Predictability of Species Distributions Deteriorates Under Novel Environmental Conditions in the California Current System

Abstract

Spatial distributions of marine fauna are determined by complex interactions between environmental conditions and animal behaviors. As climate change leads to warmer, more acidic, and less oxygenated oceans, species are shifting away from their historical distribution ranges, and these trends are expected to continue into the future. Correlative Species Distribution Models (SDMs) can be used to project future habitat extent for marine species, with many different statistical methods available. However, it is vital to assess how different statistical methods behave under novel environmental conditions before using these models for management advice, and to consider whether future projections based on these techniques are biologically reasonable. In this study, we built SDMs for adults and larvae of two ecologically important pelagic fishes in the California Current System (CCS): Pacific sardine (Sardinops sagax) and northern anchovy (Engraulis mordax). We used five different SDM methods, ranging from simple [thermal niche model (TNM)] to complex (artificial neural networks). Our results show that some SDMs trained on data collected between 2003 and 2013 lost substantial predictive skill when applied to observations from more recent years, when ocean temperatures associated with a marine heatwave were outside the range of historical measurements. This decrease in skill was particularly apparent for adult sardine, which showed non-stationary relationships between catch locations and sea surface temperature (SST) through time. While sardine adults and larvae shifted their distributions markedly during the marine heatwave, anchovy largely maintained their historical spatiotemporal distributions. Our results suggest that correlative relationships between species and their environment can become unreliable during anomalous conditions. Understanding the underlying physiology of marine species is therefore

Exploring timescales of predictability in species distributions

“…becoming increasingly degraded given the accelerating pace of global change and non-stationarity in ecosystem responses (Zimmermann et al 2009, Zurell et al 2009, Scales et al 2017a, Muhling et al 2020. Indeed, much of the climate velocity literature argues that local environmental conditions drive species movements and population range shifts (Pinsky et al 2013, Sunday et al 2015, Brito-Morales et al 2018).…”

“…due to decadal climate oscillations and secular climate change) (Checkley Jr and Barth 2009). These changes in the regional climate and oceanography can have pronounced ecological impacts; for example, a severe marine heatwave from 2014 to 2016, with positive sea surface temperature anomalies up to 6°C (Bond et al 2015, Leising et al 2015, Jacox et al 2016), led to a broad range of ecosystem impacts including species redistributing across the CCS (Cavole et al 2016, Becker et al 2018, Muhling et al 2020).…”

“…Second, models were trained on all data from 1998 to 2013, and tested against data from 2014 to 2016 when a severe marine heatwave occurred in the CCS. This out-of-sample cross-validation approach tests how well models perform under novel environmental conditions (Muhling et al 2020). The four measures of predictive performance described above were then calculated.…”

“…Correlative species distribution models (SDMs) have become an important tool to predict and plan for changes in species abundance and distribution under climate change (Elith and Leathwick 2009, Robinson et al 2017, Araújo et al 2019, Brodie et al 2020). While correlative models provide important insights into species' realized niches under observed conditions, there is a discrepancy among studies as to whether species–environment correlations may break down (Muhling et al 2020), or hold up (Becker et al 2019), under novel environmental conditions (Sequeira et al 2018, Yates et al 2018). As climate change is driving non‐stationarity in ecosystems and reducing the utility of historical climate information (Zimmermann et al 2009, Zurell et al 2009, Franklin 2010), there is a need to understand which scales of environmental variability contribute to predictive skill (i.e.…”

Zooplankton variability in the Strait of Georgia, Canada, and relationships with the marine survivals of Chinook and Coho salmon

“…Therefore, if conditions change, the relationships derived from this study may lose their predictive skill (e.g. [ 62 , 63 ]). We also did not consider time lags among the relationships between physical and zooplankton variables, as our dependent data (annual zooplankton biomasses) were already limited (23 years).…”