Framing discussion
In this doc
I’ve sketched out a rough outline of what the discussion could include and my initial thoughts on how we may interpret the figures/selected results. I would love feedback on the figures themselves as well as what information we may want to include when interpreting the results. To refresh, I’ve included the study objectives and hypotheses here.
Hypotheses
H1: The AGI model will perform better than the dissolved oxygen and base model, and the dissolved oxygen model will perform better than the base model.
Study objective: Which model performs the best and presents the best predictions (i.e., best predictive performance scores, most ecologically realistic suitability maps)?
H2: The inclusion of dissolved oxygen and the AGI at deeper depths will result in better performing/more ecologically realistic habitat suitability predictions relative to the respective models considering surface values alone.
Study objective: How does dissolved oxygen/AGI at different depths influence habitat suitability predictions relative to oxygen/AGI at the surface? How does the relative importance of each depth layer differ?
Note: This hypothesis will likely be addressed using a supplemental figure depicting how models with DO/AGI at deeper depths improve model prediction metrics.
H3: At lower latitudes and near shore, the AGI will reach values close to 1.
Study objective: What spatial patterns can be observed with dissolved oxygen and the AGI?
H4: The base model will predict higher habitat suitability in areas or during seasons or periods with lower dissolved oxygen (e.g., upwelling periods or La Niña years) through the water column relative to the dissolved oxygen and AGI models.
Study objective: How do the habitat suitability maps differ between the models? How do these variations compare for different points in time with varying degrees of oxygenation?
Discussion
Will first review hypotheses and where we landed with each of them given the results.
Overarching Result 1: Spatial insights from including DO and AGI relative to the base model
Base and DO models identify low DO area of Pacific North Equatorial Current (NEC) as highly suitable habitat, while AGI only model doesn’t. Possibly showing a distinction of including metabolic demands relative to environmental features alone.
- If AGI HSI map is more ecologically accurate, could indicate that the AGI model provides distinct insight better capturing habitat use than models that don’t account for oxygen (base model) or that account for oxygen alone (DO model) rather than its interactive effects with temperature and the physiological outcomes.
- DO and AGI models have considerable distinctions regarding degree of high habitat suitability. Could be because including DO and temp separately doesn’t capture interactive effects that can generate low suitability areas?
Implications for understanding immature mako habitat use.
All modeled maps indicate high coastal habitat suitability, which is in agreement with previous work describing immature/juvenile mako habitat use (Nosal et al. 2019);(Nasby-Lucas et al. 2019). All models outside of the AGI model, however, also indicate offshore habitat suitability and a wide latitudinal spread up through the CCS (Carreón-Zapiain et al. 2018);(Nasby-Lucas et al. 2019).
The SCBe still holds a high concentration of high habitat suitability across all model types, agreeing with previous tagging efforts completed for immature mako sharks (Nosal et al. 2019);(Nasby-Lucas et al. 2019).
- This could have concerning conservation implications as these coastal and low latitude regions in the CCS may be some of the most metabolically vulnerable under future climate scenarios (Deutsch et al. 2015).
Large portions of area at 250m have AGI < 1, especially around the NEC. Could indicate that while this area is a sought after foraging zone for many sharks, including larger/adult makos, it may be metabolically stressful. This region overlaps with areas of strong habitat compression over the OMZ where DO concentrations at 100m are 3 ml/L or less, and contains areas known to be avoided by makos (Byrne et al. 2024). This area is also near part of the western warm pool of the Pacific, an area with high SSTs (>28 degree), low salinity, and low dissolved oxygen, which may further compress metabolically sustainable habitats through the water column near these coastal low latitude regions (O’Brien et al. 2014; Griffiths et al. 2019).
Sharp thermocline and low DO areas (i.e., metabolically stressful habitat) have been targeted for foraging as prey items are concentrated towards the surface, which can explain the trips out to this region (Polovina et al. 2004);(Stewart et al. 2019);(Logan et al. 2023);(Ryan et al. 2017).
Patterns of avoidance or long term use of this area provides further evidence that only the AGI model was able to capture the reduced suitability in this area, having better agreement with known habitat use from tracking data and previous work done for the species in this region. Has been suggested that the interactive effects of low DO and high temps may be causing this avoidance, rather than the effects of each feature in isolation.
Been suggested that the shallow depth of the OMZ within the NEC combined with warm SST creates severe habitat compression for makos and may act as a soft barrier to their southward movement (Byrne et al. 2024).
Important implications for future habitat use and nursery area designation if OMZ continues to shoal and temperatures continue to rise along the coast and at lower latitudes.
Could be extrapolated to other sharks that use the SCBe as a rookery (e.g., blue sharks) (Nosal et al. 2019).
Reference papers that have observed low DO waters or OMZ depth shaping habitat use of high-DO demand species: Gilly et al. (2013)
HSI maps across ENSO phases for the DO and AGI model. Percentages during the neutral year represent the percent area where the HSI > 0.75, while percentages for the La Niña and El Niño year represent the percent change in HSI > 0.75 area relative to the neutral year. The “neutral year” presents HSI averages from Jan. 2013 - Dec. 2013, the “La Niña year” presents averages from Sept. 2010 - Nov. 2010”, and the “El Niño year” presents averages from Nov. 2014 - Jan. 2015. These windows were selected to capture the most extreme conditions for the respective La Niña or El Niño conditions. The fill for the La Niña and El Niño years represent change in HSI values relative to the neutral year. Prey items at the top are listed in order of importance and GII values were calculated following the methods described in Preti et al., 2012.
Note: Please see figure caption for how ENSO rasters were averaged. I would love feedback if there is a more recommended way for going about this!
Different ENSO phases have also altered habitat suitability and species distributions in the CCS. Here, metabolically unsuitable habitat (AGI < 1) expanded by 2.5% when comparing a normal to LN year, and as these phases are anticipated to become more extreme with climate change, may result in bigger sways of habitat contractions. Additionally, AGI reductions during LN conditions were observed, providing a preview of possible habitat loss as ocean deoxygenation intensifies under anticipated climate change scenarios.
ENSO impacts on habitat suitability and shifts in species locations: (Ohman et al. 2017)
Observed changes in CCS during 2011-2012 LN: (Bjorkstedt et al. 2012)
ENSO impacts on shark observations: Tiger shark predicted occurrence was least likely during strong LNs possibly due to high energetic requirements but increased during strong EN (Osgood, White, and Baum 2021). For other shark species, also observed that occurrence was driven by ENSO effects on shifting prey distributions.
Increased intensity of ENSO phases with climate change in CCS: (Yoon et al. 2015)
Diet data note: diet cruises were completed between 30-48 degrees latitude and extended out to the U.S. EEZ, so offshore prey consumption is not considered here.
All maps that either considered DO or the AGI indicated habitat loss during the LN phase with general patterns of reduced offshore habitat suitability, and base model depicted little to no change across ENSO phases. Could be due to high metabolic demands of makos and reductions in metabolically viable habitat due to strong LN conditions.
Highlight that it is important to include DO and/or AGI as the base model did not capture these spatial shifts, but it’s interesting that DO models indicated much larger areas of high habitat suitability relative to the AGI model. The AGI model also captured the largest amount of high habitat suitability loss (~1.4%) possibly suggesting this model approach could be the best positioned for capturing the spatial consequences of predicted deoxygenation, as other model types poorly captured these expected habitat contractions.
Biggest contractions observed at lower latitudes and increased areas of high habitat suitability along the coast at higher latitudes. Could be due to increased productivity/prey availability in these regions due to increased mixing in addition to the reductions in metabolic habitat at lower latitudes. Combined push/pull factors?
Anticipated that LN conditions (low SST, high Chl) yield high jumbo squid catch, possibly explaining the observed shift in diet (switching from Saury to Jumbo squid). Jumbo squid have plastic metabolic rates an life histories, with the ability of supressing their metabolic rates up to 45% (Medellín-Ortiz, Cadena-Cárdenas, and Santana-Morales 2016). Observed that LN conditions (expanded low DO areas, low pH) have direct consequences on jumbo squid habitat driving them to retreat to shallow areas to feed and repay oxygen debts. Could explain why we see the switch in primary prey during LN conditions, and thus could be a critical source of prey for makos under anticipated climate variability (Rosa and Seibel 2008).
Unsure why Saury not appearing in diet during LN year, due to metabolic sensitivity and seasonal distributions, would have possibly overlapped. Or maybe as Saury are more offshore and during LN makos were more coastal, wouldn’t have overlapped? (Preti et al. 2012). Diet data was collected within the U.S. EEZ and between 31-48 degrees latitude, so saury could have been outside of this sampling range.
For prey overlap insights, I’d love to confirm interpretation with someone who is more familiar with the Saury/Squid habitat use/shifts!
During the 2014 EN, all models picked up a decline in habitat suitability, with the model considering DO showing the largest contraction (1.8%). Generally, areas of high habitat suitability appeared more offshore, while patterns of habitat suitability loss were concentrated along the 15 degree latitude line and near shore at moderate latitudes.
Jumbo squid catch observed to decline under EN condtions (high SST, low Chl) (Medellín-Ortiz, Cadena-Cárdenas, and Santana-Morales 2016), and high SST conditions contract areas of suitable habitat for Saury in the eastern Pacific (Yu et al. 2021). Could be retreating more offshore to reach more suitable temperature ranges? Observed that off of Peru, coastal habitat suitability declines during strong EN conditions (Yu, Chen, and Zhang 2019). Could explain how they remained as top prey item, overlapping with offshore suitability for the makos.
Observed declines in habitat suitability for Saury under EN conditions could explain shift in importance, but remaining pelagic mako habitat could support why they are in top three important prey (Chang et al. 2019);(Preti et al. 2012)? While during LN season distributions were more coastally restricted?
For prey overlap insights, I’d love to confirm interpretation with someone who is more familiar with the Saury/Squid habitat use/shifts!
Overarching Result 2: Inclusion of DO/AGI into habitat models
DO/AGI at 0m daily resolution and at 250m annual resolution consistently appeared in the top 3 most important predictors. Could be capturing different physiological processes/mechanisms at these scales and depth layers.
0m daily = more important for oxygen reloading and could speak to DO availability through water column. More relevant for more immediate processes like prey distributions and foraging?
250m annual = more important for driving metabolic constraints/areas that are more metabolically stressful. More relevant for broader scale temporal patterns of general habitat suitability?
Overall consistency of importance provides evidence for needing to consider these variables in future habitat modeling for makos, other species with high metabolic demands, and possibly ectotherms more broadly given anticipated deoxygenation relative to historical norms. These results also indicate that including AGI/DO at different temporal resolutions may provide more diverse insights covering the different processes they relate to. Could say the same about exploring different spatial scales in future?
Bathymetry and temp appear as most important predictors for the base model, and while temp remains as a top predictor for the other model types, bathymetry shifts down. Bathymetry has appeared to be an important predictor for mako SDMs in the CCS (Lezama-Ochoa et al. 2024; Brodie et al. 2018). Other studies have considered that horizontal mako movement may be correlated with bathymetry, but no evidence has been strong enough to formalize this (Sepulveda et al. 2004). Bathymetry declines in importance for other model types, possibly because its role is outweighed by the influence that DO/AGI and temp play.
The effect of DO and AGI on the probability of mako shark presence at 0m and 250m, and at daily, seasonal, and annual temporal resolutions. The red line is the mean and the grey bars represent the 25% CIs. These values were calculated by running 20 iterations of the DO and AGI models. Grey bars at the top of the plot represent data availability at each DO/AGI value. Note: Figure will be moved to supplement.
Low DO at surface at short time scales negatively correlated with species presence, while at depth, higher DO values had negative correlations with species presence. At depth, high oxygenation is found offshore and at higher latitudes, and thus this pattern may not be based on the species physiological preferences, and more on where the species were actually found when pooling locations across the region and study domain. Future studies may seek to look at DO/AGI patterns at a finer spatial scale across different latitudes, regions (e.g., near vs. offshore) and seasons (e.g., upwelling) as the relationships may be more physiologically meaningful. Could also provide different insights into the use of these variables.
Similar patterns observed for the AGI at depth, and at the surface, a more narrow band of AGI values between 4-5 were observed as being the most optimal at daily/seasonal resolutions. Indicates that oxygen supply at the surface should be about 4-5x greater than demand.
However, the AGI only represents the most minimal of oxygen demands (solely those for maintenance including survival, feeding, and movement, but not growth), and thus having additional empirical measurements or improved methods for estimating metabolic demands would result in a more accurate understanding of how the interactive effects of temperature and oxygen availability may drive habitat suitability (Clarke et al. 2021).
Note: Figure got horizontally squished, so axis text appearing smaller than expected.
Note: Unsure if I should include the significance results here given previously published concerns about statistical tests comparing model results given arbitrary sample sizes (White et al. 2014). Happy to hear thoughts!
The inclusion of DO/AGI improved model predictive performance when tested using new data (AUC, TSS) and improved how well the model explained the data (deviance explained).
Improvement not entirely surprising given that we know DO/metabolic performance can be a key variable driving species distributions.
Combined DO-AGI model slightly out-performed the other model types, which could be relevant if suitability maps produced by this model seem to be the most ecologically realistic?
Biggest improvement was in deviance explained, thus showcasing the importance of accounting for DO and/or metabolic performance when understanding mako habitat suitability.
Will end emphasizing the necessity for considering role of oxygen and metabolic demands when predicting and projecting species distributions and to continue investigating what species this may be the most critical for and how they can be used to better understand species redistributions into the future.
Consequences for management include:
Better understanding of multi-nation habitat use and how conservation risk varies across these regions. Emphasize importance for future climate models and species habitat projections that appropriately capture the relevant spatiotemporal resolution and spatial extent to guide meaningful and effective management decisions/plans.
- Cite work that is being done for SMPI6 and MOM6 for CEFI which includes spatial extent through Baja
Indications of how temperature and deoxygenation have differing effects on a species (either directly or indirectly through their prey) creating different drivers for observed shifts.
Coastal areas will face more challenges for the protection of species under climate change, which may have considerable economic implications for these regions (i.e., changing access to fished species).
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