Jéssica Fernanda Ramos Coelho¹, Sergio Maia Queiroz Lima¹, Flávia de Figueiredo Petean¹
1Departamento de Botânica e Zoologia, Centro de Biociências, Universidade Federal do Rio
Grande do Norte, Campus Universitário, BR 101 s/n, 59078-900, Lagoa Nova, Natal, RN, Brazil.
E-mail: jess.fernandd@gmail.com
ABSTRACT
Climate change is a growing global-scale issue with an increasing body of evidence revealing its potential to impact patterns of distribution of living organisms. Some biological and ecological traits make some organisms more vulnerable than others to climate-related abiotic stress, with species of slow growth, late maturation, and limited geographic distribution being of particular concern. Riorajini is a tribe of four sympatric skates’ species occurring in the temperate southwestern Atlantic falling into this category. Considering skates’ life-history traits, coupled with species’ ecological requirements, we hypothesize a poleward geographic shift with potential shrink in overall distribution range of these species as a response to climate change impacts at the coastal zones this clade occupies. We compiled satellite-derived raster imagery and data on species occurrence from public online databases to model the ecological niche of Riorajini species under present and future (2100, RCP 8.5) climatic scenarios. Between the two climatic scenarios modelled per species, we calculated metrics of niche overlap, stability, expansion, and unfilling, as well as niche similarity and equivalency. All analyses were conducted in R. Our results show high overlap between the two climatic scenarios and reveal an expansion in up to 20% in future environmental suitability for the occurrence of the tribe. The expansion occurred to deeper zones (longitudinal shift), however still within the bathymetric limit of the continental shelf. Although positive at first glance, future research focusing on ontogenetically different responses (adults versus eggs capsules) to cascade events resulting from global warming are needed to address the physiological resilience of this group. Also, consequences of such shift can be detrimental to the local biota in newly invaded areas, as the introduction of new predatory species can affect negatively the dynamics of the community.
INTRODUCTION
Evidence for both terrestrial and marine biotas shifting ranges as a response to impacts of climate change are growing in the literature. Terrestrial organisms are changing distributions in latitude and elevation to cope with thermal stress (Root et al., 2003; Hickling et al., 2006; Chen et al., 2011). In the oceans, these differences are in latitudinal range and depth (Perry et
al., 2005; Nicolas et al., 2011). Perry et al. (2005) showed that two-thirds of the fish species
analysed from the North Sea respond to warming waters shifting in mean latitude, depth, or both, as well as a distribution boundary shift poleward. These changes are a reflection of a climate-related disequilibrium.
Long-term analyses show a global scale warming resulting from the continuous increase in the emission of greenhouse gases, likely an effect of anthropogenic activities (Houghton, 1996; Mann et al., 1999; Barnett et al., 2001; Rosenzweig et al., 2008). Such changes are occurring faster than most organisms can adapt to (Quintero & Wiens, 2013), and, besides the difficulty to attribute an impact as a consequence of anthropic global warming, studies are consistently finding rather compelling evidence of theoretical predictions for climate change- related impacts on biodiversity distribution (Hughes, 2000; Walther et al., 2002; Parmesan & Yohe, 2003). The accumulation and synergistic effect of these changes on ecosystems can alter abiotic conditions of the planet to the extent that living organisms will be pressured towards the maxim “move, adapt or die”.
Climate change is not likely to impact species or habitats in the same way. For example, for elasmobranchs, animals with long life cycles, slow growth, and late maturation, the “move” option seems more feasible in face of environmental stress (Stevens et al., 2000; Helfman et
al., 2009). Fewer physical barriers in comparison with terrestrial habitats makes it easier to
move and disperse in the marine environment, however, some biological and ecological characteristics, along with a lack of connectivity between some populations, makes this group more susceptible than others to adverse events (Somero, 2010). In skates, for example, sessile eggs capsules and philopatry imply in a restricted area of occurrence and strong reliance on particular habitats, which adds to the vulnerability of this group to climatic changes in comparison with other pelagic elasmobranchs (Dulvy & Reynolds, 2002; Parmesan, 2006; Dulvy et al., 2014; Di Santo, 2015).
Riorajini (sensu McEachran & Dunn, 1998) is a tribe of four skates of the Arhynchobatidae family occurring in sympatry in the southwest Atlantic Ocean, a region that
harbours the highest number of threatened chondrichthyan species in the Neotropics (Field et
al., 2009). According to the IUCN latest global assessment, the species in this group are
evaluated as “endangered” (EN): Atlantoraja castelnaui (Hozbor et al., 2004), and “vulnerable” (VU): A. cyclophora, A. platana, and Rioraja agassizii (Massa et al., 2006; Kyne et al., 2007; San Martín et al., 2007). These species occur mainly at the Warm Temperate Province, as defined by Spalding et al. (2007), influenced at north by the Cabo Frio upwelling system, and at south by the effects of cold-water masses from Falkland current (Peterson & Stramma, 1990; Coelho-Souza et al., 2012). A range of occurrence limited to the shore makes these taxa more vulnerable as coastal zones are considered to be more exposed to natural climate-related hazards (Nicholls & Small, 2002; Nicholls & Cazenave, 2010).
Frameworks on how to answer climate change-related questions (e.g.: Broennimann et
al., 2012, and Guisan et al., 2014) coupled with the improvement of computational models able
to address biological issues boosted our capacity to test eco-biological-based hypothesis, and to visualize theoretical scenarios more efficiently. For example, Representative Concentration Pathways (RCPs) represents data from the literature on possible paths for the main driving agents of climate change. There are currently four RCPs available, from mild to more extreme scenarios, varying from 2.6 to 8.5 W/m² (ranging from ~490 to ~1370 ppm CO2, respectively)
by the end of the century, predicted as for the trends in emission of greenhouse gases and land use (Van Vuuren et al., 2011). These emissions translate into an increase of up to 1.7°C in mean temperature in the 2.6 RCP scenario, and up to 4.8°C in the 8.5 RCP scenario, both compared to pre-industrial levels (Stocker et al., 2013). Studies on the distribution of species under different geographic and temporal scenarios benefited from these advances and became more popular (Guisan & Zimmermann, 2000), however, studies focusing on the impacts of climate change in distribution patterns in the marine environment are yet scarce in comparison with terrestrial ones (Dambach & Rödder, 2011).
In a context of rapid climatic changes in a global scale, these are of particular interest given that the patterns of biodiversity distribution may interfere in ecosystems’ goods for human populations, such as fisheries (Blanchet et al., 2019). Besides, with an increasing number of species threatened with extinction detrimental to such changes (Thomas et al., 2004; Maclean & Wilson, 2011), the use of non-invasive methodologies to aid addressing such urgent matters has been necessary. There is an increasing body of literature in this regard, using models to identify ecological barriers to the distribution of species (Costa et al., 2017). Yet, perhaps because of sampling difficulties and costly logistics, studies focusing on elasmobranchs are to
some degree neglected compared to bony fish, molluscs, and marine mammals, for example, for which studies applying ENMs correspond to over half of the studies published so far considering marine taxa (Melo-Merino et al., 2020). Such studies are of paramount importance to provide the basis from which conservation efforts can be planned and implemented.
In this study, we compiled information from public biodiversity databases and RCP data to model current and projected (2100) distributions of Riorajini, a Neotropical tribe of four skates’ species. Considering life-history traits (e.g. slow growth, late maturation, and philopatry), ecological features (benthic, sedentary habit, coastal habitats, temperate waters) and putative future climate change impacts on coastal zones, we hypothesize Riorajini species will present a southward shift in their current geographic distribution and a reduction in the range of areas where these species are likely to occur in a future scenario of climate change (considering the extreme scenario of global warming impacts, RCP 8.5).
MATERIALS AND METHODS
Models of present and future climatic scenarios
We ran maximum entropy (maxent) ecological niche models (ENMs) for each one of the four species in the tribe following a correlative approach, which requires georeferenced sites of occurrence of a given species, and data characterizing abiotic, climatic conditions where such species is present (also called abiotic predictors, or layers) (Phillips et al., 2006; Robinson et
al., 2011).
Occurrence sites were derived from published literature and public online databases filtered by preserved specimens georeferenced in the area of known occurrence, to increase data reliability. Localities were partitioned into model training and testing points applying the ‘block’ method, suitable when spatial and temporal transferability is required (Muscarella et
al., 2014). Layers for future (2100) environmental conditions considered the worst-climatic-
scenario (RCP 8.5) for which 18 variations (e.g. minimum, maximum, mean) of three variables (salinity, temperature and currents velocity) for benthic maximum depth were available in Bio- ORACLE (Tyberghein et al., 2012; Assis et al., 2017). A Pearson’s correlation test was conducted to remove highly-correlated variables (|r| ≥ 0.8) from the analysis aiming to avoid multicollinearity (Warren et al., 2014) and model overfitting (Parolo et al., 2008). For comparative purposes, the ENM for the current climatic scenario, including only the variables
selected after the Pearson correlation test for future modelling, was also performed for each species, following the same procedure.
Environmental variables were scaled to equal dimension and resolution (0.833°, ~9km). ENMeval package was used to select the combination of maxent parameters to output the parsimonious model (ΔAICc = 0), which are considered the best models (Muscarella et al., 2014). ENMs were conducted in R program version 3.5.1 (R Core Team, 2018).
Statistical Analysis – Measuring differences
We followed the methodological framework proposed by Guisan et al. (2014) to map and measure the degree of change between niches of present and future climatic scenarios for each species. Niche expansion, stability and unfilling were measured by comparing intra- specific models for both climatic scenarios. Niche expansion refers to the portion of niche available in a future climatic scenario, but not occupied in the current scenario; niche stability reflects the proportion of climatic conditions available in both temporal scenarios; and niche unfilling refers to conditions of current climatic scenario that are not available in the projected future climatic scenario (Guisan et al., 2014). The Schoener’s D index was calculated to measure niche overlap between present and future models per species (Warren et al., 2008). Finally, niche similarity and equivalency were measured to test if present and future niches will be more similar or equivalent than expected at random (Broennimann et al., 2012). All niche metrics were calculated using ade4 package version 1.7.13 and ecospat package version 3.0 in R (Chessel et al., 2004; Dray & Dufour, 2007; Dray et al., 2007; Bougeard & Dray, 2018; Broennimann et al., 2018).
RESULTS
Six uncorrelated environmental variables were selected for the ENMs of current and future climatic scenarios: temperature mean (°C), salinity mean and range (psu), current velocity mean, minimum, and maximum (m-1). The most parsimonious models included different feature classes and values of regularization multiplier compared with default maxent models (Table 1).
Table 1: Summary of the best combination (ΔAICc = 0) of parameters established by ENMeval package (Muscarella et al., 2014) per species and climatic scenario: P – present; F – future. n: number of occurrence points in the dataset; FC: Feature Classes allowed in the model (L – linear; Q – quadratic; H – hinge); RM: Regularization Multiplier; AUC: Area Under ROC curve per model; sd: standard deviation of AUC.
Species Code n FC RM AUC sd
Atlantoraja castelnaui Acas 31 P LQ 0.5 0.986 0.003
F LQ 1.5 0.982 0.003
Atlantoraja cyclophora Acyc 60 P LQ 0.5 0.991 0.001
F H 2.5 0.991 0.001
Atlantoraja platana Apla 30 P H 2.5 0.977 0.004
F H 4.0 0.977 0.005
Rioraja agassizii Raga 36 P LQH 2.5 0.978 0.003
F H 2.0 0.981 0.003
The importance of each of the six abiotic predictors included in the models varied in each climatic scenario modelled per species (Table 2), as well as the range of values of the three main environmental variables for the models (Table 3). Within species, niche overlap and stability were overall high (> 80%), and values of niche expansion and unfilling were low (< 22%), suggesting the abiotic conditions currently required for the existence of these species in that area will be available in a future of warmer climatic conditions (Table 4). Both climatic scenarios are also significantly more similar than expected by chance (Figures 2, 4, 6, and 8).
Table 2: Permutation importance (%) per variable per species for present (P) and future (F) climatic scenarios.
Acas – Atlantoraja castelnaui; Acyc – A. cyclophora; Apla – A. platana; Raga – Rioraja agassizii. Bold highlights
the variables of higher contribution (Σ > 80%) to models.
Acas Acyc Apla Raga
P F P F P F P F
Temperature mean 94.7 89.1 96.9 51.1 17.1 2.4 48.3 73
Salinity range 0.5 5.5 0.1 23.4 39.8 72 44.6 20.7
Salinity mean 0.7 0.9 1.3 17.7 2.4 0.3 2 2.7
Current velocity minimum 0.1 0.2 0.5 5.8 36.5 18.2 0.8 2
Current velocity mean 1.5 2.5 0.3 1.9 3.3 6.2 1.5 0.1
Current velocity maximum 2.6 1.7 0.9 0.1 0.9 0.9 2.8 1.4
Table 3: Minimum and maximum values of the three main variables to the ENMs of Riorajini species in present (P, grey shaded) and future (F, white) climatic scenarios modelled. Acas: Atlantoraja castelnaui; Acyc: A.
cyclophora; Apla: A. platana; Raga: Rioraja agassizii.
Salinity mean (psu) Salinity range (psu) Temperature mean (°C) Acas P 33.20–36.72 0.35–1.25 9.34–21.84 F 33.26–37.21 0.38–1.42 11.53–23.99 Acyc P 33.60–36.61 0.43–1.20 10.59–19.67 F 33.83–37.04 0.38–1.34 12.36–21.77 Apla P 34.05–37.12 0.13–1.40 3.53–25.47 F 33.94–37.79 0.18–1.53 4.37–28.09 Raga P 33.64–37.11 0.47–1.59 10.59–25.63 F 33.85–37.77 0.37–1.81 12.36–28.34
ENMs show an increase in habitat suitability for the occurrence of A. castelnaui (Figure 1) and R. agassizii (Figure 7) along the latitudinal gradient they occupy, and in La Plata river mouth. For A. cyclophora, such increase occurs more expressively at the Brazilian coast (Figure 3). There is a slight loss in environmental adequacy for the occurrence of A. platana near the coastline of Rio de Janeiro (23°S) but an overall increase in habitat suitability in deeper areas, still constrained to the continental shelf (Figure 5); besides, occurrence sites plotted into the future modelled climatic scenario fall into areas of up to 2°C warmer than the present scenario.
Figure 1: Ecological niche models of present (left) and future (right) climatic scenarios, showing degrees of environmental suitability for the occurrence of Atlantoraja castelnaui (Acas).
Figure 2: Niche dynamics of Atlantoraja castelnaui. Red arrow in the bottom left graph indicates direction of shift of the distribution’ centroid between the two climatic scenarios. Green: present climatic conditions; red: projected future climatic scenario (2100, RCP 8.5); purple: overlap between scenarios.
Figure 3: Ecological niche models of present (left) and future (right) climatic scenarios, showing degrees of environmental suitability for the occurrence of Atlantoraja cyclophora (Acyc).
Figure 4: Niche dynamics of Atlantoraja cyclophora. Red arrow in the bottom left graph indicates direction of shift of the distribution’ centroid between the two climatic scenarios. Green: present climatic conditions; red: projected future climatic scenario (2100, RCP 8.5); purple: overlap between scenarios.
Figure 5: Ecological niche models of present (left) and future (right) climatic scenarios, showing degrees of environmental suitability for the occurrence of Atlantoraja platana (Apla).
Figure 6: Niche dynamics of Atlantoraja platana. Red arrow in the bottom left graph indicates direction of shift of the distribution’ centroid between the two climatic scenarios. Green: present climatic conditions; red: projected future climatic scenario (2100, RCP 8.5); purple: overlap between scenarios.
Figure 7: Ecological niche models of present (left) and future (right) climatic scenarios, showing degrees of environmental suitability for the occurrence of Rioraja agassizii (Raga).
Figure 8: Niche dynamics of Rioraja agassizii. Red arrow in the bottom left graph indicates direction of shift of the distribution’ centroid between the two climatic scenarios. Green: present climatic conditions; red: projected future climatic scenario (2100, RCP 8.5); purple: overlap between scenarios.
Table 4: Niche overlap, expansion, stability, and unfilling measured between present and future climatic scenarios for each Riorajini' species. All values range from 0 (none) to 1 (identical). Acas – Atlantoraja castelnaui; Acyc –
A. cyclophora; Apla – A. platana; Raga – Rioraja agassizii.
Acas Acyc Apla Raga
Niche overlap 0.8953927 0.9241363 0.9520403 0.8097972
Niche expansion 0.1947291 0.10116894 0.05615735 0.10056594
Niche stability 0.8052709 0.89883106 0.94384265 0.89943406
Niche unfilling 0.2116576 0.07294093 0 0.1013457
DISCUSSION
Minding the caveats
Models for current climatic conditions developed in Chapter 1, including nine environmental predictors, showed temperature mean, salinity mean and range, and current velocity as variables of low importance for characterizing the abiotic niche of Riorajini (permutation importance < 12% for these variables, for all species). However, as explained in the Methods’ section, these are the only predictors available in Bio-ORACLE for modelling benthic habitats in an RCP 8.5 scenario. Nevertheless, we assume that this lack of data does not constrain our understanding on the shifts of the realised niche of these species. Bearing in mind this limitation, a variety of questions worth of debate arise from these results, and some of which will be briefly discussed in the next sections.
What explains the modelled increase in environmental suitability?
The abiotic niche of Riorajini did not markedly expand southward as hypothesized, but in longitude, towards deeper areas; however, still within the limits of the continental shelf, reinforcing the barrier that depth poses to the distribution of this group (Chapter 1). The environment is expected to change as climate changes. Tracking suitable environmental conditions leads Riorajini species to occupy deeper zones, and such range expansion in response to global warming is seemingly the pattern for many other marine organisms, from invertebrates to teleosts, elasmobranchs, and mammals, for example (Parmesan & Yohe, 2003; Perry et al., 2005; Parmesan, 2006; Molinos et al., 2015). Marine taxa exhibiting niche conservatism are even faster tracking these conditions, as for their tendency to maintain ancestral lineages’ characteristics of niche (Chivers et al., 2017), and fewer physical barriers constraining distribution (Pinsky et al., 2013). Such eastward shift in abiotic conditions necessary for the occurrence of these species is likely to push their distribution towards the limits of the
continental shelf. However, these species are not likely to surpass the barrier imposed by the continental shelf, since the abiotic conditions beyond this boundary are not suitable for their occurrence – high depth and high concentration of nitrate, for example (Chapter 1). Besides, a geographic expansion beyond the shelf requires physiological adaptations, for example, changes in osmoregulatory strategies (Treberg & Speers-Roesch, 2016), not feasible in 100- years scale.
Niches of A. castelnaui and R. agassizii are more at risk because these species presented the lowest values of niche overlap and highest values of niche unfilling between climatic scenarios modelled. This result was expected considering the shallower waters these species occur are likely to be more exposed to climate change impacts (Nicholls & Cazenave, 2010). Abiotic niches of these species are also likely to expand more drastically towards deeper areas, in ~20 and ~10%, respectively. Atlantoraja cyclophora and A. platana, on the other hand, presented the highest values of niche overlap between the two climatic scenarios (> 90% for both species), suggesting that the areas where these species currently occur will not face severe changes.
Overall for the group, low values of niche unfilling indicate that most of the current abiotic conditions required by the species will be available in the future. Similarly, high values of niche overlap and stability (>80% in both metrics) suggest stasis between the two modelled scenarios. Nevertheless, it is important to consider that an increase in environmental adequacy does not necessarily translates into the organisms’ ability to occupy new climatically available areas, as other local forces might interact compromising dispersion (Vaz & Nabout, 2016). VanDerWal et al. (2013) draw attention to the complexity of the combined climate change impacts and other uncountable factors influencing species distribution in a way so that simply looking at the expected poleward shift in biodiversity geographic distribution detrimental to climate change underestimates the real effects of this phenomenon. To shed light on an often overlooked pattern arising from a phenomenon it is important to assess issues from different angles and be able to discuss beyond the obvious (or expected).
Beyond distribution
Temperature mean was the variable of higher contribution to the models of present and future climatic conditions for all analysed species except A. platana, which showed salinity range as the variable of higher contribution for both climatic scenarios (Table 2). While higher temperatures might be tolerable for adults, it is likely to be harmful for young and eggs (Pörtner
& Peck, 2010), as seen for skates Leucoraja erinacea (Di Santo et al., 2015) and Raja
microocelata Montagu, 1818 (Hume, 2019), for example.
Changes in biological and physiological aspects of elasmobranchs have been documented with probable link to global warming. Laboratory experiments simulating future concentrations of atmospheric carbon dioxide indicate behavioural alterations in sharks detrimental to water acidification (Green & Jutfelt, 2014), as well as a decrease in metabolic and hunting efficiency of these predators (Pistevos et al., 2015). Negative impacts in young bony fishes have also been documented, with Pistevos et al. (2017) study’s showing that ocean acidification modifies the perception of physiochemical cues by fish larvae with potential to