Climate change threatens Chinook salmon throughout their life cycle

Widespread declines in Atlantic and Pacific salmon (Salmo salar and Oncorhynchus spp.) have tracked recent climate changes, but managers still lack quantitative projections of the viability of any individual population in response to future climate change. To address this gap, we assembled a vast database of survival and other data for eight wild populations of threatened Chinook salmon (O. tshawytscha). For each population, we evaluated climate impacts at all life stages and modeled future trajectories forced by global climate model projections. Populations rapidly declined in response to increasing sea surface temperatures and other factors across diverse model assumptions and climate scenarios. Strong density dependence limited the number of salmon that survived early life stages, suggesting a potentially efficacious target for conservation effort. Other solutions require a better understanding of the factors that limit survival at sea. We conclude that dramatic increases in smolt survival are needed to overcome the negative impacts of climate change for this threatened species.

6) It is surprising that the model suggests that the populations are essentially at no risk of going extinct given the 'stationary' climate scenario. This does not seem realistic given the current status of these populations. 7) I think the 'Caveats' section should be part of the Discussion of the paper.
8) The Discussion highlights the weakness of the paper. Here the text basically runs through what is known about different processes or conditions that affect salmon survival. Nothing is particularly novel in this summary, nor does it highlight the main contributions of this specific modeling effort. Thus, it reinforces the lack of clarity about what this specific research activity contributes to our general understanding of Chinook salmon ecology and conservation.
Reviewer #2 (Remarks to the Author): I found this paper to be exceptionally well written and the topic to be of broad international interest as a study of the impact of climate on a commercially valued species that is now at risk. This work explores the population dynamic of 8 populations of Chinook salmon in the Snake River watershed. This paper uses a life cycle model and two climate change scenarios to model the potential population trajectories of these populations. It effectively tells the risks of changing climate, particularly at sea, to all these populations but especially the smaller ones. It is sobering in its assessment of the viability of these populations-not a happy story. However, it effectively looks at the fact that these fish persist in highly altered systems and actions in freshwater might mitigate or at least slow population declines and localized extinction. These findings are novel for the populations of interest and findings (and solutions) will they be of interest to not only the fisheries biology community but ecologists and also to the public. This work is a tangible story of the impact of change upon a species that captures the imagination of a broad spectrum of society. The species also has rich cultural importance to first nations in this watershed. These conclusions are supported by appropriate modeling and add to not only Pacific salmon literature but will be of great interest to Atlantic salmon conservationists. The sea-run nature of this species also will inform understanding of challenges in freshwater and marine systems. I found this paper to be extremely convincing and the conclusions are strengthened by a rice use of citations and the addition of allied related manuscripts on the stream temperature model and wild/hatchery marine survival. This work is cutting edge and current. I firmly believe that this paper will influence thinking in the field due to its clarity of message and exceptional graphical presentation of results. I am generally familiar with salmon life cycle models and the overall use was both appropriate and added novel methods. I noted in one part of the paper where I am less familiar with one of the statistical analyses used. I would defer to you and others there. The open nature of the models, data and coding would allow not only the researchers to reproduce the work but add on to a toolkit of models that could be used for other species and habitats.
I have included comments in the margin of the manuscript as well as made some direct in-text suggestions to improve clarity. This was a pleasure to read.

John F. Kocik
Reviewer #3 (Remarks to the Author): In this paper the authors apply a stage-based life history model to 8 populations of Chinook salmon in the Columbia River/Snake River basins and show strong associations between warming (particularly warming SST) during the marine stage of the life history and probability of population extinction/extirpation. I accepted the review with considerable excitement of seeing something truly new, insightful, or transformative. Unfortunately I was underwhelmed, not by the statistical rigour, but rather with the interpretation of the results. The claim is essentially that warming is bad and that smolts need to survive the ocean better for populations to avoid extinction. The authors all but said, and perhaps should have, that this analysis provides strong evidence that these populations are doomed and that restoration/conservation is a fools-errand (that would indeed have been provocative at least).
What I was hoping to see more of was a more holistic linkage between different stages of the life history and a quantitative appreciation that what happens in freshwater may lead individuals down trajectories that result in the ocean life history being the proximate stage of mortality. I was hoping and expecting to see discussion that warming SSTs may be detrimental to southern Chinook populations, but Alaska populations may fare better during warming temperatures (and indeed the authors did not cite the obvious paper to suggest so).
So in the end I am left wanting the authors to make a better case for novelty and insights that can be gleaned by this very complex modelling exercise that goes beyond what is already firmly established.
Although the reference section is extensive, I do suggest the authors incorporate information from populations beyond their focal range to broaden the discussion of SSTs and to also contrast their work to other very similar approaches.   all of which affect productivity throughout the California Current Ecosystem 28 . Although the 112 response of these processes to greenhouse gas forcing will vary, SST will likely continue to be 113 an important indicator, and SST will increase with climate change 29,30 . represented by a stable climate (Fig. 4). During the spawning migration, summer-run populations (i.e., populations that returned 204 to their spawning areas in summer, Secesh River and Valley Creek) were more affected by 205 temperature than spring-run populations, with net declines of up to -17% by the 2060s. However, 206 temperature effects on the juvenile, downstream migration reduced populations by about -18% 207 from the 2020s to the 2060s, on average, while climate change effects in the marine stage 208 reduced survival by -83% to -90% (Fig. 6). 209 It is important to note that the freshwater climate impacts in this study were conservative 210 in some respects. Specifically, we have assumed linear responses to environmental variables, but 211 physiological and ecological thresholds can create non-linear responses, by which future 212 temperatures or flows could have more severe effects. Furthermore, the primary covariate for 213 juvenile survival in two of our models was fall flow, which is the covariate that is least sensitive 214 to climate change 32 . Summer flows could also be limiting (Model 2), which would cause a more 215 negative response. More generally, the high elevation, mostly-wilderness habitat of these 216 populations is unusual for salmon in the region, and partially explains the relatively small effects 217 of climate change on their freshwater life stages. Other populations face more immediate impacts 218 on freshwater productivity 14,33 . 219 Survival through the migration corridor declined for all juvenile migrants and adult 220 summer-run migrants due to rising temperatures. Still, the declines we found were relatively 221 small because of their early run timing compared with other salmon that migrate during peak 222 temperatures. In particular, endangered Snake River sockeye adults experience much higher 223 mortality from heat stress 34,35 . In our analysis, we found relative resilience in freshwater stages and the dominant driver towards extinction was the ~90% decline in survival due to rising SST 225 in the marine life stage. Therefore, closely monitoring ocean survival and directing research into 226 these populations potential response to novel conditions is clearly needed. 227

Caveats 228
There are two main caveats to these projections. First, the northeast Pacific might not 229 warm at the rate modeled, despite rising levels of CO 2 in the atmosphere. Over the past century, 230 internal variability in the climate system represented by variation in sea level pressure and 231 natural variability in ocean circulation has been a stronger determinant of coastal SST than 232 global mean temperature 36 . How long this situation will continue is difficult to predict. Warming 233 might occur slower than modeled, which would reduce the rate of population declines. 234 Nonetheless, with the entire ocean warming at all depths 30 , at some point this signal will 235 inevitably reach coastal waters. 236 The second possibility is that the northeast Pacific does warm, but some sort of 237 ecological surprise 37 will reverse the historical relationship between SST and salmon survival. 238 Ocean temperature does not affect salmon primarily through a physiological response, but rather 239 through a combination of bottom up and top down ecological processes that jointly regulate 240 salmon growth and survival 38-40 , which explains the non-stationarity of statistical correlations 24 . 241 Warm conditions have been associated with poorer-quality prey and more warm-water predators, 242 likely generating the correlation we have observed. However, it is possible that novel 243 communities will arise with different responses to temperature, or that salmon will adapt to an 244 altered food web in a positive manner. We do see consumption rates increase and unexpected Nonetheless, the correlation strength with SST has been increasing rather than decreasing 251 43 , and in fact salmon fared poorly 44 during the recent marine heatwave, with a decadal-low 252 number of adult Chinook returning in many ESUs 45 and the closure of multiple fisheries in 253 2020. Thus, although the various processes that historically generated the PDO 20 may interact 254 differently in the future, it seems likely that SST will continue to be a negative indicator of 255 salmon survival. Further exploration using our model with a changing correlation structure over 256 time could clarify possible trajectories and when they could be detected. 257 Other ecological surprises should also be considered, such as increases in competitors 258 such as jellyfish 40 and Humboldt or market squid 46 , which could reduce salmon survival in an 259 altered ocean. Predators, such as seabirds that currently concentrate on alternative prey, change 260 behavior when their preferred prey dwindle or alter their distribution, which can increase or 261 decrease predation on salmon 47 . We recommend closely monitoring trophic interactions and 262 salmon growth rates to detect such a possibility. 263 Finally, our model is conservative in that we have not accounted for any negative effects 264 of ocean acidification. Declines of sensitive species such as crabs and calcariferous zooplankton 265 could have a negative effect on salmon, especially salmon populations that prey extensively on 266 sensitive species 48 . We have assumed that ocean-stage salmon are relatively insensitive to pH, 267 but if there are effects, they will likely be negative 49,50 .

Discussion 269
Our results indicate that rising SST as one symptom of a changing ocean puts all of these 270 populations at high risk of extinction. Small populations have minimal buffer against declining 271 marine survival rates, and are at immediate risk (Fig. 1). The threat to larger populations causes 272 even greater concern because they are the remaining salmon strongholds, which provide genetic 273 and demographic resilience for the ESU as a whole 51  In the marine realm, human activities affect salmon survival through targeted fishing on 287 salmon, their prey (sardine, anchovy, krill, juvenile rockfish, juvenile crab), predators (e.g., 288 marine mammals) and competitors (e.g., hake, Pacific halibut, arrowtooth flounder). Changes in 289 fisheries can have positive or negative effects on salmon. Curtailed salmon harvest since the 290 1960s reduced one major source of direct mortality. Reduced harvest on top predators, however, 291 led to rebounding marine mammal populations that now consume large amounts of salmon 39,63 . 292 Competition with hatchery fish has complex interactions with climate effects on wild salmon 59 , 293 and is an area of active research. Sea level rise combined with coastal development threatens 294 complete loss of intertidal marshes in California and Oregon, and the majority of estuary habitat 295 in Washington 64 , which will affect some salmon and their prey. 296 Other indirect anthropogenic effects on marine habitats are not well quantified, and 297 warrant more research. Increased awareness of the importance of forage fish for the entire food 298 chain 65 led to a ban on the development of fisheries to exploit forage fish, demonstrating a 299 proactive approach that should support salmon. But in sum, we lack key information on the full 300 mechanistic basis of salmon marine survival, which limits the strength of end-to-end models for 301 guiding management 66 . 302 Efforts to mitigate carryover effects from freshwater that could affect marine survival in 303 these populations have primarily focused on dams. Survival through Columbia and Snake River 304 dams generally now meets recovery targets (>96%) 67 , and cumulative mortality over 500 km of 305 in-river migrating fish (~50%) is similar to that estimated for unregulated rivers of similar length 306 (i.e., Fraser River 68 ). However, slow travel time through reservoirs combined with temperatures 307 that have been elevated by dams 69 can potentially result in lower marine survival 70 . Mitigation 308 efforts to increase smolt body size and advance migration timing could increase marine survival 309 71,72 . Restoration efforts in freshwater habitat, such as restoring floodplains, riparian planting to 310 reduce stream temperature, reconnecting side-channel habitat 73 or adding nutrients to juvenile 311 salmon rearing areas could also enhance/restore freshwater salmon production.
Our models estimated strong density dependence at the parr to smolt stage, although it is 313 not clear whether summer or winter habitat is constrained. The headwaters have minimum 314 anthropogenic impacts, but the mainstem Salmon River has experienced structural simplification 315 and loss of wood, which could limit both rearing and overwintering capacity. Our results suggest 316 that smolt carrying capacities are currently limited by flow rather than temperature. Higher flows 317 may create more habitat, improve connectivity, or decrease contact with predators. The predator 318 community has also been affected by human impacts, from introduced sport fish (smallmouth 319 bass, Micropterus dolomieu and brook trout Salvelinus fontinalis) to creation of reservoir habitat 320 more favorable for invasive fish (e.g., American shad, Alosa sapidissima). 321 Throughout salmon watersheds, improving and expanding access to rearing habitat 322 should increase smolt abundance and body condition resulting in improved salmon viability 74 . 323 Intrinsic habitat potential is negatively correlated with current levels of disturbance, so restoring 324 habitat could yield substantial benefits. Specifically, habitat at lower elevation that was 325 historically highly productive has been preferentially lost. Improving individual fish growth by 326 reducing contaminant loads 75 , increasing floodplain habitat 74   (productivity and capacity parameters for both stages as well as coefficients for temperature and 393 flow) were assumed to be random samples from an underlying normal distribution 89 . To 394 determine the best environmental covariates, we compared the estimated predictive error of 395 alternative models in a leave-one-out cross-validation method for Bayesian models using the LOO package 90 . Because of correlations among the climate variables tested, multiple models 397 had similar support from the data. Our primary objective was to identify divergent potential 398 responses to climate change. Therefore, we selected two models with covariates that show 399 different trajectories with climate change (Fig. S2) Table S4). Fish that stay in the ocean longer have additional mortality (S 0 ). There is still an 434 advantage to spawning as an older fish because of higher fecundity (F 5 ). In the model, the 435 effective number of spawners reflects the age distribution of female spawners, which return as 436 either 4 or 5 y olds, and the 5 y olds have the fecundity advantage. A very small percentage of 437 fish return as 6 y olds; these fish were added to 5 y olds.

Adult upstream survival (S
We fit the tuning parameters (S 0 , b 3 , b 4 , and F 5 ) using a modified Approximate Bayesian 439 Computing approach 86,87 . We applied this method by first generating a prior distribution for 440 each parameter. The priors for parameters that range between 0 and 1 (S 0 , b 3 , b 4 ,) (Table S2). For juvenile mainstem survival, we used COMPASS 448 reconstructions of juvenile migration that incorporated actual river management. Note that 449 historical river management was extremely variable over this time period and not comparable to 450 the conditions we projected in the climate simulations (in particular, transportation rates were 451 higher, spill was lower, and dam passage survival was lower than in the action proposed in the 452 Environmental Impact Statement ACOE 93 ). We ran the SAR model in retrospective mode, 453 which uses the fitted estimates of historical random effects and observation error. We simulated 454 other life stages in the calibration as we would in future projections. 455 The parameter values from top 0.2% of these parameter sets compared with their prior 456 distributions are shown in Fig. S1 Table S6). We smoothed each of these individual time series using a 20-y 497 running mean to reduce interannual variation that was already accounted for by the TMB model. SSTarc in winter for transported fish, and SSTarc in winter + SSTwa in summer for in-river fish) 517 to show the full time series of population response to the climate scenarios ( Fig. 1-2). 518

Sensitivity analyses 519
In our first sensitivity analysis, we compared population outcomes from Model 1 with a 520 different freshwater covariate model (Model 2: summer flow + Model 1 marine covariates). 521 Finally, we exchanged the marine covariates in Model 3: summer temperature + fall flow for 522 freshwater, SSTwa in summer for transported fish, and SSTarc in spring + spring upwelling for in-river fish (Fig 3). Thus, we explored all combinations of the top two models for freshwater 524 and marine stages, respectively. 525 In our second sensitivity analysis, we applied the climate trends for the ensemble mean of 526 RCP 8.5 to one life stage while the other life stages experienced a stationary climate (Fig. 6). We 527 cycled through parr to smolt, downstream migration, smolt to adult return, and upstream 528 migration. In each case, we reported the extent of population decline as the ratio of geometric 529 mean population size in 2080-2089 divided by mean abundance in 2020-2029. We ran 1,000 530 simulations per life stage, and calculated the mean change in abundance for each population.  Submodels for individual life stages  554  Table S1. Biological data sources 555 Table S2. Environmental data sources 556 Table S3. Model comparison of spawner to smolt productivity models. 557 Table S4. SAR models selected 558 Figure S1.            This manuscript describes an effort to use a state-of-the-art life-cycle model to quantify the risks of extinction for 8 populations of Chinook salmon in the Pacific Northwest of the USA. The model relies heavily on an extraordinary dataset of individually tagged fish to estimate various functional relationships that describe the processes that affect survival throughout the life-cycle of this species. The model is used to simulate the potential effects of climate change on the viability of these 8 populations. The primary conclusion reached by these efforts is that climate warming is likely to doom this group of populations and that the primary survival bottleneck is during the fish's first year of life in the sea. While the model development is commendable and the data used are impressive, the conclusions that are drawn are not particularly new and have been appreciated for some time. Thus, it is not really clear what the main contribution from this paper actually is.
We revised the introduction to clarify three specific contributions of this model on LN50-126 as follows: Retrospective analyses also show strong relationships between climate indices and salmon performance (e.g., 8). Looking toward the future, indirect and qualitative assessments point to anthropogenic climate change as an additional overriding threat for salmon in the North Atlantic and California Current (e.g., 9, 10-12). How to mitigate for this threat is therefore a primary concern among conservation organizations and management agencies.
… This is a novel approach to downscaling climate projections in multiple environments.
Thus, there are three reasons why existing approaches for modeling the biological impacts of climate change are inadequate for evaluating potential management actions for salmon. Similar limitations apply to other species that are migratory or have complex life histories. First, proposed management actions are usually focused on conditions in freshwater, so accounting for "carry over" effects from freshwater to marine life stages is essential for their evaluation. Carryover effects occur when an individual's previous history affects its performance in a subsequent life stage (24). For example, the timing of migration from freshwater to the ocean and back again is a key determinant of salmon survival in every life stage, and one of the most sensitive traits in relation to climate (25-28). Timing is also a key element in multiple management actions, especially those involving the hydrosystem (29) and fisheries (30). Quantification of carryover effects that will be affected by climate change is therefore essential for evaluating the net benefits of proposed actions to protect endangered species.
Second, current models of survival in the salmon marine stage rely on climate indices that cannot be linked directly to global climate model (GCM) projections, so it is impossible to conduct formal analyses of how alternative carbon emission scenarios or other anthropogenic actions to mitigate climate change might affect the timing of declines in marine survival. Nor is it possible to quantify uncertainty in modelled projections across GCMs and thus take full advantage of the Coupled Model Intercomparison Project, which represents the major advances of global climate modeling in recent decades (31, 32).
Third, approaches that are currently available for accounting for climate impacts on freshwater and marine life stages use independent downscaling methods for the two environments. Terrestrial downscaling methods usually employ statistical or dynamical downscaling of temperature and precipitation that feed into hydrological models. Statistical downscaling is an efficient way to explore many alternative climate projections and characterize model uncertainty at many steps in the modeling chain (33). A common approach to marine downscaling, on the other hand, is to integrate GCM output into Regional Ocean Models, which in practice are only available for very few GCM projections (32). As a result, these methods often rely on projections from different GCMs and are not consistent in characterizing potential model biases, and thus uncertainty in climate projections. Moreover, they are not temporally linked, which prevents complete accounting for carryover effects from one life stage to the next.
We address each of these difficulties by developing a novel modeling approach with a flexible and explicit mechanism for accounting for the correlation structure among all climate drivers. We also use a multi-model approach to indirectly account for a change in the relationship between climate drivers and ecological responses. Finally, we allowed the timing of the initiation of juvenile migration to vary with environmental conditions, which subsequently affected both smolt migration survival and the probability that fish would be transported in a barge through the hydrosystem (a mitigation action that has fixed start and stop dates). Three factors --timing, hydrosystem operations and transportation --subsequently affected arrival timing at the Columbia River estuary, ocean survival, and upstream migration timing and survival. Although the details of the migration models are unique to this system, the need to account for carryover effects and the correlation structure of climate drivers in multiple environments is shared by many migratory species.
My concerns for the current manuscript are given below.
1) The manuscript attempts to grasp at jargon to increase its general appeal, but these attempts are often a bit off-target. For example, there has been a lot of interest in 'non-stationarity' in ecology and in fisheries science recently and this manuscript attempts to link in to these interests. However, while this term does refer to a changing variable (in the strictest statistical sense), use of this term in ecology typically is referring to changes in a relationship between variables (i.e. the relationship or correlation among variables is non-stationary). Thus, why call climate change 'stationary' or 'non-stationary'? We have known for decades that climate is non-stationary; what is interesting is that the relationships between climate conditions and ecological processes are non-stationary. Thus, use of non-stationary/stationary in this manuscript is somewhat distracting. While this is just a semantic issue, I don't think the paper benefits from the current use of these terms.
We acknowledge that we unfortunately used the term 'stationarity' in both of the meanings mentioned ("strictest statistical sense" and "typical ecology" sense). We replaced the former use of the word with the term "detrended" throughout. We also used more specific language for the latter use.
2) Similarly, the manuscript refers to 'aggressive' warming scenarios. This also seems misplaced. Typically we refer to aggressive scenarios of curtailing carbon emissions (i.e. it's not the climate that is aggressive, it's the policy actions to reduce emissions that are aggressive).
LN 300 We have changed "more aggressive warming scenarios" to "the upper quartile of GCM projections, in which warming occurred at a faster rate" 3) The key results are expressed as the time to quasi-extinction for each of these populations. In general, I do not think this is the best way to present the key results. When a modeled population goes extinct in these simulations is based on arbitrary population thresholds that probably don't apply well when populations are reduced to very low numbers where stochastic processes are more likely to ultimately determine whether they go extinct or not. Thus, the primary results of these simulations would be more useful if the population growth rate was the response variable used to explore the consequences of different climate scenarios (i.e. the posterior distribution of lambda). The result could then be focused on how much of the posterior distribution was <1, thereby leading to population decline, etc. Time to extinction is too arbitrary and too 'loaded' a variable that is easily misinterpreted that it shouldn't be used in these types of analyses.
We respectfully disagree with the reviewer's claim that lambda is an appropriate description of the dynamics in populations that are density dependent, such as these. Lambda changes systematically as density dependence is reduced, which results in changes in lambda being an underestimate and hence misleading representation of deterministic population declines. It also can increase when populations stabilize, but if the new level of abundance is extremely low, then this too is misleading.
So although it is the case that lamda was more negative, and a larger proportion of the posterior distribution was below 1 in the climate change scenarios, we feel this is a trivial point that is much better made with the figure showing changes in abundance.
Extinction is a process that is more closely related to the number of fish in the stream than the rate of a prolonged decline. It is exactly because stochastic processes are so important for small populations (unlike lambda), that we use this quasi-extinction threshold. Although the actual threshold is indeed arbitrary, it is based on evolutionary theory, and in particular the relationship between effective population size and raw abundance in salmon. It was developed specifically in response to the concerns mentioned by the reviewer.
We are not concerned that it is "loaded" because it is one metric that is used in formal management decisions (NMFS 2020). It is related to recovery targets and a large body of work on population viability.
To address the reviewers concerns, we re-ordered the results section to emphasize changes in abundance before introducing the concept of the QET, and modified the presentation of QET as follows: LN 51 "With a warming climate, deterministic declines inevitably lead to extinction unless some ecological, evolutionary, or climatic rescue effect occurs (38). Climate trajectories did level off in the RCP 4.5 scenarios in the second half of the 21 st century, which reduced the rate of population declines in that scenario. However, for the most part populations had already reached very low abundances at that point.
For practical purposes in salmon management, populations that have fewer than 50 spawners on average for 4 years in a row are considered to be at extremely high risk of extinction from chance fluctuations in abundance, depensatory processes, and long-term consequences from loss of genetic variability (39, 40). The evolutionary theory behind this threshold applies to isolated populations, and these populations are not truly isolated. So some small populations may be sustainable within a larger salmon metapopulation. Nonetheless, when the majority of populations within the ESU pass this threshold, the ESU itself is at high risk. This ESU is already threatened with extinction because of historical declines (41), so although this exact threshold is somewhat arbitrary, it is a useful metric for demonstrating the severity of the declines across all of our simulations. We assessed the first year (if any) in which a population in a given simulation fell below a quasi-extinction threshold of adult abundance (QET50 We modified the discussion to note the specific benefit of quantifying climate impacts in this study: LN 399 Our analysis showed relative resilience in freshwater stages, with the dominant driver towards extinction being rising SST, which tracked a ~90% decline in survival in the marine life stage. This occurred despite an advance in smolt migration timing and other changes in hydrosystem management. The modeled carryover effects of changes in timing are likely to be adaptive, but inadequate as compensation for large declines in marine survival.

LN 454
The results of our model 3, in which marine survival was driven by upwelling indicated that improved productivity in a warmer ocean could benefit salmon. Nonetheless, the benefit in that case was relatively small compared with overall negative effects.

And
LN 620 Our modeling approach, which accounts for carryover effects across the life cycle, allows systematic exploration of alternative correlation structures among climate drivers and between climate drivers and ecological responses, and a thorough accounting for uncertainty in climate projections lays a path forward for evaluating benefits of proposed actions to protect our critical resources.
In this paper the authors apply a stage-based life history model to 8 populations of Chinook salmon in the Columbia River/Snake River basins and show strong associations between warming (particularly warming SST) during the marine stage of the life history and probability of population extinction/extirpation. I accepted the review with considerable excitement of seeing something truly new, insightful, or transformative. Unfortunately I was underwhelmed, not by the statistical rigour, but rather with the interpretation of the results. The claim is essentially that warming is bad and that smolts need to survive the ocean better for populations to avoid extinction. The authors all but said, and perhaps should have, that this analysis provides strong evidence that these populations are doomed and that restoration/conservation is a foolserrand (that would indeed have been provocative at least).
What I was hoping to see more of was a more holistic linkage between different stages of the life history and a quantitative appreciation that what happens in freshwater may lead individuals down trajectories that result in the ocean life history being the proximate stage of mortality.
We added more specific details about the changes in timing. LN 363 Our model predicted that smolts would shift their migration timing about 4.5 days earlier arrival at Lower Granite Dam, which does reduce temperature exposure. Nonetheless, temperature effects on the juvenile migration still grew over time, and reduced populations by about -18% on average from the 2020s to the 2060s.
Climate impacts were most dramatic in the marine stage, where survival was reduced by -83 to -90% (Fig. 6). This occurred despite the fact that smolts arrived at Bonneville Dam, initiating the marine stage, about 6.5 days earlier, which generally improves marine survival (26).
Adult Chinook were predicted to shift their migration ~4 days earlier in response to warmer mainstem conditions with lower flows (43). But again, this was not enough to prevent mortality from increased heat exposure. During the adult migration, populations that returned to their spawning areas in summer (Secesh River and Valley Creek) were more affected by temperature than spring-run populations, with net declines of up to -17% by the 2060s. Still, the declines we found in the mainstem were relatively small because of the early adult run timing of Snake River spring/summer Chinook compared with other run types or species that migrate during peak temperatures.
And in the discussion: LN 404 The modeled carryover effects of changes in timing are likely to be adaptive, but inadequate as compensation for large declines in marine survival, regardless of entry timing I was hoping and expecting to see discussion that warming SSTs may be detrimental to southern Chinook populations, but Alaska populations may fare better during warming temperatures (and indeed the authors did not cite the obvious paper to suggest so).
Text added: So in the end I am left wanting the authors to make a better case for novelty and insights that can be gleaned by this very complex modelling exercise that goes beyond what is already firmly established.
Text added: LN 422 Quantitative comparisons of combined climatic and anthropogenic influences and more exploration of changing relationships among drivers are needed to unravel the multiple pressures these populations face AND LN 514 Additional changes to the juvenile transportation schedule or faster transit through the mainstem can still be explored, but additional improvements to survival through other mechanisms are needed.
AND LN 620 Our modeling approach, which accounts for carryover effects across the life cycle, allows systematic exploration of alternative correlation structures among climate drivers and between climate drivers and ecological responses, and a thorough accounting for uncertainty in climate projections lays a path forward for evaluating benefits of proposed actions to protect our critical resources.
Although the reference section is extensive, I do suggest the authors incorporate information from populations beyond their focal range to broaden the discussion of SSTs and to also contrast their work to other very similar approaches.