tr><td></td><td>0</td><td>35</td><td>5322</td><td>23</td><td>5298</td><td>25</td><td>5302</td><td>26</td><td>5309</td><td>22</td><td>5297</td></tr><tr><td>Uncertain</td><td colspan="10"></td><td>3</td><td>5</td></tr></tbody></table>

<table><caption>Table S3. Species distribution model metric results. BNiCAR = Binomial Intrinsic Conditional Autoregressive Model, ZIB = Zero inflated binomial, SO = Site Occupancy, AUC = Area Under the Receiver Operating Curve Score, PCC = Percent Correctly Classified, PPV = Positive Predicted Value, NPV = Negative Predictive Value, CMAP = Combined Model for Accurate Predictions.</caption><thead><tr><th>Model</th><th>Metric</th><th>A. alosa</th><th>A. fallax</th><th>A. agone</th><th>A. anguilla</th><th>C. ramada<br>Med</th><th>C. ramada<br>Atlantic</th><th>P. flesus</th><th>O. eperlanus</th></tr></thead><tbody><tr><td rowspan="7">BNiCAR</td><td>Hauls</td><td>6157</td><td>6500</td><td>367</td><td>4512</td><td>362</td><td>4018</td><td>5958</td><td>5404</td></tr><tr><td>Presence</td><td>415</td><td>574</td><td>87</td><td>60</td><td>50</td><td>407</td><td>852</td><td>79</td></tr><tr><td>PCC</td><td>0.96</td><td>0.94</td><td>0.86</td><td>0.99</td><td>0.91</td><td>0.94</td><td>0.94</td><td>0.99</td></tr><tr><td>AUC</td><td>0.96</td><td>0.96</td><td>0.91</td><td>0.96</td><td>0.95</td><td>0.96</td><td>0.97</td><td>0.98</td></tr><tr><td>Sensitivity</td><td>0.51</td><td>0.44</td><td>0.59</td><td>0.1</td><td>0.62</td><td>0.64</td><td>0.79</td><td>0.56</td></tr><tr><td>Specificity</td><td>0.99</td><td>0.99</td><td>0.96</td><td>0.99</td><td>0.95</td><td>0.98</td><td>0.96</td><td>0.99</td></tr><tr><td>PPV</td><td>0.8</td><td>0.79</td><td>0.73</td><td>0.85</td><td>0.69</td><td>0.75</td><td>0.79</td><td>0.94</td></tr><tr><td>NPV</td><td>0.97</td><td>0.95</td><td>0.9</td><td>0.99</td><td>0.94</td><td>0.96</td><td>0.96</td><td>0.99</td></tr><tr><td rowspan="7">SOiCAR</td><td>Hauls</td><td>60107</td><td>62932</td><td>5089</td><td>37353</td><td>4640</td><td>46067</td><td>58110</td><td>43428</td></tr><tr><td>Presence</td><td>750</td><td>1056</td><td>176</td><td>101</td><td>88</td><td>891</td><td>2930</td><td>237</td></tr><tr><td>PCC</td><td>0.87</td><td>0.83</td><td>0.71</td><td>0.99</td><td>0.8</td><td>0.92</td><td>0.9</td><td>0.99</td></tr><tr><td>AUC</td><td>0.93</td><td>0.89</td><td>0.85</td><td>0.97</td><td>0.89</td><td>0.96</td><td>0.96</td><td>0.98</td></tr><tr><td>Sensitivity</td><td>0.78</td><td>0.81</td><td>0.76</td><td>0.43</td><td>0.88</td><td>0.78</td><td>0.89</td><td>0.68</td></tr><tr><td>Specificity</td><td>0.88</td><td>0.83</td><td>0.74</td><td>0.99</td><td>0.79</td><td>0.93</td><td>0.9</td><td>0.99</td></tr><tr><td>PPV</td><td>0.32</td><td>0.32</td><td>0.44</td><td>0.58</td><td>0.4</td><td>0.51</td><td>0.59</td><td>0.7</td></tr><tr><td>NPV</td><td>0.98</td><td>0.98</td><td>0.94</td><td>0.99</td><td>0.98</td><td>0.98</td><td>0.98</td><td>0.99</td></tr><tr><td rowspan="7">ZIBiCAR</td><td>Hauls</td><td>6157</td><td>6500</td><td>367</td><td>4512</td><td>362</td><td>4018</td><td>5958</td><td>5404</td></tr><tr><td>Presence</td><td>750</td><td>1056</td><td>176</td><td>101</td><td>88</td><td>891</td><td>2930</td><td>237</td></tr><tr><td>PCC</td><td>0.88</td><td>0.83</td><td>0.74</td><td>0.98</td><td>0.8</td><td>0.94</td><td>0.89</td><td>0.99</td></tr><tr><td>AUC</td><td>0.94</td><td>0.92</td><td>0.86</td><td>0.97</td><td>0.89</td><td>0.96</td><td>0.95</td><td>0.98</td></tr><tr><td>Sensitivity</td><td>0.78</td><td>0.71</td><td>0.74</td><td>0.33</td><td>0.88</td><td>0.66</td><td>0.88</td><td>0.71</td></tr><tr><td>Specificity</td><td>0.88</td><td>0.89</td><td>0.79</td><td>0.99</td><td>0.79</td><td>0.97</td><td>0.89</td><td>0.99</td></tr><tr><td>PPV</td><td>0.34</td><td>0.33</td><td>0.47</td><td>0.57</td><td>0.4</td><td>0.74</td><td>0.58</td><td>0.67</td></tr><tr><td>NPV</td><td>0.98</td><td>0.98</td><td>0.92</td><td>0.99</td><td>0.98</td><td>0.96</td><td>0.98</td><td>0.99</td></tr><tr><td rowspan="2">Ensemble</td><td>Hauls</td><td>6157</td><td>6500</td><td>367</td><td>4512</td><td>362</td><td>4018</td><td>5958</td><td>5404</td></tr><tr><td>Presence</td><td>750</td><td>1056</td><td>176</td><td>101</td><td>88</td><td>891</td><td>2930</td><td>237</td></tr></tbody></table>
<table><tbody><tr><td></td><td>PCC</td><td>0.91</td><td>0.87</td><td>0.76</td><td>0.98</td><td>0.85</td><td>0.93</td><td>0.91</td><td>0.99</td></tr><tr><td></td><td>AUC</td><td>0.94</td><td>0.93</td><td>0.88</td><td>0.97</td><td>0.91</td><td>0.96</td><td>0.96</td><td>0.98</td></tr><tr><td></td><td>Sensitivity</td><td>0.71</td><td>0.75</td><td>0.71</td><td>0.22</td><td>0.86</td><td>0.71</td><td>0.87</td><td>0.67</td></tr><tr><td></td><td>Specificity</td><td>0.93</td><td>0.88</td><td>0.84</td><td>0.99</td><td>0.85</td><td>0.96</td><td>0.91</td><td>0.99</td></tr><tr><td></td><td>PPV</td><td>0.4</td><td>0.39</td><td>0.49</td><td>0.68</td><td>0.47</td><td>0.66</td><td>0.62</td><td>0.77</td></tr><tr><td></td><td>NPV</td><td>0.98</td><td>0.97</td><td>0.92</td><td>0.99</td><td>0.97</td><td>0.97</td><td>0.98</td><td>0.99</td></tr><tr><td rowspan="5">CMAP</td><td>PCC</td><td>0.97</td><td>0.97</td><td>0.88</td><td>0.99</td><td>0.93</td><td>0.95</td><td>0.95</td><td>0.99</td></tr><tr><td>Sensitivity</td><td>0.7</td><td>0.69</td><td>0.84</td><td>0.43</td><td>0.84</td><td>0.74</td><td>0.87</td><td>0.71</td></tr><tr><td>Specificity</td><td>0.99</td><td>0.99</td><td>0.9</td><td>0.99</td><td>0.95</td><td>0.97</td><td>0.96</td><td>0.99</td></tr><tr><td>PPV</td><td>0.81</td><td>0.79</td><td>0.73</td><td>0.58</td><td>0.69</td><td>0.75</td><td>0.79</td><td>0.7</td></tr><tr><td>NPV</td><td>0.98</td><td>0.98</td><td>0.94</td><td>0.99</td><td>0.98</td><td>0.97</td><td>0.98</td><td>0.99</td></tr></tbody></table>
Combined Model for Accurate Predictions (CMAP)

Model 1:

Max positive predictive value

<figure><img src="image_198.png" /></figure>

CMAP

<figure><img src="image_199.png" /></figure>

Probability
of presence

<figure><img src="image_200.png" /></figure>

Model 2:

Max negative predictive value

<figure><img src="image_201.png" /></figure>

Protected areas

- Without protection measures

- With protection measures

* Assessing the relevance of the protected areas for species

* Maximising protection & minimising impacts on stakeholders

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<figure><img src="image_202.png" /></figure>

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