Can patterns of spatial autocorrelation reveal population processes? An analysis with the fire salamander

Spatial autocorrelation (SAC) is often observed in species distribution data, and can be caused by exogenous, autocorrelated factors determining species distribution, or by endogenous population processes determining clustering such as dispersal. However, it remains debated whether SAC patterns can actually reveal endogenous processes. We reviewed studies measuring dispersal of the salamander Salamandra salamandra, to formulate a priori hypotheses on the scale at which dispersal is expected to determine population distribution. We then tested the hypotheses by analysing SAC in distribution data, and evaluating whether controlling for the effect of environmental variables can reveal endogenous processes. We surveyed 565 streams to obtain species distribution data; we also recorded landscape and microhabitat features known to affect the species. We used multiple approaches to tease apart endogenous and exogenous SAC: the analysis of residuals of logistic regression models considering different environmental variables; the analysis of eigenvectors extracted by several implementations of spatial eigenvector mapping. In capture–mark–recapture studies, 98% of individuals moved 500 m or less. Both species distribution and environmental features were strongly autocorrelated. The residuals of logistic regression relating species to environmental variables were autocorrelated at distances up to 500 m; analyses considering different sets of environmental variables, or assuming non‐linear species habitat relationships, yielded identical results. The results of spatial eigenvector mapping strongly depended on the matrix of distances used. Nevertheless, the eigenvectors of models with best fit were autocorrelated at distances up to 200–500 m. The concordance between multiple approaches suggests that 500 m is the scale at which dispersal connects breeding localities, increasing probability of occurrence. If exogenous variables are correctly identified, the analysis of SAC can provide important insights on endogenous population processes, such as the flow of individuals. SAC analysis can also provide important information for conservation, as the existence of metapopulations or population networks is essential for long term persistence of amphibians.     

Spatial synchrony in red grouse population dynamics

Red grouse Lagopus lagopus scoticus populations exhibit unstable dynamics that are often characterised by regular periodic fluctuations in abundance. Time-series' of grouse harvesting records collected from 287 management units (moors) across Scotland, England and Wales were analysed to investigate the broad scale patterns of synchrony in these fluctuations. Estimation of the spatial autocorrelation of grouse population dynamics across moors indicates relatively high levels of synchrony between populations on adjacent moors, but that this synchrony declines sharply with increasing inter-moor distance. At distances of greater than 100  km, grouse population time-series exhibit only weakly positive cross-correlation coefficients. Twenty-eight geographical, environmental and other candidate variables were examined to construct a general linear model to explain variation in local synchrony. Grouse moor productivity (average size of shooting bag), distance from the Atlantic coast moving in a north-easterly direction, April and June temperatures, and June rainfall significantly increased the explanatory power of this model. An understanding of the processes underlying synchrony in red grouse population dynamics is a prerequisite to anticipating the effects of large-scale environmental change on regional patterns of grouse distribution and abundance.     

Spatial regression techniques for inter‐population data: studying the relationships between morphological and environmental variation

Understanding the importance of environmental dimensions behind the morphological variation among populations has long been a central goal of evolutionary biology. The main objective of this study was to review the spatial regression techniques employed to test the association between morphological and environmental variables. In addition, we show empirically how spatial regression techniques can be used to test the association of cranial form variation among worldwide human populations with a set of ecological variables, taking into account the spatial autocorrelation in data. We suggest that spatial autocorrelation must be studied to explore the spatial structure underlying morphological variation and incorporated in regression models to provide more accurate statistical estimates of the relationships between morphological and ecological variables. Finally, we discuss the statistical properties of these techniques and the underlying reasons for using the spatial approach in population studies.     

Multiple regression on distance matrices: a multivariate spatial analysis tool

I explore the use of multiple regression on distance matrices (MRM), an extension of partial Mantel analysis, in spatial analysis of ecological data. MRM involves a multiple regression of a response matrix on any number of explanatory matrices, where each matrix contains distances or similarities (in terms of ecological, spatial, or other attributes) between all pair-wise combinations of n objects (sample units); tests of statistical significance are performed by permutation. The method is flexible in terms of the types of data that may be analyzed (counts, presence–absence, continuous, categorical) and the shapes of response curves. MRM offers several advantages over traditional partial Mantel analysis: (1) separating environmental distances into distinct distance matrices allows inferences to be made at the level of individual variables; (2) nonparametric or nonlinear multiple regression methods may be employed; and (3) spatial autocorrelation may be quantified and tested at different spatial scales using a series of lag matrices, each representing a geographic distance class. The MRM lag matrices model may be parameterized to yield very similar inferences regarding spatial autocorrelation as the Mantel correlogram. Unlike the correlogram, however, the lag matrices model may also include environmental distance matrices, so that spatial patterns in species abundance distances (community similarity) may be quantified while controlling for the environmental similarity between sites. Examples of spatial analyses with MRM are presented.     

Red-shifts and red herrings in geographical ecology

I draw attention to the need for ecologists to take spatial structure into account more seriously in hypothesis testing. If spatial autocorrelation is ignored, as it usually is, then analyses of ecological patterns in terms of environmental factors can produce very misleading results. This is demonstrated using synthetic but realistic spatial patterns with known spatial properties which are subjected to classical correlation and multiple regression analyses. Correlation between an autocorrelated response variable and each of a set of explanatory variables is strongly biased in favour of those explanatory variables that are highly autocorrelated - the expected magnitude of the correlation coefficient increases with autocorrelation even if the spatial patterns are completely independent. Similarly, multiple regression analysis finds highly autocorrelated explanatory variables "significant" much more frequently than it should. The chances of mistakenly identifying a "significant" slope across an autocorrelated pattern is very high if classical regression is used. Consequently, under these circumstances strongly autocorrelated environmental factors reported in the literature as associated with ecological patterns may not actually be significant. It is likely that these factors wrongly described as important constitute a red-shifted subset of the set of potential explanations, and that more spatially discontinuous factors (those with bluer spectra) are actually relatively more important than their present status suggests. There is much that ecologists can do to improve on this situation. I discuss various approaches to the problem of spatial autocorrelation from the literature and present a randomisation test for the association of two spatial patterns which has advantages over currently available methods.     

Phenological patterns of neotropical lotic chironomids: Is emergence constrained by environmental factors?

Abstract In this paper we analyzed the emergence phenology of a highly diverse chironomid assemblage to test for association between emergence and some environmental variables and for the presence of synchrony in emergence. We used a time series of 48 weekly samples from a tropical low order forested stream (south‐eastern Brazil) to describe how this assemblage varied in an intra‐annual scale. An eigenvector‐based filtering approach was adapted to create temporal variables that could be used in our multiple regression analyses, trying to overcome the problems of temporal autocorrelation. Emergence of the Chironomidae, two subfamilies, concordant species, and of dominant species was not related to rainfall, temperature, moon phase or photoperiod. Taxonomic composition and species richness did not change across time. The number of emerging individuals of the subfamily Orthocladiinae was significantly related to temperature and to temporal filters. The inclusion of the temporal filters into the analyses almost eliminated autocorrelation in the regression residuals. We detected interspecific synchrony in a group of species, but an absence of trends and periodicity in chironomid emergence, which was not related to environmental variables. This suggests that unknown factors, differing from those known to control emergence in temperate regions, operate in the tropics. The erratic behaviour of the analyzed series raises the question of whether chaotic dynamics may generate this variability.     

New insights into testing for density dependence

The reasons why tests for density dependence often differ in their results for a particular time-series were investigated using modelled time-series of 20 generations in lenght. The test of Pollard et al. (1987) is the most reliable; it had the greatest power with the three forms of density dependent data investigated (mean detection rates of 50.8–61.1%) and was least influenced by the form of the density dependence in time-series. Bulmer's first test (Bulmer 1975) had slightly lower power (mean detection rates of 27.4–56.8%) and was more affected by the form of density dependence present in the data. The mean power of the other tests was lower and detection rates were more variable. Rates were 24.6–46.2% for regression of k-value on abundance, 6.4–32.6% for regression of k-value on logarithmic abundance and 0.2–13.7% for Bulmer's second test (Bulmer 1975). Bulmer's second test is not useful because of low power. For one method, regression of k-value on abundance. density dependence was detected in 19.9% of timeseries generated using a random-walk model. For regression of k-value on logarithmically-transformed abundance the equivalent figure was 18.3% of series. These rates of spurious detection were significantly (P<0.001) greater than the generally accepted 5% level of type 1 errors and so these methods are not suitable for the analysis of time-series data for density dependence. Levels of spurious detection (from random-walk data) were around the 5% level and hence were acceptable for Bulmer's first test, Bulmer's second test, and the tests of Pollard et al. (1987), Reddinguis and den Boer (1989) and Crowley (1992). For all tests, except Bulmer's second test, the rate of detection and the amount of autocorrelation in time-series were negatively correlated. The degree of autocorrelation accounted for as much as 59.5–77.9% of the deviance in logit proportion detection for regression of k-value on abundance, Bulmer's first test, and the tests of Pollard et al. and Reddingius and den Boer. For regression of k-value on abundance this relationship accounted for less of the deviance (29.4%). Independent effects of density dependence were largely absent. It is concluded that these are tests of autocorrelation, not density dependence (or limitation). Autocorrelation was found to become positive (which is similar to values from random-walk data) as the intrinsic growth rate became either small or large. As the strength of density dependence (in the discrete exponential logistic equation) is dependent on the product of the intrinsic growth rate and the density dependent parameter it is unclear whether this is because of variation in the strength of density dependent mortality or reproduction per se. However, small values of the intrinsic grwoth rate cause the amount of variation in the data to become small, which might hinder detection of density dependence, and large values of the intrinsic growth rate are coincident with determinstic chaos which hinders detection. The user of these tests for density dependence should be aware of their potential weakness when variation within time-series is small (which itself is difficult to judge) or if the intrinsic growth rate is large so that chaotic dynamics might result. Power and levels of variability in rates of detection using Reddingius and den Boer's test were intermediate between those of the test of Pollard et al. and Bulmer's first test. This, combined with the strong relationship between rates of detection of limitation and the value of the autocorrelation coefficient, make testing for limitation similar to testing for density dependence. Crowley's test of attraction gave the widest range of mean detection rates from density dependent data of all the tests (20.4–60.6%). The relative rates of detection for the three forms of density dependent data were opposite to those found for Bulmer's first test and the test of Pollard et al. I conclude that testing for attraction is a complementary concept to testing for density dependence. As dynamics represented in time-series generated using a stochastic form of the exponential logistic equation became chaotic, Bulmer's first test, the test of Pollard et al. and regression of k on abundance failed to detect density dependence reliably. Conversely, Crowley's test was capable of detecting attraction with a power between 96 and 100% with time-series containing both stochastically and deterministically chaotic dynamics. This difference from other tests is in agreement with the lower influence of autocorrelation.     

Combining spatial and phylogenetic eigenvector filtering in trait analysis

Aim To analyse the effects of simultaneously using spatial and phylogenetic information in removing spatial autocorrelation of residuals within a multiple regression framework of trait analysis. Location Switzerland, Europe. Methods We used an eigenvector filtering approach to analyse the relationship between spatial distribution of a trait (flowering phenology) and environmental covariates in a multiple regression framework. Eigenvector filters were calculated from ordinations of distance matrices. Distance matrices were either based on pure spatial information, pure phylogenetic information or spatially structured phylogenetic information. In the multiple regression, those filters were selected which best reduced Moran's I coefficient of residual autocorrelation. These were added as covariates to a regression model of environmental variables explaining trait distribution. Results The simultaneous provision of spatial and phylogenetic information was effectively able to remove residual autocorrelation in the analysis. Adding phylogenetic information was superior to adding purely spatial information. Applying filters showed altered results, i.e. different environmental predictors were seen to be significant. Nevertheless, mean annual temperature and calcareous substrate remained the most important predictors to explain the onset of flowering in Switzerland; namely, the warmer the temperature and the more calcareous the substrate, the earlier the onset of flowering. A sequential approach, i.e. first removing the phylogenetic signal from traits and then applying a spatial analysis, did not provide more information or yield less autocorrelation than simple or purely spatial models. Main conclusions The combination of spatial and spatio‐phylogenetic information is recommended in the analysis of trait distribution data in a multiple regression framework. This approach is an efficient means for reducing residual autocorrelation and for testing the robustness of results, including the indication of incomplete parameterizations, and can facilitate ecological interpretation.     

基于GWR模型的于田绿洲土壤表层盐分空间分异及其影响因子

以于田绿洲为研究靶区,利用24个采样点的土壤表层盐分数据,选取9个与土壤表层盐分密切相关的影响因子,结合空间自相关、传统回归分析和地理加权回归模型,分析表土盐分的空间分布特征及其影响因子的空间分异.结果表明:于田绿洲表土盐分在空间上并非随机分布,而是存在较强的空间依赖关系,空间自相关指数为0.479.地下水矿化度、地下水埋深、高程和温度是影响干旱区平原绿洲表土积盐的主要因子,这些因子具有空间异质性,选取的9个环境变量中除土壤pH值外,其他变量对表土盐分的影响强度均存在显著的空间分异.GWR模型对存在空间非平稳性数据的解释能力和估计精度都优于OLS模型,而且在模型估计参数的可视化上具有明显优势.     

Can niche‐based distribution models outperform spatial interpolation?

Aim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms. Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada. Methods We built 13 different models on 190 bird species using regression trees. Our habitat‐based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species. Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R² in predicting artificial ranges. Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.     

Environmental fluctuations promote intraspecific diversity and population persistence via inflationary effects

The impact of temporal variation in the environment, specifically the amount of temporal autocorrelation, on population processes is of growing interest in ecology and evolutionary biology. It was recently discovered that temporal autocorrelation in the environment can significantly increase the abundance of populations that would otherwise have low, or even negative long‐term growth rates (via so‐called ‘inflationary effects’), provided that immigration from another source prevents extinction. Here we use a mathematical model to ask whether inflationary effects can also increase population persistence without immigration if different phenotypes within that population partition growth over time and buffer each other from extinction via mutation. Using a combination of analytical and numerical methods, we find that environmental autocorrelation can inflate the abundance of phenotypes that would otherwise be excluded from the population, provided that phenotypes are sufficiently different in their use of the environment. This inflation of abundance at the phenotypic level also generates an inflation of abundance at the population level. Remarkably, intraspecific inflationary effects can increase both phenotypic and whole population abundance even if one or all phenotypes are maladapted to the environment, as long as mutations prevent phenotypic extinction during periods of poor environmental conditions. Given the prevalence of temporally autocorrelated environmental variables in nature, intraspecific inflationary effects have the potential to be of widespread importance for population persistence as well as the maintenance of intraspecific diversity.     

Extinctions in competitive communities forced by coloured environmental variation

Understanding the relationships between environmental fluctuations, population dynamics and species interactions in natural communities is of vital theoretical and practical importance. This knowledge is essential in assessing extinction risks in communities that are, for example, pressed by changing environmental conditions and increasing exploitation. We developed a model of density dependent population renewal, in a Lotka–Volterra competitive community context, to explore the significance of interspecific interactions, demographic stochasticity, population growth rate and species abundance on extinction risk in populations under various autocorrelation (colour) regimes of environmental forcing. These factors were evaluated in two cases, where either a single species or the whole community was affected by the external forcing. Species' susceptibility to environmental noise with different autocorrelation structure depended markedly on population dynamics, species' position in the abundance hierarchy and how similarly community members responded to external forcing. We also found interactions between demographic stochasticity and environmental noise leading to a reversal in extinction probabilities from under- to overcompensatory dynamics. We compare our results with studies of single species populations and contrast possible mechanisms leading to extinctions. Our findings indicate that abundance rank, the form of population dynamics, and the colour of environmental variation interact in affecting species extinction risk. These interactions are further modified by interspecific interactions within competitive communities as the interactions filter and modulate the environmental noise.     

Environmental and other factors influencing fertility in Sri Lanka

The study utilizes the complete tabulations from the 1971 census and data from the Registrar General's and Meteorological departments for the same year. It applies a multiple regression analysis of the total fertility rate for each district for 1971 on a series of environmental variables. These include the proportion of the rural population by district to the all-island rural population; the district mean annual rainfall and the proportion of the district population employed in the major rural occupations to the total employed. To these 3 environmental variables were added 2 socioeconomic variables: the proportion of the population aged 10+ years and who are literate, and infant mortality. Results indicate that the 5 independent variables are clearly intercorrelated. The multiple regression analysis shows that the 5 variables together account for 76.4% of the total variation in district total fertility rate. It is argued that this and other studies undertaken previously provide useful pointers to the type of variable to be considered in any policy aimed at population control and indicate where major efforts should be directed.     

Environmental forcing as a main determinant of bloom dynamics of the Chrysochromulina algae

In this paper, we demonstrate that the seasonal dynamics in the abiotic factors, without including seasonal changes in the biological relationships, can appropriately account for the seasonal dynamics of Chrysochromulina spp. This is through the analysis of data on the population dynamics of Chrysochromulina spp. off southern Norway that is evaluated in relation to environmental factors and season by the analyses of 12 year monthly time-series. Chrysochromulina spp. abundance, nutrient concentrations, hydrographical properties, as well as current and wind data were analysed on a monthly scale by means of autoregressive moving average models, principal component analyses (PCA), and linear and nonlinear regression models. Seasonal development of the Chrysochromulina assemblage was well predicted from regression models forced with two PCA components representing seasonal variation in nutrient and chlorophyll a levels and ratios, inflow of North Seawater to the Skagerrak and northeasterly wind along the Norwegian coast. Assuming these to be general results, we might hypothesis that marine algal communities are governed by seasonally varying abiotic factors to a large extent.     

Effect of habitat quality and phenotypic variation on abundance- and trait-based early warning signals of population collapses

Loss of resilience in population numbers in response to environmental perturbations may be predicted with statistical metrics called early warning signals (EWS) that are derived from abundance time series. These signals, however, have been shown to have limited success, leading to the development of trait-based EWS that are based on information collected from phenotypic traits such as body size. Experimental work assessing the efficacy of EWS under varying ecological and environmental factors are rare. In addition, disentangling how such warning signals are affected under varying ecological and environmental factors is key to their application in biological conservation. Here, we experimentally test how different rates of environmental forcing (i.e. warming) and varying ecological factors (i.e. habitat quality and phenotypic diversity) affected population stability and predictive power of early warning signals of population collapse. We analyzed population density and body size time series data from three phenotypically different populations of a protozoan ciliate Askenasia volvox in two levels of habitat quality subjected to three different treatments of warming (i.e. no warming, fast warming and slow warming). We then evaluated how well abundance- and trait-based EWS predicted population collapses under different levels of phenotypic diversity, habitat quality and warming treatments. Our results suggest that habitat quality and warming treatments had more profound effects than phenotypic diversity had on both population stability and on the performance of abundance-based signals of population collapse. In addition, trait-based EWS generally performed well, were reliable and more robust in forecasting population collapse than abundance-based EWS, regardless of variation in environmental and ecological factors. Our study points towards the development of a predictive framework that includes information from phenotypic traits such as body size as an indicator of loss of resilience of ecological systems in response to environmental perturbations.     

Extrinsic environmental factors,not resident diversity itself,lead to invasion of <Emphasis Type="Italic">Ageratum conyzoides</Emphasis> L. in diverse communities

The relationship between diversity and invasibility might be confounded by extrinsic environmental factors and the evolutionary structure of the resident community. To examine the role of extrinsic environmental factors, species and phylogenetic diversity in regulating community susceptibility to invasion, we established 109 plots either with or without Ageratum conyzoides L. in Liandu, China. We identified all the species in our samples, weighed the aboveground biomass of each species, and measured environmental variables. For all species recorded in our survey, we constructed a community phylogeny using PhytoPhylo mega-phylogeny as a backbone. We selected the best-fit environment model based on the minimum corrected Akaike information criteria score to examine the effect of extrinsic environmental variables on the relative abundance of A. conyzoides. Relationship between biodiversity and invasion of A. conyzoides was examined by a multiple regression, in which extrinsic ecological factors and biodiversity were combined to predict the relative abundance of A. conyzoides. To reduce the number of extrinsic variables, the first six components produced by a principal component analysis of environmental variables were used as predictive variables in the multiple regression. The best-fit environment model indicated that the relative abundance of A. conyzoides was higher in summer and in communities with lower total organic matter and higher total nitrogen in the soil. The multiple regression indicated that only the positive relationship between the Shannon–Wiener diversity of exotics and the relative abundance of A. conyzoides was significant. This result challenges the importance of diversity–resistance to plant invasion. Generalist facilitation might exist between A. conyzoides and other exotic species, although mechanisms for such facilitation are unclear. Overall, our finding suggests the extrinsic factors covarying with diversity are more important than diversity itself in regulating community susceptibility to invasion.     

Accounting for spatial pattern when modeling organism-environment interactions

Statistical models of environment-abundance relationships may be influenced by spatial autocorrelation in abundance, environmental variables, or both. Failure to account for spatial autocorrelation can lead to incorrect conclusions regarding both the absolute and relative importance of environmental variables as determinants of abundance. We consider several classes of statistical models that are appropriate for modeling environment-abundance relationships in the presence of spatial autocorrelation, and apply these to three case studies: 1) abundance of voles in relation to habitat characteristics; 2) a plant competition experiment; and 3) abundance of Orbatid mites along environmental gradients. We find that when spatial pattern is accounted for in the modeling process, conclusions about environmental control over abundance can change dramatically. We conclude with five lessons: 1) spatial models are easy to calculate with several of the most common statistical packages; 2) results from spatially-structured models may point to conclusions radically different from those suggested by a spatially independent model; 3) not all spatial autocorrelation in abundances results from spatial population dynamics; it may also result from abundance associations with environmental variables not included in the model; 4) the different spatial models do have different mechanistic interpretations in terms of ecological processes – thus ecological model selection should take primacy over statistical model selection; 5) the conclusions of the different spatial models are typically fairly similar – making any correction is more important than quibbling about which correction to make.     

闽江河口湿地土壤盐分的空间分异与集聚特征

探究河口湿地土壤盐分的空间异质性,揭示分异格局下的空间集聚特征,对河口湿地的可持续发展具有重要意义。本文以福州市闽江河口湿地的Landsat 8遥感影像、数字高程模型和地面实测土壤盐分为数据源,利用相关性分析与主成分分析法选取显著性环境因子,去除变量间的共线性,分别采用支持向量机回归克里格法(SVROK)和回归克里格法(RK)分析了土壤盐分空间异质性,在基础上运用空间自相关法定量描述了土壤盐分空间集聚特征。结果表明: 通过主成分分析提取出3个主成分,可解释数据总方差的85%,反映植被覆盖、土壤属性和地形状况等综合变化信息,并保留原始变量的大部分信息;土壤盐分及其插值残差的空间变异受结构性因素和随机性因素的影响,采用主成分为自变量所建立的SVROK模型能更为精准地体现土壤盐分 “北高南低”的空间异质特征;土壤盐分的Moran I大于0.5,具有显著的空间正相关,空间集聚程度较高,呈现出“高值集聚、低值广布、低值包围高值”的空间集聚特征。     

Multiple stressor effects on coral reef ecosystems

Global climate change has profound implications on species distributions and ecosystem functioning. In the coastal zone, ecological responses may be driven by various biogeochemical and physical environmental factors. Synergistic interactions can occur when the combined effects of stressors exceed their individual effects. The Red Sea, characterized by strong gradients in temperature, salinity, and nutrients along the latitudinal axis provides a unique opportunity to study ecological responses over a range of these environmental variables. Using multiple linear regression models integrating in situ, satellite and oceanographic data, we investigated the response of coral reef taxa to local stressors and recent climate variability. Taxa and functional groups responded to a combination of climate (temperature, salinity, air‐sea heat fluxes, irradiance, wind speed), fishing pressure and biogeochemical (chlorophyll a and nutrients ‐ phosphate, nitrate, nitrite) factors. The regression model for each species showed interactive effects of climate, fishing pressure and nutrient variables. The nature of the effects (antagonistic or synergistic) was dependent on the species and stressor pair. Variables consistently associated with the highest number of synergistic interactions included heat flux terms, temperature, and wind speed followed by fishing pressure. Hard corals and coralline algae abundance were sensitive to changing environmental conditions where synergistic interactions decreased their percentage cover. These synergistic interactions suggest that the negative effects of fishing pressure and eutrophication may exacerbate the impact of climate change on corals. A high number of interactions were also recorded for algae, however for this group, synergistic interactions increased algal abundance. This study is unique in applying regression analysis to multiple environmental variables simultaneously to understand stressor interactions in the field. The observed responses have important implications for understanding climate change impacts on marine ecosystems and whether managing local stressors, such as nutrient enrichment and fishing activities, may help mitigate global drivers of change.