A predictive model for the ichnological suitability of the Jezero crater, Mars: searching for fossilized traces of life-substrate interactions in the 2020 Rover Mission Landing Site

Geological setting

Jezero is a 45-km wide impact crater in the north-eastern area of Mars in the Syrtis Major quadrangle (18.2°N, 77.6°E), a region dominated by a mafic crust (Horgan et al., 2020; Mangold et al., 2020). The central basin floor of the Jezero crater is capped by a ∼13 m thick volcanic unit, whereas sedimentary deposits are observed close to the crater rim (Schon, Head & Fassett, 2012; Shahrzad et al., 2019). Based on crater size-frequency distribution, the volcanic unit has been dated back to the Early Amazonian (Schon, Head & Fassett, 2012; Shahrzad et al., 2019). Two ancient fluvial valleys enter into the Jezero crater, Neretva Vallis to its west (Fig. 1) and an unnamed valley to the north (Fassett & Head, 2005; Mangold et al., 2020). Deltaic deposits are found at the mouth of the corresponding palaeorivers within the Jezero crater. The Western Delta is dominated by Fe/Mg smectites and exhibits well defined sedimentary layering, whereas the Northern Delta is dominated by Mg-carbonates and associated olivine, but is less well preserved (NASA, 2020b). The delta plain environment is the most well-preserved depositional setting of the Jezero delta complex, whereas most of the prodelta deposits have been eroded by aeolian processes (Schon, Head & Fassett, 2012; Day & Dorn, 2019). Accordingly, the present front of the Jezero fan is not a primary depositional feature but a steep (≥10–30°) erosional escarpment (Schon, Head & Fassett, 2012). Isolated distal remnants of sedimentary material, located ∼3 km from the continuous deposit, rise ∼150 m above the basin floor and also serve as indicators of the larger previous extent of the delta (Schon, Head & Fassett, 2012). Jezero crater is the only known location on Mars where orbital detections of carbonates are found close to robust fluvio-lacustrine features (Horgan et al., 2020). Jezero crater has been studied for more than a decade, but the timing and duration of its fluvial and lacustrine activity are still poorly constrained (Mangold et al., 2020). According to Fassett & Head (2008), incision of the Jezero valley system ended at approximately 3.8 Ga, at the Noachian–Hesperian boundary (see also Goudge et al., 2018).

This sedimentologic, stratigraphic and geomorphic evidence allowed to reconstitute to a certain extent the palaeoenvironmental evolution of the Jezero crater. Accommodation space resulted from the formation of a Noachian-aged impact crater (Schon, Head & Fassett, 2012; Goudge et al., 2018). Successively, the Jezero crater rim was breached by crater degradation processes and precipitation-fed valley networks, initiating the filling of the basin (Schon, Head & Fassett, 2012). Formation of the outlet channel began once these valley networks flooded the crater basin (Schon, Head & Fassett, 2012). Although the Jezero delta is thought to have formed when a river flowed into the Jezero crater around the Late Noachian/Early Hesperian boundary (Fassett & Head, 2008; Schon, Head & Fassett, 2012), its age remains uncertain, and the duration of surface flows that formed the delta is poorly constrained (Lapôtre & Ielpi, 2020). It is hypothesized that the majority of Martian fluvial activity peaked at approximately the Noachian-Hesperian boundary and ceased shortly thereafter (Goudge et al., 2015). The carbonate unit may represent authigenic lacustrine carbonates, precipitated in the near-shore environment of the Jezero palaeolake (Horgan et al., 2020). The presence of significant residual accommodation space in the Jezero Crater indicates that sediment transport and deposition into the lake terminated before the basin was completely filled (Schon, Head & Fassett, 2012). Duration estimates for delta deposition and lake persistence vary from several years to millions of years (Schon, Head & Fassett, 2012; Goudge et al., 2015; Mangold et al., 2020).

GIS organization

This study applies predictive modeling, a technique used in palaeontology and archaeology (Oheim, 2007; Anemone, Emerson & Conroy, 2011), to predict the location of (eventual) ichnofossil sites in the Mars 2020 Landing Site. Predictive modelling typically uses a geographic information system software to combine attributes associated with the preservation and distribution of fossils (Oheim, 2007). In this study, the software QGIS 3.10.12 ‘A Coruña’ (QGIS.org, 2021) is used to develop a geographic information system of the Mars 2020 Landing Site. The extent of the study area corresponds to the area of greatest scientific interest to the Mars 2020 Science Team and where high-resolution data of the High Resolution Imaging Science Experiment (HiRISE) are available (Stack et al., 2020). The map of Stack et al. (2020) (Fig. 3) is also used for deriving palaeoenvironmental information about Mars.

The GIS of the study area is organized in six input layers and three output predictive layers (Table 1). Each input layer maps the suitability for one or more ichnofossil types (bioturbation, bioerosion, biostratification structure) based on the assessment of a single attribute (suitability factor) of the Martian environment. In other words, any location within a single input layer is associated with a suitability score about the most desirable or least desirable conditions relative to ichnological site location. As a result, each location of the Mars 2020 Landing Site is associated with six suitability scores (Table 2). We followed the predictive modelling procedure of Oheim (2007), which successfully used four levels of classification for suitability scores. Accordingly, in this study suitability scores range from 1 to 4, with 4 representing the most desirable conditions (e.g., uncovered bedrock) and 1 the least desirable ones (e.g., completely covered bedrock). The scoring system is a relative one, i.e., it informs about how a location of the study area ranks in relation to the others. Scores are attributed based on the characteristics of the geological units (Table 3) of the Mars 2020 landing site.

The predictive layers result from the weighted overlay of multiple input layers (Table 1). The predictive layers map the suitability for a specific ichnofossil type, e.g., the higher the value on the bioturbation map layer, the more suitable the corresponding location it is for the preservation of bioturbation ichnofossils.

Source data collection

The source data for constructing the predictive model comprise (1) ichnosite data, (2) vector data, and (3) raster data. 18 reference ichnosites on Earth are considered for assessing suitability scores and the importance (weight) of each suitability factor in controlling ichnofossil distribution. The reference ichnosites are selected because of their similarity with the Mars 2020 Landing Site in terms of depositional environment (fluvial, lacustrine), processes (deltaic processes), genetic surfaces (unconformities) or exploration conditions. Location, palaeoenvironment and age of the ichnosites are presented in Fig. 4 and Table 4. We conducted fieldwork in each of the reference ichnosites, with few exceptions. Specifically, four sites (Renox Creek, La Brava Lake, Little Muddy Creek and the Francevillian Basin) have been studied using bibliographic references (Hannon & Meyer, 2014; El Albani et al., 2014; El Albani et al., 2019; Tietze & Esquius, 2018; Zonneveld, Bartels & Clyde, 2003). During fieldwork, ichnofossils have been photographed using different cameras: Nikon Coolpix W300, Canon EOS 100D, Sony DSC-HX60V, Fujifilm FinePix S9500, Olympus X450. The specifications of these cameras are comparable to those of the imaging tools mounted on the Perseverance rover, thus allowing to test the feasibility of the ichnological approach on Mars. Specifically, the field of view, resolution, and focal length of the fieldwork cameras are within the range of the Perseverance imaging tools, as defined in Mars 2020 technical reports and analogue studies (Godin, Caudill & Osinski, 2017; Edgett, Caplinger & Ravine, 2019; Martin et al., 2020).

Vector and raster data are used to evaluate the distribution of environmental parameters across the Mars 2020 landing site. Vector data comprise the shapefile of the photogeologic map of the Perseverance rover field site Team (Stack et al., 2020). Raster data include HiRISE image pairs (NASA, 2020c), the HiRISE visible base map (USGS Astrogeology Science Center, 2020a), and the HiRISE digital terrain model (USGS Astrogeology Science Center, 2020b).

Workflow

The predictive model of the Mars 2020 landing site is obtained by following a five-step workflow. The procedure is based on the predictive modelling workflow of Balla et al. (2014), which has been slightly modified to accommodate the lack of field data from the Jezero crater. The following steps have been applied:

1. Selection of the suitability factors: selecting the environmental parameters (suitability factors) controlling the distribution of potential ichnofossils;

2. Proxy assessment: determining the geological proxies that inform on the suitability factors selected in step 1;

3. Quantification of the suitability scores: attributing suitability scores to each location of the Mars 2020 Landing Site based on the assessment of suitability factors;

4. Assessment of suitability weights: estimating the suitability weights, namely the importance of each suitability factor in controlling ichnosite location;

5. Data aggregation: adding together the scores of multiple suitability factors (overlay analysis) to identify the most suitable locations for bioturbation, bioerosion and biostratification ichnofossils.

The methodological aspects of the workflow are presented below, whereas the assessment of predictive variables (e.g., suitability factors, proxies, scores and weights) are thoroughly discussed in the next section because of the specificity of the subject. From the methodological perspective, the first step for developing a predictive model requires the selection of the predictive parameters, i.e., the suitability factors that control the distribution of the objects of interest. In fact, predictive models use multi-parametric spatial analysis of georeferenced data to identify areas of possible interest (Store & Kangas, 2001; Balla et al., 2014). In the present study, suitability factors are selected among the environmental attributes that are known to determine ichnosite location on Earth, provided that they are independent from Earth-type life.

Except for the surficial cover, all the selected suitability factors are related to the palaeoenvironmental conditions of Mars, which cannot be directly observed. For this reason, the second step of the workflow requires the identification of environmental proxies, i.e., geological proxies that are informative of the ancient environmental conditions of Mars. This step is based on the principles of (palaeo)environmental analysis, which is the process by which the depositional environment of sediment is determined (Selley, 2000). The characteristics of a depositional environment have a fundamental control on the properties of the resulting rock unit (Nichols, 2009), including texture and sediment size, sedimentary structures, mineralogy and elevation range. In this paper, such characteristics are derived from the HiRISE visible map and the digital terrain model of the landing site, as well as by considering published observations on the Jezero crater (Hoefen, 2003; Ehlmann et al., 2008b; Ody et al., 2013; Goudge et al., 2015; Bramble, Mustard & Salvatore, 2017; Palumbo & Head, 2018; Rogers et al., 2018; Kremer, Mustard & Bramble, 2019; Horgan et al., 2020; Mandon et al., 2020; Stack et al., 2020).

In the third step of the workflow, each location of the study site is attributed a set of suitability scores for ichnofossils. Following the ranking scheme of Oheim (2007), the scores of ichnological suitability range from 1 to 4, with 4 representing the most desirable condition and 1 representing the least desirable condition for ichnofossils. Score assessment is based on theoretical considerations and the characteristics of 18 reference ichnosites on Earth (Table 4).

From the practical side, scores are first attributed to the geological units described in the most recent photogeologic map of the Mars 2020 Landing Site (Stack et al., 2020). Score assessment is based on the environmentally informative characteristics (proxies) of each unit. As a result of the scoring process, four scores are linked to each bedrock unit and a single score is associated to each surficial unit (Table 2). Successively, scores are related to the spatial distribution of each unit, which is derived from the vector file of the photogeologic map of the Mars 2020 Landing Site (Fig. 3). A code snippet is written to automate the process of relating scores to the spatial distribution of the geological units (Supplemental Information 1). The snippet is run using the field calculator of QGIS.

A similar process is followed for the suitability score of the water table position (W). Using the digital terrain model of the landing site, high suitability scores (W = 4) are attributed to the locations below the most elevated position of the Jezero lake shoreline (shoreline elevation based on Salese et al., 2020), whereas low suitability scores (W = 1) are attributed to those above.

Our predictive model takes into account the fact that some suitability factors are more influential than others in ichnological site distribution. The fourth step is therefore the assessment of the suitability weights, i.e., those suitability factors that have more importance in the model are given a higher percentage influence (weight) than the others. The relative importance of the suitability factors is based on theoretical considerations and observations at the reference sites of Fig. 4.

The fifth step is data aggregation, according to which the scores of multiple suitability factors are weighted and added together to identify the most favourable locations for detecting bioturbation, bioerosion and biostratification ichnofossils. This process is a weighted overlay analysis. Following Balla et al. (2014), aggregation of palaeoenvironmental data is achieved by using Weighted Linear Combination, i.e., each suitability score related to the ancient Martian environment is multiplied with the value of its weight and the results are summed. Provided that the sum of all weights equals to 1, the result will have the same range (1–4) as the one specified for the suitability scores (Balla et al., 2014). Characteristics of the modern Martian environment can preclude the observation of the bedrock, e.g., thick deposits of unconsolidated sediment (surficial cover) can completely obscure the bedrock. To consider this aspect, a quantity describing the surficial cover conditions is subtracted from the result of the aggregation of palaeoenvironmenal data. The following Eq. (1), based on the formula of Balla et al. (2014: p. 122), expresses this aggregation process in a generalized form:

where N is the aggregated suitability score, x_i value of the suitability factor i, w_i the weight of the suitability factor i, k is the suitability score for the surficial cover. Equation (1) is used to calculate the three aggregated suitability scores of our model, namely the bioturbation (A), bioerosion (B) and biostratification (C) suitability scores (Eqs. (2)–(4)). The same environmental condition can have a different importance for different trace types, therefore, the same suitability factor can be associated with a different weight when calculating A, B, or C (Table 5). To this aim, vector input layers are rasterized and aggregated. The result is a set of three predictive maps, each of which maps the suitability for a specific ichnofossil type (bioturbation, bioerosion or biostratification structure).

Assessment of suitability scores

Many different attributes of the environment, both modern and past, influence fossil preservation and accessibility on Earth (Oheim, 2007), being therefore eligible as suitability criteria for the Mars 2020 predictive model. On Earth, ichnosite location depends on the percentage of surficial cover concealing the bedrock and by the attributes of the palaeoenvironment controlling tracemaker activity, e.g., hydrodynamic energy, substrate cohesiveness, oxygenation, salinity, sedimentation rate, food supply, bathymetry, water turbidity, climate and position of the water table (Bromley, 1996; Buatois & Mángano, 2011; Knaust, 2017). However, only five of these factors are considered in the here proposed model: (1) surficial cover (variable K); (2) energy regime (E); (3) substrate cohesiveness (variables L and H); (4) sedimentation rate (R); (5) position of the water table (W).

These suitability factors were selected because they influence ichnological suitability independently from the planetary locale in which they are found. This criterion for selecting suitability factors is explained by the fact that extraterrestrial ecosystems, if any, may have differed for environmental conditions, biochemistry and evolutionary history from Earth ecosystems (Benner, Ricardo & Carrigan, 2004; McKay, 2010). Even if it is acknowledged that early Earth and Mars shared similar physical and chemical surface properties (Horneck, 2000; Kargel, 2004; Read, Lewis & Mulholland, 2015), their early environmental history was necessarily different, and there cannot be a perfect analogy between the two planets during their early history (Hipkin et al., 2013; Baucon et al., 2017). Also, the evolutionary history of any inhabited astronomical object should be unique (Morris, 1999), and environmental events can drive evolution via mass extinctions and directional selection (Schulze-Makuch, Irwin & Fairén, 2013). Factors excluded from the predictive model (e.g., oxygenation, salinity) are closely tied to terrestrial biology, being therefore inappropriate for predicting the ichnological suitability of Martian locations. For instance, ancient oxygenation levels are known to control the distribution of marine ichnofossils on Earth (Bromley & Ekdale, 1984; Baucon et al., 2020b), but this pattern is related to the fact that most metazoan life on Earth evolved to require oxygen (Danovaro et al., 2010). Each of the following sections presents a single selected suitability factor, describing (1) the specific criteria for its selection, (2) the geological proxies used to deduce its spatial variability, (3) the distribution of the related suitability scores.

Surficial cover

Selection criteria— The identification of ichnological sites on Earth is based on the observation of the bedrock where eventual ichnofossils are found. Surficial cover, consisting of unconsolidated superficial deposits covering solid rock, hampers the observation of the bedrock, thus precluding the detection of eventual ichnofossils (Fig. 5). Surficial cover (regolith, dune systems), if present, precludes the observation of the Martian bedrock as well, thus preventing the observation of eventual ichnofossils. Consequently, the surficial cover is selected as a suitability factor for ichnosite location.

Recent erosional phenomena (e.g., wind weathering) are also known to influence trace fossil preservation on Earth (Henderson, 2006). However, the impact of wind erosion is difficult to predict because it requires to quantify either the present or the past prevailing wind direction and intensity. This is further complicated by the fact that Mars differs from Earth in several weather-related parameters, i.e., its greater distance from the Sun, its smaller size, its lack of liquid oceans and its thinner atmosphere, composed mainly of CO₂ (Henderson, 2006; Read, Lewis & Mulholland, 2015). In addition, wind erosion can obliterate but also enhance the visibility of trace fossils via selective weathering processes. Areas that are highly exposed to winds may not necessarily less suitable than sheltered ones. For these reasons, there is a high risk of overinterpreting the effect of wind on ichnofossil suitability. Consequently, we did not include it in the predictive model.

Proxies for spatial distribution — The distribution of surficial cover across the Mars 2020 Landing Site can be deduced from the photogeologic map of Stack et al. (2020), which presents the distribution of surficial units. HiRISE imagery allows us to understand the degree to which each surficial unit covers the bedrock.

Suitability scoring — Surficial units are attributed suitability scores ranging from 1 to 4, with 4 representing the most desirable cover conditions (i.e., uncovered bedrock) and 1 the least desirable ones (i.e., completely covered bedrock). Comparison with the Bayanzag ichnosite, which presents surficial units comparable with those found within the Jezero Crater, has been particularly informative for attributing suitability scores (Figs. 5A–5C). In fact, Cretaceous ichnofossils can be observed within the talus deposits of Bayanzag, consisting of fragmented bedrock accumulated at the base of the cliffs (Fig. 5A). Based on this observation, the talus deposits of the Jezero crater are assigned a relatively high score notwithstanding their nature of surficial cover. The aeolian and deflation units of Bayanzag also find immediate analogies with the aeolian (large and small) and undifferentiated smooth units of the Mars 2020 Landing Site. These surficial units significantly hamper the observation of ichnofossils, therefore a similar unsuitable condition is assumed for the areas of the Jezero crater covered by large aeolian bedforms (e.g., Neretva Vallis, crater floor) (Fig. 6). These areas are, therefore, unsuitable for the detection of ichnofossils, if any. By contrast, large areas of the Western Delta are uncovered, allowing the observation of the Martian bedrock and of the eventual ichnofossils preserved within it.

Energy regime

Selection criteria — Fluid flow is one of the most widespread transport and deposition processes in both subaerial and aqueous sedimentary environments. On Earth, the importance of fluid flow is exemplified by the pervasive action of currents, waves, and winds, among other processes, over the type and mobilization of the substrates. Geological evidence indicates persistent water flow on Early Mars (Malin & Edgett, 2003), as well as for ancient wind activity (Banham et al., 2018). Any flowing fluid possesses energy due to its motion, which is often referred as to hydrodynamic energy in the case of flowing water. The energy of a flowing fluid is one of the most common limiting factors in trace fossil distribution on Earth, influencing both the tracemaker behaviour and the preservation potential of ichnofossils (Buatois & Mángano, 2011). The energy regime is chosen as a selection criterion because it influences the suitability for ichnofossils independently from the planetary locale. In fact, substrate particles are increasingly removed from the bottom as the energy increases (Allen, 1992; Nichols, 2009; Duran Vinent et al., 2019). Particle removal is independent from the physical or biogenic nature of the sedimentary structures preserved within the substrate, i.e., a burrow obeys to the same physical rules governing, for instance, the preservation of ripples or mudcracks. Consequently, high-energy conditions tend to obliterate pre-existing fabrics and sedimentary structures regardless of their biogenic or abiogenic nature. This phenomenon depends solely on sediment and flow dynamics, holding on Earth and on Mars.

Proxies for spatial distribution — The spatial variability of the past energy conditions at the Mars 2020 Landing site is here deduced from sedimentary architecture, grain size and landforms. Deltas are formed by deceleration of the river outflow into a basin with a standing body (Postma, 1990; Postma, 2003), therefore the distance from the mouths of the palaeorivers is used as a reliable indicator of hydrodynamic energy in the Jezero palaeolake. Grain size is also used as a proxy for hydrodynamic energy because transport of sediments occurs when the currents are high enough for the bed shear stress to exceed the threshold of motion, which depends upon the median sediment grain size (Ward et al., 2020). An additional hydrodynamic proxy is the geomorphic evidence of channels, which are recognized as areas of high-velocity flow in fluvio-deltaic systems (Shaw, Mohrig & Wagner, 2016). Since hydrodynamic energy usually decreases with increasing water depth, the current elevation of the landing site is also taken as a proxy for hydrodynamic conditions.

Suitability scoring — The bedrock units of the landing site are attributed scores from 1 to 4, with 4 representing the best (lowest-energy) hydrodynamic conditions for ichnosite location.

Low scores are attributed to high-energy settings because most surface or near-surface bioturbation traces are removed by erosion in high-energy environments, whereas the preservation potential increases with decreasing energy (Hallam, 1975; Curran, 1994; Bromley, 1996; Buatois & Mángano, 2011). This phenomenon is explained by the sedimentological nature of bioturbation structures, which are at one with the substrate before and after diagenesis (Hallam, 1975). The low ichnological suitability of high-energy settings is supported by observations at the Arda reference ichnosite, which encompasses a gradient from high-energy (fluvial, shoreface) to low-energy (offshore) fluvial-influenced settings (Crippa et al., 2018). High-energy deposits of the Arda section, Italy (Fig. 7A) display a lower bioturbation intensity than low-energy ones (Fig. 7B). Also, these high-energy deposits show how tracemakers need to invest in extra-efforts to cope with the shifting substrates associated to high energy conditions, i.e., the producers of Ophiomorpha reinforced burrows with pellets (Fig. 7A).

Based on these observations, high suitability scores for bioturbation are attributed to the relatively quiet, distal areas of the delta, characterized the delta thinly layered and delta layered rough unit. By contrast, lower scores are attributed to the proximal areas of the Jezero delta, often characterized by channelized deposits consisting of the coarse-grained delta blocky unit (Fig. 8). This scoring is supported by the observations made at the Ventimiglia palaeodelta (Pliocene, Italy). Here, high-energy deltaic deposits are unbioturbated or sparsely bioturbated (Figs. 9A–9C), whereas higher bioturbation intensities are associated with lower energy ones (Fig. 9D). Similarly, the high-energy regime of the Neretva Valley, consisting of an incised fluvial channel, is interpreted as being particularly unsuitable for ichnofossil preservation.

By removing substrate particles, high-energy conditions negatively influence the preservation of bioerosion and biostratification structures as well. However, high-energy conditions have a lesser influence on the preservation of bioerosion and biostratification structures because these are related to more cohesive, erosion-resistant substrates. This aspect is addressed in the data aggregation stage (step 5) of the workflow, i.e., a small weight is attributed to the energy regime when calculating the overall bioerosion and biostratification suitability. A higher weight is attributed to the energy regime for bioturbation suitability.

More in detail, this choice is justified by the fact that bioerosion ichnofossils necessarily develop in hardgrounds, which are less prone to erosion than softgrounds by their nature of lithic substrates. For this reason, the energy regime is not regarded among the major factors influencing the suitability for (eventual) bioerosion ichnofossils in the Mars 2020 Landing Site. In parallel to bioerosion ichnofossils, biostratification structures tend to withstand high-energy conditions better than bioturbation ichnofossils. This is explained by the fact that biostratification tend to stabilize depositional surfaces and shelter the sediment against erosion (Noffke et al., 2001). As a result, biostratification structures are common in a wide hydrodynamic range. Modern subtidal Bahamian stromatolites are positively associated with strong tidal currents because these are unfavorable for competitors such as metazoans and macroalgae (Noffke & Awramik, 2013). Exclusion of competitors is also the reason explaining why stromatolites are common in hypersaline lagoons, exceedingly warm waters and macrotidal settings (Noffke & Awramik, 2013; Suosaari, Reid & Andres, 2019). Some MISS are associated to bland erosional regimes, e.g., erosional pockets are produced where pieces of microbial mat are removed by erosion, leaving an irregularly shaped mat border surrounding a depression through which the underlying sediment is exposed (Bose & Chafetz, 2009). Despite their resilience to erosion, biostratification structures tend to be more delicate than bioerosion ichnofossils because of their nature of sedimentary structures. Low-energy conditions favor the preservation of biostratification structures, as exemplified by the concentric biostratification structures of the Canelas reference ichnosite (Ordovician, Portugal). Here, taphonomy of body fossils indicate extremely low-energy conditions, which aided the excellent preservation of microbial-related biostratification structures (Fig. 7C) (Neto de Carvalho et al., 2016). Another example is provided by the Miocene palaeocoast of the Rio Negro Formation (Argentina), which was differentiated into high-energy erosional domains and protected areas where MISS became preserved (Carmona et al., 2012). Based on these observations, the high-energy deposits of the Jezero crater are here interpreted as less suitable for biostratification than low-energy ones. This parallels the impact of the energy regime on the suitability for bioturbation structures. However, because of the mentioned substrate-stabilizing effect, a relatively low weight is assigned to the suitability score of the energy regime for biostratification. Weight is used in overlay analysis, during which a weighted sum is computed across multiple layers to account the fact that some suitability factors are more influential than others in biostratification structure distribution.

Substrate cohesiveness

Selection criteria — Substrate cohesiveness is selected as a suitability factor because the mechanical properties of the substrate constrain the ichnofossil type that can (eventually) be produced. On Earth, the mechanisms of moving through solid substrata depend on the mechanical properties of the substrate (Dorgan, 2015). Organisms move through loose substrates by displacing sediment grains (bioturbation; Fig. 10A); they move by creating an opening in hard substrata by mechanical or chemical means (bioerosion; Fig. 11A) (Bromley, 1996; Dorgan, 2015). As a result, the cohesiveness of a given substrate constrains the type of ichnofossils that can be produced within it, i.e., bioturbation structures can be produced only in unconsolidated substrates because hardgrounds do not provide grains to displace. This relationship between substrate cohesiveness and ichnofossil type holds not only for Earth but also for Mars because it derives solely from the mechanical properties of the substrate, being independent of the planetary locale on which the substrate is found. This is a fact proved by the traces left by the Curiosity rover, producing ‘bioturbational’ trails on Martian softgrounds and ‘bioerosional’ drill holes into hardgrounds (Baucon et al., 2017). Consequently, the presence of softgrounds is here regarded as a suitability factor for bioturbation ichnofossils, whereas the presence of hardgrounds is a suitability factor for bioerosion. For this reason, the impact of the substrate on ichnofossil suitability is accounted by two different variables, L (substrate suitability for bioturbation) and H (substrate suitability for bioerosion; Table 1). When calculating the overall suitability for bioturbation (A; Table 1), H is ignored, and L contributes to the weighted sum. Conversely, L does not contribute to the weighted sum for bioerosion suitability (B; Table 1).

Whereas substrate cohesiveness constrains the development of bioerosion and bioturbation structures, it exerts less influence on biostratification. This is counterintuitive because biostratification acts on loose particles, which however can be found not only within softground substrates but also in the water column. For instance, the most commonly cited pathway for Phanerozoic marine stromatolites is the trapping and binding model, according to which successive generations of microbial filaments (or extracellular polymeric substances) trap grains settling from the water column and bind the sediments via precipitated cements (Shapiro, 2007).

Substrate type does not necessarily limit movements of biostratification-forming organisms, e.g., cyanobacteria on Earth move through the sediment that blanketed them by jet gliding upwards in the secreted exopolysaccharides (Hoiczyk, 2000; Foster et al., 2009). The weak influence of substrate type on biostratification is supported by the fact that fossil and modern stromatolites are reported from both softgrounds and hardgrounds (Reid et al., 1995; Stefano et al., 2002). It should be however noted that MISS are restricted to softgrounds by their nature of sedimentary structures. This is exemplified by the lacustrine MISS of the Collio Formation, Italy, which are dissected by mudcracks, being therefore related to softgrounds (Figs. 10B–10C).

Proxies for spatial distribution — The cohesiveness of the ancient substrate is deduced from the sedimentological characteristics of the bedrock units. Specifically, deltaic deposits are interpreted as proxies for softgrounds based on the fact that delta formation requires unconsolidated sediments. Conversely, deposits that predate the Jezero impact are interpreted as hardgrounds. Carbonate-rich units were deposited as softgrounds, but the terrestrial record (Knaust, Curran & Dronov, 2012) show that they can undergo early cementation. For this reason, deposits rich in carbonate are taken as a proxy for both softground and hardground conditions.

Suitability scoring — Bedrock units have been attributed bioturbation suitability scores ranging from 1 to 4, with 4 representing the most desirable substrate conditions (i.e., loose substrates) and 1 the most unsuitable conditions (i.e., hardgrounds) (Fig. 12). A specular scoring system is used for bioerosion, i.e., 4 represents the most suitable conditions (hardgrounds) and 1 the most unsuitable ones (loose substrates) (Fig. 13).

Deltaic units are assigned high bioturbation scores because they necessarily derive from the deposition of unconsolidated sediments, allowing benthic organisms, if any, to displace sediment grains (Fig. 10B). By contrast, the crater rim units are attributed low suitability scores for bioturbation (Fig. 12) because they are part of the basement sequence that predates the formation of Jezero crater (Stack et al., 2020). Consequently, they plausibly represented hardgrounds at the time of the Jezero palaeolake. For the same reason, the crater rim is attributed a high suitability score for bioerosion (Fig. 13). Hard substrates were also provided by the crater rim breccia, which has been interpreted as an impact breccia formed during the Jezero impact event or the Isidis event (Stack et al., 2020).

The above suitability scores are based on the fact that bioturbation and bioerosion require opposite substrate conditions (e.g., hardgrounds cannot be bioturbated). In most cases, sites with a high bioturbation score (L = 4) are attributed a low bioerosion score (H = 1), and vice-versa. However, the ichnofossil record of the Earth shows that, under specific environmental conditions, the same site can be suitable for both bioturbation and bioerosion ichnofossils. For instance, the unconformity surface of the Oura reference ichnosite (Portugal) displays crustacean bioturbation ichnofossils (Thalassinoides) intersected by bivalve bioerosion ichnofossils (Gastrochaenolites) (Figs. 11B–11D). The unconformity developed within a Miocene (Serravallian) bioturbated softground that successively lithified and formed a rocky shoreline, which was subsequently bioeroded (Cachão et al., 2009). A similar scenario is possible for the crater floor units of the Mars 2020 Landing Site, which may be cross-cut by an unconformity surface (Stack et al., 2020). Unconformities tend to become colonized by substrate-controlled trace fossil suites when exposed to aqueous conditions (Pemberton et al., 2001; MacEachern et al., 2007; Buatois & Mángano, 2009; Richiano et al., 2019). Similarly, the richness in carbonate of the margin fractured unit (Ehlmann et al., 2008b; Goudge et al., 2015; Horgan et al., 2020) may have favored fast diagenesis, producing a softground-to-hardground transition.

High suitability scores for bioturbation are attributed to the crater floor deposits based on their possible nature of fluvio-lacustrine softgrounds. This interpretation is supported by the contact of crater floor fractured 2 unit with the deltaic units, as well as the textural similarities between crater floor fractured unit 1 and 2 (Stack et al., 2020). However, it should be highlighted that other plausible interpretations are available, e.g., lava flows, magmatic intrusions, impact condensates, tephra deposits, aeolian, airfall and fluvial deposits (Hoefen, 2003; Ody et al., 2013; Bramble, Mustard & Salvatore, 2017; Palumbo & Head, 2018; Rogers et al., 2018; Kremer, Mustard & Bramble, 2019; Mandon et al., 2020; Stack et al., 2020).

Sedimentation rate

Selection criteria — Sedimentation rate has long been recognized as among the major influences on the intensity of bioturbation, i.e., the degree to which the original fabric of the substrate has been modified by organisms (Bromley, 1996; Taylor, Goldring & Gowland, 2003; Buatois & Mángano, 2011). Data from Earth show that very low bioturbation intensities commonly correlate to elevated rates of sedimentation and massive bedding, while high bioturbation intensities are usually associated with slow sedimentation and heterolithic deposition (Gingras, MacEachern & Dashtgard, 2011). Low/null sedimentation also enables the colonization of hardgrounds by boring organisms (Łaska, Rodríguez-Tovar & Uchman, 2021). The relationship between sedimentation rate and the degree of biological reworking is explained in terms of availability of time, i.e., the degree to which a substrate is biologically reworked depends on the amount of time available for biogenic activity per unit accumulation of sediment (Howard, 1975). In other words, slow sedimentation rates provide organisms with a longer amount of time for reworking (bioturbating or bioeroding) the substrate. This phenomenon is not dependent upon the planetary locale, therefore, sedimentation rate is here selected as a suitability factor for the Mars 2020 Landing Site.

Proxies for spatial distribution — On Earth, the sedimentation rate can be estimated by considering the thickness of a given sedimentary unit and the amount of time in which the unit deposited. These variables cannot be precisely estimated for the geological units of the Mars 2020 Landing Site; however, the identification of the major sources of sediment allows a qualitative estimate of the spatial variability of the sedimentation rate across the study area. It is not possible to provide absolute values for the sedimentation rate, but it is possible to assess the relative values based on the different architectural elements of the Jezero delta. In this regard, only two fluvial inlets entered the Jezero lake, bringing sediments from a mineralogically-diverse area into the lake (Schon, Head & Fassett, 2012). Consequently, the areas of maximum sedimentation coincided with the deltaic areas adjacent to the river mouths; conversely, the areas with the lowest sedimentation rates were located far away from the river mouths. This interpretation is supported by investigation of terrestrial deltas. These are not exact analogues of the Jezero delta but necessarily share similar sedimentary dynamics due to the rapid deceleration of water flow at the river mouth. For example, in the Fraser River delta, Canada, the maximum sedimentation (∼13 cm yr⁻¹) occurs in the vicinity of the river mouth (Hart, Hamilton & Barrie, 1998; Ayranci & Dashtgard, 2013). Anyway, in deltaic systems where density currents can occur regularly, a significant proportion of riverine sediment input may be transferred to the distal part of the systems leading to important distal sediment accumulation zones (distal delta lobe). A sediment budget was calculated for the Rhone River delta system, in eastern Lake Geneva, Switzerland (Silva et al., 2019). Mean sedimentation rates in these areas vary from 0.0737 m year⁻¹ (delta front) to 0.0246 m year⁻¹ (distal delta lobe). The remaining area, lake basin background deposition, show sedimentation rates one order of magnitude smaller.

Suitability scoring — The geological units of the study area have been attributed scores ranging from 1 to 4, with 4 representing the most desirable conditions (i.e., low sedimentation rate) for bioturbation and bioerosion ichnofossils (Fig. 14). High scores are attributed, for instance, to the distal areas of the Jezero delta where sedimentation rate was plausibly low, providing eventual organisms with longer amounts of time for bioturbating the substrate. By contrast, the areas in the vicinity of the palaeoriver mouth are attributed low suitability scores. This suitability scoring is motivated not only by ichnological theory (Bromley, 1996; Taylor, Goldring & Gowland, 2003; Buatois & Mángano, 2011; Gingras, MacEachern & Dashtgard, 2011; Tonkin, 2012), but also by empirical observations at the Pramollo Basin (Carboniferous-Permian; Italy-Austria) (Baucon & Neto de Carvalho, 2008; Baucon et al., 2015b). Here, delta front deposits present lower bioturbation intensities than the units deposited in more distal locations of the same basin (Fig. 15). The same phenomenon is reported from lake settings, i.e., sedimentation rate tends to exceed bioturbation rate in the more proximal sectors of lacustrine deltas (Figs. 16A, 16B) (Zonneveld, Bartels & Clyde, 2003; Buatois & Mángano, 2011). Conversely, higher bioturbation and bioerosion rates are normally associated with lower sedimentation rates (Figs. 16C, 16D).

Water level

Selection criteria — On Earth, the availability of water is a fundamental control factor on ichnofossil formation (Hasiotis & Honey, 2000; Buatois & Mángano, 2011). This mirrors the astrobiological principle by which water is an essential compound for the existence of life as we know it (Mottl et al., 2007; Jones & Lineweaver, 2010). The presence of water is considered so important for life that the astrobiological exploration of Mars has been guided by the search for water (Hubbard, Naderi & Garvin, 2002; Grotzinger, 2009). In the Jezero Crater there are clear proxies for the past presence of liquid water (Salese et al., 2020), which therefore plausibly represented an abundant resource for organisms, if any. For these reasons, the presence of water has been selected as a suitability factor for the Mars 2020 predictive model, although it should be noted that extraterrestrial life without water can be conceived as well (Mottl et al., 2007).

Proxies for spatial distribution — The presence of water within the study area is deduced from sedimentological and geomorphological observations. In this regard, fluvio-deltaic landforms are clear indicators of the presence of water in a liquid state, for which reason there is little doubt about the aqueous environment of the Jezero deltas (Salese et al., 2020). The elevation is another proxy for the presence of water. According to Salese et al. (2020), the basin was initially filled up to −2,243 m, which is here regarded as the reference elevation for establishing ichnological suitability of the water table level. After the breach of the crater rim, the water level dropped to −2,410 m and, during the deposition of the Jezero delta, the top of the delta had the same elevation as the bottom of the breach (Salese et al., 2020). According to Schon, Head & Fassett (2012), the base level within the palaeolake was controlled by the outlet channel and was near −2,400 m.

Suitability scoring — Scores ranging from 1 to 4 have been attributed to the geological units of the Mars 2020 Landing Site, with 4 representing the most desirable conditions (i.e., presence of a permanent water table) for ichnofossils (Fig. 17). Since the availability of water is important for ichnofossil formation (Hasiotis & Honey, 2000; Buatois & Mángano, 2011), high scores are attributed to the locations sited below the most elevated position of the Jezero shoreline (i.e., −2,243 m according to (Salese et al., 2020). This high score treshold is extended to −2,200 m to include the most elevated areas that are within the fluvial channel in the Neretva Valley.

This suitability scoring is also supported by empirical observations in the Nurra area (Permian-Triassic, Italy), where ichnofossil distribution is strongly controlled by the past water table level. Specifically, the Nurra sedimentary sequence records the transition from a fluvial ecosystem with a permanent water table (Permian) to hyper-arid conditions (Triassic) (Baucon et al., 2014). Bioturbation ichnofossils are abundant and diversified in the Permian deposits, whereas the Triassic hyper-arid deposits tend to be unbioturbated (Fig. 18). The lower Triassic hyper-arid period is documented in many other European deposits, often presenting comparable ichnological characteristics with those of the Nurra area (Durand, Meyer & Avril, 1989; Bourquin et al., 2007; Cassinis, Durand & Ronchi, 2007; Durand, 2008).

Even if microbial life can tolerate hyper-arid environments (Bull & Asenjo, 2013), high suitability scores for biostratification (W = 4) are here attributed to past aqueous environments. This is explained by the fact that increasing aridity reduces microbial diversity and abundance (Maestre et al., 2015). In addition, carbonate precipitation in aqueous environments is an excellent mechanism for biosignature preservation (Farmer & Marais, 1999; Horgan et al., 2020). This suitability scoring approach is supported by observations at the Collio Basin (Permian, Italy), which preserves lacustrine carbonates with abundant biostratification ichnofossils (Berra, Felletti & Tessarollo, 2019). In the Collio Basin, oncoids, stromatolites and MISS are associated with lacustrine palaeoenvironments dominated by carbonates, i.e., spring-fed ponds at the toe of alluvial fans (Fig. 19). Similarly to the Collio Basin, the Jezero crater preserves carbonate-rich deposits in close proximity to the lake margin (Horgan et al., 2020).

From ichnological neglection to ichnological appreciation

The predictive model presented in this paper identified the areas of the Mars 2020 landing site with the highest potential for ichnofossil location. This approach adds value to astrobiological research because ichnofossils have been largely neglected in the search of Martian life, with the only exception of microborings (e.g., Staudigel et al., 2008; McLoughlin et al., 2010; Lepot, 2020), stromatolites (e.g., Mustard et al., 2013b; Hays et al., 2017) and MISS (e.g., Noffke, 2015; see the review by Baucon et al., 2017 for a more complete list of ichnological approaches to astrobiology). Bioturbation ichnofossils have completely been disregarded in the search for Martian life, with few exceptions (e.g., Baucon et al., 2020a).

The neglection of ichnofossils in astrobiology plausibly derives from a common assumption, that is, the microbial nature of (eventual) Martian life. Microorganisms are regarded as the most likely candidates for a putative biota of an extraterrestrial habitat (Horneck, 2000). By contrast, ichnofossils conjure up with images of macroscopic worm burrows. It should be however noted that ichnofossils “are not the only result of the activity of burrowing or grazing macroorganisms, but also of microbes that interact with the sediment” (Noffke, 2009: p. 173). On Earth, this is not only the case of microborings but also of macroscopic bioturbation structures such as Trichichnus. Trichichnus is a macroscopic (0.1–0.7 mm in diameter) cylindrical structure deriving from bioelectrical operations resulting from bacterial activities in the oxygen-depleted part of sediments (Kȩdzierski et al., 2015). Macroscopic bioturbation ichnofossils, consisting of winding burrows, are documented from 2.1 Ga deposits of Gabon, and are tentatively attributed to the bioturbating activity of ameboid cell aggregates (El Albani et al., 2019). Tubular titanite tubes, possibly representing microbioerosion ichnofossils, are widely reported from ∼3.5 to ∼2.5 Ga metamorphosed basaltic pillow-lavas, basaltic hyaloclastite breccias and metamorphosed volcanoclastic rocks (Staudigel et al., 2008; McLoughlin et al., 2010; Lepot, 2020). 1.7 Ga deposits preserve the oldest unquestionable microboring organisms on Earth (Zhang & Golubic, 1987). Early Archaean (3.49 Ga) conical stromatolites are documented from the Pilbara Craton, Australia (Allwood et al., 2007), and even older (Nutman et al., 2016), but controversial (Allwood et al., 2018), ones are described from >3.7 Ga deposits of the Isua supracrustal belt, Greenland. Undisputed branched columnar and domal stromatolites are known from the ∼2.9 Ga Steep Rock Group in north-west Ontario (Grassineau et al., 2006). The oldest MISS have been documented in the 2.9 Ga old Pongola and Witwatersrand Supergroup, as well as the 3.2 Ga old Moodies Group, South Africa (Noffke et al., 2006; Heubeck, 2009; Noffke, 2009).

These examples show that the macroscopic record of microbial ichnofossils is abundant, spread by many geological environments, types of rocks and ages and well preserved on Earth, therefore the application of ichnology to astrobiology is much required. Although microbes are usually regarded as the most likely candidates for a putative extraterrestrial biota (Horneck, 2000), Bains & Schulze-Makuch (2016) suggest that the evolution of complex macroscopic life is nearly inevitable in any world where life has arisen and sufficient energy flux exists. This further encourages the search for extraterrestrial macroscopic ichnofossils.

Advantages and limitations of ichnological predictive modelling

Predictive modelling has never been used to detect ichnofossils, either on Earth or beyond. To date, predictive modelling methods have successfully been used to detect archaeological artefacts (Brandt, Groenewoudt & Kvamme, 1992; Kamermans, 2004; Balla et al., 2014; Nicu, Mihu-Pintilie & Williamson, 2019) and, more recently, body fossil sites (Oheim, 2007; Anemone, Emerson & Conroy, 2011). As such, this study opens new avenues not only for the search of extraterrestrial life but also for palaeontological research on Earth.

There are multiple advantages in the application of predictive modelling to the search for ichnofossils on Mars (or other extraterrestrial locales). In parallel to the advantages of predictive modelling in archaeology (Balla et al., 2014), predictive modelling can contribute to astrobiological research by minimizing the requirements for trial observations and excavations, as it detects areas of high biological probability. On Earth, predictive modelling proved to enhance the chances of finding body fossils and archaeological artefacts (Oheim, 2007; Vaughn & Crawford, 2009), therefore, the same advantages can plausibly be expected for the search of life beyond Earth. By maximizing efforts in high probability areas, resources (time, energy, and money) used for surveying areas with little potential can be reallocated to mapping and observation efforts in higher probability areas (Vaughn & Crawford, 2009). The application of predictive modelling is particularly promising for the detection of ichnofossils. Indeed, most ichnofossils cannot be transported, with few and easily detectable exceptions (Seilacher, 2007; Buatois & Mángano, 2011). For instance, a burrow cannot be transported from delta front to prodelta settings without being destroyed by the transport processes themselves. For this reason, ichnological predictive models are particularly reliable because they mostly depend on the in situ characteristics of the rock record. By contrast, predictive modelling of body fossils should take into account also transport factors, e.g., the body fossil of a delta front organism can be found as an allochthonous element in prodelta deposits.

The predictive modelling approach followed in this paper is not without limitations. As pointed out by other authors (Brandt, Groenewoudt & Kvamme, 1992), one weakness of the weighted approach to predictive modeling is the element of subjectivity in the weights assigned to each suitability factor. Other statistical approaches are available to deal with the subjectivity factor, e.g., Bayesian statistics (Millard, 2005), machine learning (Yaworsky et al., 2020) and graph similarity analysis (Mertel, Ondrejka & Šabatová, 2018) have been applied to predictive modelling. However, there are no field data for the Jezero crater and only Earth-type life is known, therefore, application of more sophisticate statistical methods would bring the risk of overinterpretation, that is, reading too much into the limited dataset available. Fortunately, the element of subjectivity is partly reduced by the peculiarities of the ichnofossil record. Ichnofossils can be universal biosignatures, i.e., they are ideally capable of detecting any type of life because they are independent from morphology, size and biochemistry of the life form they document (Baucon et al., 2017). For this reason, ichnological assumptions based on Earth-type life can readily be applied to the search of extraterrestrial ichnofossils. This universal nature of ichnofossils, and the study of reference ichnosites from different areas and geological periods, dampen the inherent subjectivity of the weighted approach to predictive modelling.

Temporal resolution is a second major limitation of the approach followed in this paper. The ichnological predictive model resulting is aimed at detecting ichnofossils formed during the presence of the Jezero palaeolake, but it is not possible to exclude a priori the existence of habitable conditions prior or after the Jezero palaeolake. In parallel to the future directions purported by Brandt, Groenewoudt & Kvamme (1992), a future direction for astrobiological predictive modelling is analyzing separately each geological period of Mars, thus generating an ichnological model for each period. At present this approach is not feasible because of data limitations and the urgency of focusing on the most promising subject, i.e., the habitable Jezero palaeolake, where Preserverance touched down on Mars on Thursday, Feb. 18, 2021.

Spatial resolution is only an apparent limitation of the approach. Specifically, the fine resolution of the ichnological pictures (e.g., Figs. 7, 9, 11) contrasts with the coarser resolution of the HiRISE-derived photogeologic map (Fig. 3) on which our model is based. Nevertheless, the resolution of the HiRISE imagery/photogeologic map does not preclude the results of our paper. In fact, HiRISE resolution allows to infer the characteristics of Mars at the scale of the depositional (sub)environment. These characteristics plausibly influenced the distribution of (eventual) life on Mars, therefore, they have a predictive value. The vast number of papers interpreting - directly or indirectly - HiRISE imagery further supports our approach (Hoefen, 2003; Ehlmann et al., 2008b; Ody et al., 2013; Goudge et al., 2015; Bramble, Mustard & Salvatore, 2017; Palumbo & Head, 2018; Rogers et al., 2018; Kremer, Mustard & Bramble, 2019; Horgan et al., 2020; Mandon et al., 2020; Stack et al., 2020). In addition, the distribution of trace fossils (fine-scale) depends upon the characteristics of the depositional environment (coarse-scale). This well-established relationship between the ichnofossil-scale and the scale of the depositional environment (see Seilacher, 2007 and references therein) strongly supports our study. By contrast, the HiRISE resolution is too coarse to spot eventual centimetric ichnofossils on Mars, but this is not the aim of this study. In other words, the approach of our paper parallels the challenge of detecting sand grains from satellite imagery of Earth. A microscope is needed to detect sand grains, but satellite imagery is enough to assess the possibility of finding sand grains on a beach.

The third limit is the lack of testing, i.e., at present is not possible to evaluate the performance of the model. Predictive modelling remains useless without some form of test (Brandt, Groenewoudt & Kvamme, 1992). However, this limitation will plausibly overcome in the next years, during which the Perseverance rover will thoroughly explore the deltaic deposits of the Jezero crater. This limitation can turn out to be an advantage because the here proposed model can be used as a planning tool for organizing the path and operations of the Perseverance rover.

An ichnological strategy for the Perseverance rover

Guidance in planning survey campaigns is among the major applications of predictive models on Earth (Balla et al., 2014). In parallel, the predictive maps resulting from this paper find their ideal application in suggesting a traverse plan capable of maximizing the scientific (ichnological) gain of the Perseverance rover. In other words, the predictive maps can guide or focus the Perseverance rover on the sites with the highest potential for ichnofossils. Accordingly, we identified an ichnological strategy for the Perseverance rover (Fig. 23), indicating (1) ten high-suitability sites, (2) the ichnofossil types that are more likely to be present at each site, (3) the detection strategy that is best suited for each site.

With the regard to the third aspect, visible light photography is enough to carry out an ichnological survey on Mars. Ichnofossils are morphological evidence of biological behaviour, therefore they offer the practical advantage of requiring no sophisticate tools to be detected. This said, the Perseverance rover provides the opportunity of more sophisticate analyses. The Mars 2020 mission will not only seek the signs of ancient life in situ but also cache a maximum of 43 samples for a possible return to Earth by a follow-on mission (Williford et al., 2018; Grady, 2020; Muirhead et al., 2020; NASA, 2021). Predictive maps can therefore suggest the optimal cache strategy, based on the assumption that each type of biogenic structure (bioturbation, bioerosion, biostratification) requires a specific type of analysis due to its size constraints. Specifically, grain size constrains the size of the smallest bioturbation and biostratification ichnofossils that may be present in a given sedimentary unit. Bioturbation and biostratification structures are formed by the displacement and reorganization of sediment grains, therefore they cannot be smaller than the smallest grain. Such size constraint does not hold for bioerosion structures, which are formed by creating an opening in hard substrata. Consequently, the imaging tools of the Perseverance rover are well suited for (potential) bioturbation and biostratification structures, but their resolution may be not enough for imaging the smallest microbioerosion structures if any are present on Mars. Imaging of microborings requires specific sample preparation techniques (e.g., casting-and-embedding technique) and SEM observations (Golubic, Brent & LeCampion-Alsumard, 1970; Wisshak, 2012). Returned samples allow similar sophisticated sample preparation techniques and analytical techniques (Grady, 2020).

The first site (Fig. 23) of the ichnological traverse is located within the 7.7 × 6.6 km ellipse in which the rover landed (see Farley et al., 2020 for more detail about the landing ellipse). Site 1 displays distal delta remnants, which are suitable for both bioerosion and biostratification. Site 2 and 3 are also related to a deltaic depositional setting, although their position is more proximal with respect to site 1. Our results suggest employing the imaging tools of the Perseverance rover for the search of eventual bioturbation and biostratification ichnofossils preserved in the deltaic units. The crater floor units outcrop at sites 4 and 5, which have a high potential for bioturbation and a moderate potential for bioerosion. The possible presence of microbioerosional ichnofossils suggests collecting samples for a future sample-return mission. Site 6 displays deltaic units that are particularly promising for bioturbation, thus encouraging imaging-based observations. Close to site 6, the carbonate-rich deposits of site 7 outcrop. Here, sampling is suggested in order to better evaluate the presence of stromatolite laminations. We suggest visiting site 8, located south of the Belva crater, because here the delta truncated curvilinear layered units outcrop. These units outcrop over a relatively limited area, therefore site 8 offers one of the few excellent exposures of these units. The substrate conditions of site 9 and 10 are particularly suitable for bioerosion, for which reason sampling is suggested. These sites are located along the border between areas suitable for bioerosion and bioturbation, hence they allow to maximize scientific gain.

It should be noted that scientific gain is not the only element of traverse planning, which can be seen as an optimization process in which science gain and the level of safety are maximized while driving energy is minimized (Ono et al., 2015; Ono et al., 2020; Fink et al., 2019). Identifying and avoiding terrain hazards are particularly important aspects for the safety of planetary rovers, e.g., the Spirit rover ended its mission because it got stuck in soft terrain and pointy rocks damaged the wheels of the rover Opportunity (Ono et al., 2015). The predictive maps resulting from this paper are released as raster files (Supplemental Information 2–4) and therefore they can be easily integrated in any optimization process related to traverse planning. Supplemental Materials (SM) also include a QGIS-ready zipped archive with both input and predictive layers (SM5). The raster maps provided in this paper can be combined with other information that is useful to optimize the success of the traverse, e.g., terrain smoothness maps and/or maps of terrain hazards. This step is important because some of the predicted areas may be not safely accessible by the Perseverance rover.