Title : Ecological and methodological drivers of species ’ distribution and phenology responses to climate change Running head : Species ’ responses to climate change

Climate change is shifting species’ distribution and phenology. Ecological traits, such as mobility or reproductive mode, explain variation in observed rates of shift for some taxa. However, estimates of relationships between traits and climate responses could be influenced by how responses are measured. We compiled a global dataset of 651 published marine species’ responses to climate change, from 47 papers on distribution shifts and 32 papers on phenology change. We assessed the relative importance of two classes of predictors of the rate of change, ecological traits of the responding taxa and methodological approaches for quantifying biological responses. Methodological differences explained 22% of the variation in range shifts, more than the 7.8% of the variation explained by ecological traits. For phenology change, methodological approaches accounted for 4% of the variation in measurements, whereas 8% of the variation was explained by ecological traits. Our ability to predict responses from traits was hindered by poor representation of species from the tropics, where temperature isotherms are moving most rapidly. Thus, the mean rate of distribution change may be underestimated by this and other global syntheses. Our analyses indicate that methodological approaches should be explicitly considered when designing, analysing and comparing results among studies. To improve climate impact studies, we recommend that: (1) re-analyses of existing time-series state how the existing datasets may limit the inferences about possible climate responses; (2) qualitative comparisons of species’ responses across different studies be limited to studies with similar methodological approaches; (3) meta-analyses of climate responses include methodological attributes as covariates and; (4) that new time series be designed to include detection of early warnings of change or ecologically relevant change. Greater consideration of methodological attributes will improve the accuracy of analyses that seek to quantify the role of climate change in species’ distribution and phenology changes. A cc ep te d A rt ic le This article is protected by copyright. All rights reserved. Introduction A large number of marine (Poloczanska et al., 2013) and terrestrial (Parmesan & Yohe, 2003) species have shifted their distributions and phenology in recent decades, indicating that climate change is driving a global biological response. For example, recent climate change has driven the invasion of pest species (Ling et al., 2008), contributed to declines in commercially important fish species (Beaugrand & Kirby, 2010) and appears to be increasing mismatch in the seasonal timing between predators and their prey (Edwards & Richardson, 2004, Barbraud & Weimerskirch, 2006). Despite an overall broad consistency in species’ responses to climate change, there is considerable variability in magnitudes and patterns of responses (Parmesan, 2007, Poloczanska et al., 2013, Sunday et al., 2015). Variability poses a challenge to ecological science and management of species impacted by climate change, because it hinders predictions of future responses. Analyses across many species have examined how combinations of taxonomic identity, ecological traits and local environmental variables may explain variability in responses (Perry et al., 2005, Wolkovich et al., 2012, Pinsky et al., 2013, Poloczanska et al., 2013, Sunday et al., 2015). All of these approaches base their inferences on standardised measures of distribution and phenology, yet observed responses to climate change may also depend on how distribution and phenology are measured (Wolkovich et al., 2012). Measurements of distribution and phenology are influenced by a suite of decisions that are made in two stages of all studies: their sampling design and data analysis (Brown et al., 2011, Bates et al., 2014a). In the sampling design phase researchers decide how species’ variables are measured. For example, distribution can be measured as mean latitude of a populations’ geographic extent (e.g. Perry et al., 2005) or by measuring the most extreme latitudes where a species is found (e.g. Robinson et al., 2015). Similarly, the phenology of breeding events can be measured by censusing a population throughout a season to determine the peak breeding date, or as the first and last individuals to breed (e.g. Fitter & Fitter, 2002, Barbraud & Weimerskirch, 2006). Measures of distribution and phenology based on the most extreme individuals rather than variables that represent the distribution of individuals within a population may lead to very different estimates of climate change response A cc ep te d A rt ic le This article is protected by copyright. All rights reserved. rates. For example, single individuals may by chance have extreme responses (Brown et al., 2011) and measurements using single individuals are susceptible to detection biases (Cook et al., 2012, Bates et al., 2015). In the analysis phase, most marine climate change studies include only a single predictor – temperature – to explain changes in distribution or phenology, and thus do not explicitly consider other drivers of change (Brown et al., 2011). It is unknown whether studies that do not account for other potential anthropogenic drivers, such as fishing, eutrophication and habitat loss, could lead to higher estimates of impacts of climate change. For example, an investigation of changes in the distribution of North Sea cod showed fishing pressure explains part of the observed biological changes (Engelhard et al., 2014). A greater understanding of how different methodological approaches affect detection of observed responses to climate change will benefit climate change ecology in four main ways. First, studies that analyse existing data sets to test for climate impacts, need to account for historical choices made about field data collection that could limit the ability to detect species’ responses to climate change. For instance, uneven sampling effort along coasts means museum records of species occurrences may misrepresent historical range boundaries (Shoo et al., 2006, Przeslawski et al., 2012). Second, many regional studies compare rates of change with other studies in their discussions of how ecological traits influence a species’ response (e.g. Richardson, 2008). Comparisons of change may also need to consider differences in methodological approaches across studies, such as how occurrence data are used to estimate ranges. Third, new time-series are being initiated with the aim of measuring future distribution and phenology change (e.g. Robinson et al., 2015). Greater understanding of how different methodological approaches can affect measured responses to climate change could assist the design of new time-series. For instance, inconsistent sampling effort through time may bias measured rates of change (Bates et al., 2015). Finally, meta-analyses of existing studies will produce more accurate estimates if they standardise for study differences, or constrain comparisons to be among studies with similar methods (Parmesan, 2007, Przeslawski et al., 2012). For instance, differences in A cc ep te d A rt ic le This article is protected by copyright. All rights reserved. rates of range shifts among European butterflies, birds and plants could be a consequence of taxonomic identity, geographic biases or differences in the metrics used (Parmesan and Yohe 2003). Here we examine potential causes of variability in observed marine species distribution and phenology responses to climate change using a meta-analysis. First, we ask whether measurements of distribution and phenology change from the peer-reviewed literature are representative of different taxa, oceans and methodological approaches. Then we conduct a meta-analysis on rates of response, to ask how ecological traits, study design and measurement approaches influence rates of distribution and phenology change. This enables us to ask how study measurement approaches may bias measured rates of change and affect inferences about the biological drivers of change. Finally, we investigate how different measurement approaches are used for different taxa and discuss the implications of measurement biases on the global understanding on climate change responses. Materials and methods