Choice of baseline climate data impacts projected species' responses to climate change

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species‐climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040–2069, 2070–2099), using downscaled climate projections, and calculated species turnover and changes in species‐specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species‐specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site‐level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses.     

Can the past predict the future? Experimental tests of historically based population models

A frequently advocated approach for forecasting the population‐level impacts of climate change is to project models based on historical, observational relationships between climate and demographic rates. Despite the potential pitfalls of this approach, few historically based population models have been experimentally validated. We conducted a precipitation manipulation experiment to test population models fit to observational data collected from the 1930s to the 1970s for six prairie forb species. We used the historical population models to predict experimental responses to the precipitation manipulations, and compared these predictions to ones generated by a statistical model fit directly to the experimental data. For three species, a sensitivity analysis of the effects of precipitation and grass cover on forb population growth showed consistent results for the historical population models and the contemporary statistical models. Furthermore, the historical population models predicted population growth rates in the experimental plots as well or better than the statistical models, ignoring variation explained by spatial random effects and local density‐dependence. However, for the remaining three species, the sensitivity analyses showed that the historical and statistical models predicted opposite effects of precipitation on population growth, and the historical models were very poor predictors of experimental responses. For these species, historical observations were not well replicated in space, and for two of them the historical precipitation‐demography correlations were weak. Our results highlight the strengths and weaknesses of observational and experimental approaches, and increase our confidence in extrapolating historical relationships to predict population responses to climate change, at least when the historical correlations are strong and based on well‐replicated observations.     

近144年来秦岭太白山林线区3-6月平均气温的重建

秦岭太白山林线植被因海拔较高且受人为扰动较轻,对气候变化的响应尤为敏感,为获取过去气候变化信息提供了可靠代用资源。然而,结合树木年代学方法及Arcgis空间插值功能进行秦岭林线气候变化重建的工作至今仍处于空白。利用采自太白山林线地带太白红杉(Larix chinensis)所建立的树轮宽度资料,与提取自太白山保护区气温栅格数据中的采样点位置气象数据进行相关分析。结果表明,太白红杉与3—6月平均气温相关性最显著,采用线性回归建立了两者的拟合模型,剔除重建方程中的1997、1998年之后,方差解释量达57.2%(调整自由度后为55.5%);重建气温序列显示偏冷时段平均跨度(16年)较偏暖时段平均跨度(10.8年)长,偏冷时段有:1870—1881年、1903—1918年和1977—1996年;偏暖的时段有:1882—1892年、1919—1929年和1997—2013年;在1931—1978年这一时期,气温相对稳定,1988年之后升温强烈;周期分析显示近144年以来3—6月气温存在22—31 a,18—22 a以及10—13 a的3个振荡周期,可能与大尺度气候驱动及太阳活动存在联系。以上结果均得到历史记录以及周边重建结果的支持。     

区域气候变化统计降尺度研究进展

统计降尺度方法(the Statistical Downscaling Methods, SDM)是为合理预测区域尺度的气候变化情景而提出的新型研究方法。统计降尺度法利用多年大气环流的观测资料建立大尺度气候要素和区域气候要素之间的统计关系,并用独立的观测资料检验这种关系的合理性。把这种关系应用于大气环流模式(Global atmospheric general circulation models, GCMs)中输出大尺度气候信息,来预估区域未来的气候变化情景(如气温和降水)。同时,10a来降尺度方法在生态过程模拟以及气候变化与生态预报关系拟合研究方面也取得一定进展。对统计降尺度方法概念的内涵和外延、基本原理和操作步骤的创新研究方面进行了综述,归纳了该方法在模拟区域气候变化中的应用进展、研究热点及发展趋势,介绍了降尺度在生态预报中的相关应用,为相关研究提供参考。     

Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change

To assess a species'' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species'' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km² study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species'' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes.     

不同气象数据空间内插对区域玉米生长模拟结果的影响

作物模型区域模拟已成为作物模型应用的一个新方向。运用作物模型进行区域研究时,遇到的问题之一就是输入模型的空间数据质量问题,研究不同空间内插法获得的气象数据对作物模型区域模拟结果的影响,可以为区域模拟对输入数据的敏感性研究提供一定的参考。利用区域校准的CERES-Maize模型,将3类内插方法(几何内插、统计内插、动力模型内插)产生的网格化天气数据分别输入到CERES-Maize模型中,模拟了50km×50km网格水平下1961—1990年我国玉米生产状况,并选取1980—1990年模拟的平均产量与同期农调队调查产量进行比较,以了解区域模拟中,不同空间内插方法所得的逐日气象数据对区域模拟结果的影响。结果表明:(1)作物模型区域应用时,所采用的3种内插方法都能满足作物模型区域模拟对网格化天气数据的要求,采用3种天气数据的区域模拟结果都能反映出玉米平均产量的空间变化特征,与网格调查平均产量之间具有极显著的相关关系,但采用不同内插天气数据对模拟结果造成了8%以内的偏差。(2)采用不同内插天气数据,在进行作物区域模拟时,各方法的模拟结果之间呈极显著的相关关系,但这些模拟结果之间,在全国大部分地区是差异显著。     

Effects of weather and climate on the dynamics of animal population time series

Weather is one of the most basic factors impacting animal populations, but the typical strength of such impacts on population dynamics is unknown. We incorporate weather and climate index data into analysis of 492 time series of mammals, birds and insects from the global population dynamics database. A conundrum is that a multitude of weather data may a priori be considered potentially important and hence present a risk of statistical over-fitting. We find that model selection or averaging alone could spuriously indicate that weather provides strong improvements to short-term population prediction accuracy. However, a block randomization test reveals that most improvements result from over-fitting. Weather and climate variables do, in general, improve predictions, but improvements were barely detectable despite the large number of datasets considered. Climate indices such as North Atlantic Oscillation are not better predictors of population change than local weather variables. Insect time series are typically less predictable than bird or mammal time series, although all taxonomic classes display low predictability. Our results are in line with the view that population dynamics is often too complex to allow resolving mechanisms from time series, but we argue that time series analysis can still be useful for estimating net environmental effects.     

Downscaled projections of Caribbean coral bleaching that can inform conservation planning

Projections of climate change impacts on coral reefs produced at the coarse resolution (~1°) of Global Climate Models (GCMs) have informed debate but have not helped target local management actions. Here, projections of the onset of annual coral bleaching conditions in the Caribbean under Representative Concentration Pathway (RCP) 8.5 are produced using an ensemble of 33 Coupled Model Intercomparison Project phase‐5 models and via dynamical and statistical downscaling. A high‐resolution (~11 km) regional ocean model (MOM4.1) is used for the dynamical downscaling. For statistical downscaling, sea surface temperature (SST) means and annual cycles in all the GCMs are replaced with observed data from the ~4‐km NOAA Pathfinder SST dataset. Spatial patterns in all three projections are broadly similar; the average year for the onset of annual severe bleaching is 2040–2043 for all projections. However, downscaled projections show many locations where the onset of annual severe bleaching (ASB) varies 10 or more years within a single GCM grid cell. Managers in locations where this applies (e.g., Florida, Turks and Caicos, Puerto Rico, and the Dominican Republic, among others) can identify locations that represent relative albeit temporary refugia. Both downscaled projections are different for the Bahamas compared to the GCM projections. The dynamically downscaled projections suggest an earlier onset of ASB linked to projected changes in regional currents, a feature not resolved in GCMs. This result demonstrates the value of dynamical downscaling for this application and means statistically downscaled projections have to be interpreted with caution. However, aside from west of Andros Island, the projections for the two types of downscaling are mostly aligned; projected onset of ASB is within ±10 years for 72% of the reef locations.     

Climate change,biological invasions,and the shifting distribution of Mediterranean fishes: A large‐scale survey based on local ecological knowledge

Climate change and biological invasions are rapidly reshuffling species distribution, restructuring the biological communities of many ecosystems worldwide. Tracking these transformations in the marine environment is crucial, but our understanding of climate change effects and invasive species dynamics is often hampered by the practical challenge of surveying large geographical areas. Here, we focus on the Mediterranean Sea, a hot spot for climate change and biological invasions to investigate recent spatiotemporal changes in fish abundances and distribution. To this end, we accessed the local ecological knowledge (LEK) of small‐scale and recreational fishers, reconstructing the dynamics of fish perceived as “new” or increasing in different fishing areas. Over 500 fishers across 95 locations and nine different countries were interviewed, and semiquantitative information on yearly changes in species abundance was collected. Overall, 75 species were mentioned by the respondents, mostly warm‐adapted species of both native and exotic origin. Respondents belonging to the same biogeographic sectors described coherent spatial and temporal patterns, and gradients along latitudinal and longitudinal axes were revealed. This information provides a more complete understanding of the shifting distribution of Mediterranean fishes and it also demonstrates that adequately structured LEK methodology might be applied successfully beyond the local scale, across national borders and jurisdictions. Acknowledging this potential through macroregional coordination could pave the way for future large‐scale aggregations of individual observations, increasing our potential for integrated monitoring and conservation planning at the regional or even global level. This might help local communities to better understand, manage, and adapt to the ongoing biotic transformations driven by climate change and biological invaders.     

Community structure and composition in response to climate change in a temperate steppe

Climate change would have profound influences on community structure and composition, and subsequently has impacts on ecosystem functioning and feedback to climate change. A field experiment with increased temperature and precipitation was conducted to examine effects of experimental warming, increased precipitation and their interactions on community structure and composition in a temperate steppe in northern China since April 2005. Increased precipitation significantly stimulated species richness and coverage of plant community. In contrast, experimental warming markedly reduced species richness of grasses and community coverage. Species richness was positively dependent upon soil moisture (SM) across all treatments and years. Redundancy analysis (RDA) illustrated that SM dominated the response of community composition to climate change at the individual level, suggesting indirect effects of climate change on plant community composition via altering water availability. In addition, species interaction also mediated the responses of functional group coverage to increased precipitation and temperature. Our observations revealed that both abiotic (soil water availability) and biotic (interspecific interactions) factors play important roles in regulating plant community structure and composition in response to climate change in the semiarid steppe. Therefore these factors should be incorporated in model predicting terrestrial vegetation dynamics under climate change.     

A Modeling Study on Responses of Alkaline Grassland Ecosystems to Climate Change in Light of Diversity and Spatial Patterns

Temporal variations of spatial coverage patterns of major plant species in the alkaline grasslands in northeast China subject to climate change were studied using a spatial simulation model. The model stressed the coupling between soil alkali and vegetation coverage. The modeled species coverage patterns were shown in close agreement with observations on a fenced one-hectare alkaline grassland from 1989 to 1993. The impacts of climate change on the species coverage were studied by subtracting the output patterns of the model under contemporary climate from those under altered climate. To relate the difference patterns to various landscape indices, the one-hectare region was divided into 25 subregions. The differencein species coverage between the present and altered climate and spatial pattern and diversity indices were computed for each subregion. A statistical analysis showed that for plants with wide ranges of tolerance to soil alkali and strong spatial migration capability, the impact of climate change was significantly related to spatial patterns, but not to diversity. However, for plants with narrow ranges of tolerance to soil alkali and less capability to migrate spatially, the impact of climate change was related to both diversity and spatial patterns.     

森林生物量遥感降尺度研究

森林生物量是评价全球碳氧平衡、气候变化的重要指标。目前已有基于星载激光雷达数据的全球森林生物量产品,但空间分辨率较低,不能很好地满足小区域森林调查和动态监测的需要。针对这一现状,以美国马里兰州两个森林分布状况不同的区域为研究区,基于CMS(Carbon Monitoring System)30 m分辨率和GEOCARBON 1 km分辨率森林地上生物量产品以及TM等数据源,通过升尺度模拟低分辨率生物量数据和直接使用低分辨率产品两种方式,分别尝试建立了多光谱地表参数和低分辨率森林地上生物量之间的统计关系,以此作为降尺度模型实现了森林地上生物量空间分辨率从1 km到30 m的转换,并对降尺度结果进行精度评价和误差分析。结果表明:模拟数据降尺度后的30 m分辨率森林地上生物量空间分布和CMS森林地上生物量分布状况大致相同,RMSE=59.2—65.5 Mg/hm~2,相关系数约为0.7;其降尺度结果优于GEOCARBON产品直接降尺度结果RMSE=75.3—79.9 Mg/hm~2;相较于线性模型,非线性模型能更好地呈现森林地上生物量和地表参数间的关系;总体上,降尺度生物量呈现高值区低估,低值区高估的现象。     

Improving the interpretability of climate landscape metrics: An ecological risk analysis of Japan's Marine Protected Areas

Conservation efforts strive to protect significant swaths of terrestrial, freshwater and marine ecosystems from a range of threats. As climate change becomes an increasing concern, these efforts must take into account how resilient‐protected spaces will be in the face of future drivers of change such as warming temperatures. Climate landscape metrics, which signal the spatial magnitude and direction of climate change, support a convenient initial assessment of potential threats to and opportunities within ecosystems to inform conservation and policy efforts where biological data are not available. However, inference of risk from purely physical climatic changes is difficult unless set in a meaningful ecological context. Here, we aim to establish this context using historical climatic variability, as a proxy for local adaptation by resident biota, to identify areas where current local climate conditions will remain extant and future regional climate analogues will emerge. This information is then related to the processes governing species’ climate‐driven range edge dynamics, differentiating changes in local climate conditions as promoters of species range contractions from those in neighbouring locations facilitating range expansions. We applied this approach to assess the future climatic stability and connectivity of Japanese waters and its network of marine protected areas (MPAs). We find 88% of Japanese waters transitioning to climates outside their historical variability bounds by 2035, resulting in large reductions in the amount of available climatic space potentially promoting widespread range contractions and expansions. Areas of high connectivity, where shifting climates converge, are present along sections of the coast facilitated by the strong latitudinal gradient of the Japanese archipelago and its ocean current system. While these areas overlap significantly with areas currently under significant anthropogenic pressures, they also include much of the MPA network that may provide stepping‐stone protection for species that must shift their distribution because of climate change.     

Scale Interactions and Regional Climate: Examples from the Susquehanna River Basin

One of the most difficult problems faced by climatologists is how to translate global climate model (GCM) output into regional- and local-scale information that health and environmental effects researchers can use. It will be decades before GCMs will be able to resolve scales small enough for most effects research, so climatologists have developed climate downscaling methods to bridge the gap between the global and local scales. There are two main streams of climate downscaling research. First, high-resolution, limited-area climate models can be embedded in the coarse-scale GCMs, producing much finer resolution climate data. Second, empirical downscaling techniques develop transfer functions linking the large-scale atmospheric circulation generated by the GCMs to surface data. Examples of both types of downscaling, aimed at improving projections of future climate in the Susquehanna River Basin (the Mid-Atlantic Region of the United States), are presented. A third case is also described in which an even higher-resolution nested atmospheric model is being developed and linked to a hydrologic model system, with the ultimate goal of simulating the environmental response to climate forcing at all time and space scales.     

Geographic patterns and dynamics of Alaskan climate interpolated from a sparse station record

Data from a sparse network of climate stations in Alaska were interpolated to provide 1‐km resolution maps of mean monthly temperature and precipitation–‐variables that are required at high spatial resolution for input into regional models of ecological processes and resource management. The interpolation model is based on thin‐plate smoothing splines, which uses the spatial data along with a digital elevation model to incorporate local topography. The model provides maps that are consistent with regional climatology and with patterns recognized by experienced weather forecasters. The broad patterns of Alaskan climate are well represented and include latitudinal and altitudinal trends in temperature and precipitation and gradients in continentality. Variations within these broad patterns reflect both the weakening and reduction in frequency of low‐pressure centres in their eastward movement across southern Alaska during the summer, and the shift of the storm tracks into central and northern Alaska in late summer. Not surprisingly, apparent artifacts of the interpolated climate occur primarily in regions with few or no stations. The interpolation model did not accurately represent low‐level winter temperature inversions that occur within large valleys and basins. Along with well‐recognized climate patterns, the model captures local topographic effects that would not be depicted using standard interpolation techniques. This suggests that similar procedures could be used to generate high‐ resolution maps for other high‐latitude regions with a sparse density of data.     

高时空分辨率植被覆盖获取方法及其在土壤侵蚀监测中的应用

土壤侵蚀是全球性生态问题,准确监测区域土壤侵蚀状况是评估区域生态质量和生态保护成效的基础。准确获取高时空分辨率植被覆盖信息并与降水动态匹配是土壤侵蚀准确监测的关键。然而,受卫星传感器限制,大区域高时间分辨率与高空间分辨率遥感数据无法同时获取,高空间分辨率植被动态遥感监测面临巨大挑战。为解决这一问题,本研究提出了一套多源遥感数据融合的高时空分辨率绿色植被覆盖度(半月尺度,空间分辨率2 m)获取方法,并与半月尺度的降水因子匹配应用于CSLE开展了天津市蓟州区的土壤侵蚀监测。研究结果表明:1)降雨和植被覆盖度因子在一年之内变异较大,半月降雨量的平均值为43.32 mm,变异系数可达150%,绿色植被半月植被覆盖度的平均值为54.74%,变异系数为18%。考虑土地覆盖类型的高时空分辨率绿色植被覆盖度融合方法,可以获取合理的高空间分辨率绿色植被覆盖度动态,为高空间分辨率土壤侵蚀监测提供了一个有效手段;2)土壤侵蚀发生范围与强度与降水及植被因子在年内的动态匹配高度相关,土壤侵蚀发生范围最大为10月上半月,发生面积为137.55 km~2,土壤侵蚀发生强度最为严重为7月下半月,25 t/hm~2以上土壤侵蚀发生面积为12.70 km~2;3)高时空分辨率植被与降水因子耦合下的土壤侵蚀监测结果与地面一致性较好(判定系数可达0.88),明显好于仅用一期高空间分辨率植被因子的土壤侵蚀监测结果(判定系数仅为0.097),采用高时空分辨率植被与降水因子耦合的土壤侵蚀监测方法可以大幅度提高土壤侵蚀监测的准确性,本研究为其他区域准确开展土壤侵蚀监测提供了一套有效的方法。     

Spatial scale affects novel and disappeared climate change projections in Alaska

The formation of novel and disappeared climates between the last glacial maximum (LGM) and the present is important to consider to understand the expansion and contraction of species niches and distributions, as well as the formation and loss of communities and ecological interactions over time. Our choice in climate data resolution has the potential to complicate predictions of the ecological impacts of climate change, since climate varies from local to global scales and this spatial variation is reflected in climate data. To address this issue, we downscaled LGM and modern (1975–2005) 30‐year averaged climate data to 60‐m resolution for the entire state of Alaska for 10 different climate variables, and then upsampled each variable to coarser resolutions (60 m to 12 km). We modeled the distributions of novel and disappeared climates to evaluate the locations and fractional area of novel and disappeared climates for each of our climate variables and resolutions. Generally, novel and disappeared climates were located in southern Alaska, although there were cases where some disappeared climates existed within coastal and interior Alaska. Climate resolution affected the fractional area of novel and disappeared climates in three patterns: As the spatial resolution of climate became coarser, the fractional area of novel and disappeared climates (a) increased, (b) decreased, or (c) had no explainable relationship. Overall, we found the use of coarser climate data increased the fractional area of novel and disappeared climates due to decreased environmental variability and removal of climate extremes. Our results reinforce the importance of downscaling coarse climate data and suggest that studies analyzing the effects of climate change on ecosystems may overestimate or underestimate their conclusions when utilizing coarse climate data.     

PaleoView: a tool for generating continuous climate projections spanning the last 21 000 years at regional and global scales

It has been difficult to access projections of global‐scale climate change with high temporal resolution spaning the late Pleistocene and Holocene. This has limited our ability to discern how climate fluctuations have affected species’ range dynamics and extinction processes, turn‐over in ecological communities and changes in genetic diversity. PaleoView is a new freeware tool, which provides a comprehensive but easy‐to‐use way to generate and view paleoclimate data at temporal and spatial resolutions suitable for detecting biotic responses to major climate shifts since the last glacial maximum. Regional to global scale simulations of temperature, precipitation, humidity and mean sea level pressure can be generated from PaleoView as gridded or time series data at time intervals as short as a decade for any period during the last 21 000 yr. They can be viewed using a built‐in geographical user interface or saved as data files. Modelled climate reconstructions are based on daily simulation output from the Community Climate System Model ver. 3 (CCSM3). This global coupled atmosphere–ocean–sea ice–land general circulation model accurately reproduces major climatic features associated with the most recent deglaciation event, and predicts present‐day patterns of climate conditions with verified hindcast skill. By providing a portal for readily accessing climate reconstructions at high temporal resolutions, PaleoView can help to better establish the consequences of past climate fluctuations on macro‐ecological patterns of biological and genetic diversity.     

The effects of climate data precision on fitting and projecting species niche models

Biosecurity agencies are particularly concerned to know the potential distribution of invasive alien species under present, and to a lesser extent, future climates; expensive decisions can hinge upon the degree of perceived threat a pest species poses. Climate‐based niche modelling techniques are available to inform these decisions. These tools now regularly employ gridded climate datasets of moderate spatial resolution (0.5 degree), though biosecurity decision‐makers continually seek greater spatial precision in the risk map products. Various splining techniques are capable of generating gridded climate datasets approaching the precision limits imposed by the availability of digital elevation model data. As the spatial precision of climate datasets increases, more detailed effects of topographic relief become apparent in the climatic data. When these datasets are used to develop and apply species niche models, the climate data is spatially intersected with species location data to infer relationships between the climate and the species’ geographic distribution. Here we investigate the effect of changing climate precision on projections of species’ niche models developed with CLIMEX, including the effect of upscaling and downscaling the outputs. We found that there were noticeable increases in sensitivity in models developed using more precise climate datasets. The largest differences in projections were noted where species range limits coincided with regions of strong climatic gradients such as where there was marked topographic relief in relation to the spatial precision of the climatic dataset. Upscaling (fitting a model with a fine resolution dataset and then projecting the results with a coarser grid), tended to produce smaller potential ranges for a species, albeit at the cost of model sensitivity. Downscaling had the opposite effect, identifying additional, mostly marginally climatically suitable habitat. It remains unclear how sensitive the fine resolution results are to the number and spatial arrangement of input location records used to build the model. The results indicate some benefits of improving the spatial resolution of climate datasets, though not at the expense of climatic data accuracy. Decision‐makers should be mindful of the inherent uncertainties in these models, and modellers have a responsibility to identify and convey these uncertainties to their intended audience.     

Ticket to spawn: Combining economic and genetic data to evaluate the effect of climate and demographic structure on spawning distribution in Atlantic cod

Climate warming and harvesting affect the dynamics of species across the globe through a multitude of mechanisms, including distribution changes. In fish, migrations to and distribution on spawning grounds are likely influenced by both climate warming and harvesting. The Northeast Arctic (NEA) cod (Gadus morhua) performs seasonal migrations from its feeding grounds in the Barents Sea to spawning grounds along the Norwegian coast. The distribution of cod between the spawning grounds has historically changed at decadal scales, mainly due to variable use of the northern and southern margins of the spawning area. Based on historical landing records, two major hypotheses have been put forward to explain these changes: climate and harvesting. Climate could affect the distribution through, for example, spatial habitat shifts. Harvesting could affect the distribution through impacting the demographic structure. If demographic structure is important, theory predicts increasing spawner size with migration distance. Here, we evaluate these hypotheses with modern data from a period (2000–2016) of increasing temperature and recovering stock structure. We first analyze economic data from the Norwegian fisheries to investigate geographical differences in size of spawning fish among spawning grounds, as well as interannual differences in mean latitude of spawning in relation to changes in temperature and demographic parameters. Second, we analyze genetically determined fish sampled at the spawning grounds to unambiguously separate between migratory NEA cod and potentially smaller sized coastal cod of local origin. Our results indicate smaller spawners farther away from the feeding grounds, hence not supporting the hypothesis that harvesting is a main driver for the contemporary spawning ground distribution. We find a positive correlation between annual mean spawning latitude and temperature. In conclusion, based on contemporary data, there is more support for climate compared to harvesting in shaping spawning ground distribution in this major fish stock in the North Atlantic Ocean.