ForestClim-Bioclimatic variables for microclimate temperatures of European forests.

Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high-resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open-access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution.

Decreasing snow cover alters functional composition and diversity of Arctic tundra.

The Arctic is one of the least human-impacted parts of the world, but, in turn, tundra biome is facing the most rapid climate change on Earth. These perturbations may cause major reshuffling of Arctic species compositions and functional trait profiles and diversity, thereby affecting ecosystem processes of the whole tundra region. Earlier research has detected important drivers of the change in plant functional traits under warming climate, but studies on one key factor, snow cover, are almost totally lacking. Here we integrate plot-scale vegetation data with detailed climate and snow information using machine learning methods to model the responsiveness of tundra communities to different scenarios of warming and snow cover duration. Our results show that decreasing snow cover, together with warming temperatures, can substantially modify biotic communities and their trait compositions, with future plant communities projected to be occupied by taller plants with larger leaves and faster resource acquisition strategies. As another finding, we show that, while the local functional diversity may increase, simultaneous biotic homogenization across tundra communities is likely to occur. The manifestation of climate warming on tundra vegetation is highly dependent on the evolution of snow conditions. Given this, realistic assessments of future ecosystem functioning require acknowledging the role of snow in tundra vegetation models.

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Forest microclimates and climate change: Importance, drivers and future research agenda.

Forest microclimates contrast strongly with the climate outside forests. To fully understand and better predict how forests' biodiversity and functions relate to climate and climate change, microclimates need to be integrated into ecological research. Despite the potentially broad impact of microclimates on the response of forest ecosystems to global change, our understanding of how microclimates within and below tree canopies modulate biotic responses to global change at the species, community and ecosystem level is still limited. Here, we review how spatial and temporal variation in forest microclimates result from an interplay of forest features, local water balance, topography and landscape composition. We first stress and exemplify the importance of considering forest microclimates to understand variation in biodiversity and ecosystem functions across forest landscapes. Next, we explain how macroclimate warming (of the free atmosphere) can affect microclimates, and vice versa, via interactions with land-use changes across different biomes. Finally, we perform a priority ranking of future research avenues at the interface of microclimate ecology and global change biology, with a specific focus on three key themes: (1) disentangling the abiotic and biotic drivers and feedbacks of forest microclimates; (2) global and regional mapping and predictions of forest microclimates; and (3) the impacts of microclimate on forest biodiversity and ecosystem functioning in the face of climate change. The availability of microclimatic data will significantly increase in the coming decades, characterizing climate variability at unprecedented spatial and temporal scales relevant to biological processes in forests. This will revolutionize our understanding of the dynamics, drivers and implications of forest microclimates on biodiversity and ecological functions, and the impacts of global changes. In order to support the sustainable use of forests and to secure their biodiversity and ecosystem services for future generations, microclimates cannot be ignored.

ForestTemp - Sub-canopy microclimate temperatures of European forests.

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately.

Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties.

The regional variability in tundra and boreal carbon dioxide (CO2 ) fluxes can be high, complicating efforts to quantify sink-source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990-2015 from 148 terrestrial high-latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO2 fluxes and test the accuracy and uncertainty of different statistical models. CO2 fluxes were upscaled at relatively high spatial resolution (1 km2 ) across the high-latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE-focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO2 sink strength was larger in the boreal biome (observed and predicted average annual NEE -46 and -29 g C m-2  yr-1 , respectively) compared to tundra (average annual NEE +10 and -2 g C m-2  yr-1 ). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO2 budget, estimated using the annual NEE ensemble prediction, suggests the high-latitude region was on average an annual CO2 sink during 1990-2015, although uncertainty remains high.

Productivity, biodiversity, and pathogens influence the global hunter-gatherer population density.

The environmental drivers of species distributions and abundances are at the core of ecological research. However, the effects of these drivers on human abundance are not well-known. Here, we report how net primary productivity, biodiversity, and pathogen stress affect human population density using global ethnographic hunter-gatherer data. Our results show that productivity has significant effects on population density globally. The most important direct drivers, however, depend on environmental conditions: biodiversity influences population density exclusively in low-productivity regions, whereas pathogen stress does so in high-productivity regions. Our results also indicate that subtropical and temperate forest biomes provide the highest carrying capacity for hunter-gatherer populations. These findings document that environmental factors play a key role in shaping global population density patterns of preagricultural humans.

SoilTemp: A global database of near-surface temperature.

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.

The effect of summer drought on the predictability of local extinctions in a butterfly metapopulation.

The ecological impacts of extreme climatic events on population dynamics and community composition are profound and predominantly negative. Using extensive data of an ecological model system, we tested whether predictions from ecological models remain robust when environmental conditions are outside the bounds of observation. We observed a 10-fold demographic decline of the Glanville fritillary butterfly (Melitaea cinxia) metapopulation on the Åland islands, Finland in the summer of 2018 and used climatic and satellite data to demonstrate that this year was an anomaly with low climatic water balance values and low vegetation productivity indices across Åland. Population growth rates were strongly associated with spatiotemporal variation in climatic water balance. Covariates shown previously to affect the extinction probability of local populations in this metapopulation were less informative when populations were exposed to severe drought during the summer months. Our results highlight the unpredictable responses of natural populations to extreme climatic events.

El Efecto de la Sequía Estival sobre la Previsibilidad de las Extinciones Locales en una Metapoblación de Mariposas Resumen Los impactos ecológicos de los eventos climáticos extremos sobre las dinámicas metapoblacionales y la composición de la comunidad son profundos y predominantemente negativos. Con los extensos datos de un sistema de modelos ecológicos probamos si las predicciones de los modelos ecológicos todavía son sólidos cuando las condiciones ambientales se encuentran fuera de los límites de observación. Observamos una declinación demográfica ocurrir diez veces en la metapoblación de la mariposa Melitaea cinxia en las Islas Aland de Finlandia durante el verano de 2018. Usamos datos climáticos y satelitales para demostrar que ese año fue una anomalía al contar con valores bajos de balance hídrico e índices bajos de productividad de la vegetación en todas las islas. Las tasas de crecimiento poblacional estuvieron fuertemente asociadas con la variación espaciotemporal del balance hídrico climático. Las covarianzas que previamente han afectado a la probabilidad de extinción de las poblaciones locales de esta metapoblación fueron menos informativas cuando las poblaciones estuvieron expuestas a sequías severas durante los meses de verano. Nuestros resultados resaltan las respuestas impredecibles de las poblaciones naturales ante los eventos climáticos extremos.

Are drivers of microbial diatom distributions context dependent in human-impacted and pristine environments?

Species occurrences are influenced by numerous factors whose effects may be context dependent. Thus, the magnitude of the effects and their relative importance to species distributions may vary among ecosystems due to anthropogenic stressors. To investigate context dependency in factors governing microbial bioindicators, we developed species distribution models (SDMs) for epilithic stream diatom species in human-impacted and pristine sites separately. We performed SDMs using boosted regression trees for 110 stream diatom species, which were common to both data sets, in 164 human-impacted and 164 pristine sites in Finland (covering ~1,000 km, 60° to 68° N). For each species and site group, two sets of models were conducted: climate model, comprising three climatic variables, and full model, comprising the climatic and six local environmental variables. No significant difference in model performance was found between the site groups. However, climatic variables had greater importance compared with local environmental variables in pristine sites, whereas local environmental variables had greater importance in human-impacted sites as hypothesized. Water balance and conductivity were the key variables in human-impacted sites. The relative importance of climatic and local environmental variables varied among individual species, but also between the site groups. We found a clear context dependency among the variables influencing stream diatom distributions as the most important factors varied both among species and between the site groups. In human-impacted streams, species distributions were mainly governed by water chemistry, whereas in pristine streams by climate. We suggest that climatic models may be suitable in pristine ecosystems, whereas the full models comprising both climatic and local environmental variables should be used in human-impacted ecosystems.

Snow is an important control of plant community functional composition in oroarctic tundra.

The functional composition of plant communities is a critical modulator of climate change impacts on ecosystems, but it is not a simple function of regional climate. In the Arctic tundra, where climate change is proceeding the most rapidly, communities have not shifted their trait composition as predicted by spatial temperature-trait relationships. Important causal pathways are thus missing from models of trait composition change. Here, we study causes of plant community functional variation in an oroarctic tundra landscape in Kilpisjärvi, Finland. We consider the community-weighted means of plant vegetative height, as well as two traits related to the leaf economic spectrum. Specifically, we model their responses to locally measured summer air temperature, snow conditions, and soil resource levels. For each of the traits, we also quantify the importance of intraspecific trait variation (ITV) for between-community functional differences and trait-environment matching. Our study shows that in a tundra landscape (1) snow is the most influential abiotic variable affecting functional composition, (2) vegetation height is under weak local environmental control, whereas leaf economics is under strong local environmental control, (3) the relative magnitude of ITV differs between traits, and (4) ITV is not very consequential for community-level trait-environment relationships. Our analyses highlight the importance of winter conditions for community functional composition in seasonal areas. We show that winter climate change can either amplify or counter the effects summer warming, depending on the trait.

Human population dynamics in Europe over the Last Glacial Maximum.

The severe cooling and the expansion of the ice sheets during the Last Glacial Maximum (LGM), 27,000-19,000 y ago (27-19 ky ago) had a major impact on plant and animal populations, including humans. Changes in human population size and range have affected our genetic evolution, and recent modeling efforts have reaffirmed the importance of population dynamics in cultural and linguistic evolution, as well. However, in the absence of historical records, estimating past population levels has remained difficult. Here we show that it is possible to model spatially explicit human population dynamics from the pre-LGM at 30 ky ago through the LGM to the Late Glacial in Europe by using climate envelope modeling tools and modern ethnographic datasets to construct a population calibration model. The simulated range and size of the human population correspond significantly with spatiotemporal patterns in the archaeological data, suggesting that climate was a major driver of population dynamics 30-13 ky ago. The simulated population size declined from about 330,000 people at 30 ky ago to a minimum of 130,000 people at 23 ky ago. The Late Glacial population growth was fastest during Greenland interstadial 1, and by 13 ky ago, there were almost 410,000 people in Europe. Even during the coldest part of the LGM, the climatically suitable area for human habitation remained unfragmented and covered 36% of Europe.

Recent range shifts of moths, butterflies, and birds are driven by the breadth of their climatic niche.

Species are altering their ranges as a response to climate change, but the magnitude and direction of observed range shifts vary considerably among species. The ability to persist in current areas and colonize new areas plays a crucial role in determining which species will thrive and which decline as climate change progresses. Several studies have sought to identify characteristics, such as morphological and life-history traits, that could explain differences in the capability of species to shift their ranges together with a changing climate. These characteristics have explained variation in range shifts only sporadically, thus offering an uncertain tool for discerning responses among species. As long-term selection to past climates have shaped species' tolerances, metrics describing species' contemporary climatic niches may provide an alternative means for understanding responses to on-going climate change. Species that occur in a broader range of climatic conditions may hold greater tolerance to climatic variability and could therefore more readily maintain their historical ranges, while species with more narrow tolerances may only persist if they are able to shift in space to track their climatic niche. Here, we provide a first-filter test of the effect of climatic niche dimensions on shifts in the leading range edges in three relatively well-dispersing species groups. Based on the realized changes in the northern range edges of 383 moth, butterfly, and bird species across a boreal 1,100 km latitudinal gradient over c. 20 years, we show that while most morphological or life-history traits were not strongly connected with range shifts, moths and birds occupying a narrower thermal niche and butterflies occupying a broader moisture niche across their European distribution show stronger shifts towards the north. Our results indicate that the climatic niche may be important for predicting responses under climate change and as such warrants further investigation of potential mechanistic underpinnings.

The Arctic Plant Aboveground Biomass Synthesis Dataset.

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m-2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

Stochastic species distributions are driven by organism size.

The strengths of environmental drivers and biotic interactions are expected to show large variability across organism groups. We tested two ideas related to the degree of ecological determinism vs. stochasticity using a large data set comprising bacterio-, phyto-, and zooplankton. We expected that (1) there are predictable, size-driven differences in the degree to which planktonic taxa respond to different drivers such as water chemistry, biotic interactions, and climatic variables; and (2) species distribution models show lowest predictive performance for the smallest taxa due to the stochastic distributions of microbes. Generalized linear models (GLMs), generalized additive models (GAMs), and generalized boosted methods (GBMs) were constructed for 84 species to model their occurrence as a function of eight predictors. Predictive performance was measured as the area under the curve (AUC) of the receiver-operating characteristic plot and true skill statistic (TSS) using independent model evaluation data. We found that the model performances were typically remarkably low for all planktonic groups. The proportion of satisfactory models (AUC > 0.7) was lowest for bacteria (11.1% of the models), followed by phyto- (24.2%) and zooplankton (38.1%). The occurrences of taxa within all planktonic groups were related to climatic variables to a certain degree, but bacteria showed the strongest associations with the climatic variables. Moreover, zooplankton occurrences were more related to biotic variables than the occurrences of smaller taxa, while phytoplankton occurrences were more related to water chemistry. We conclude that the occurrences of planktonic taxa are highly unpredictable and that stochasticity in occurrences is negatively related to the organism size perhaps due to efficient dispersal and fast population dynamics among the smallest taxa.

Horizontal, but not vertical, biotic interactions affect fine-scale plant distribution patterns in a low-energy system.

Studies of species range determinants have traditionally focused on abiotic variables (typically climatic conditions), and therefore the recent explicit consideration of biotic interactions represents an important advance in the field. While these studies clearly support the role of biotic interactions in shaping species distributions, most examine only the influence of a single species and/or a single interaction, failing to account for species being subject to multiple concurrent interactions. By fitting species distribution models (SDMs), we examine the influence of multiple vertical (i.e., grazing, trampling, and manuring by mammalian herbivores) and horizontal (i.e., competition and facilitation; estimated from the cover of dominant plant species) interspecific interactions on the occurrence and cover of 41 alpine tundra plant species. Adding plant-plant interactions to baseline SDMs (using five field-quantified abiotic variables) significantly improved models' predictive power for independent data, while herbivore-related variables had only a weak influence. Overall, abiotic variables had the strongest individual contributions to the distribution of alpine tundra plants, with the importance of horizontal interaction variables exceeding that of vertical interaction variables. These results were consistent across three modeling techniques, for both species occurrence and cover, demonstrating the pattern to be robust. Thus, the explicit consideration of multiple biotic interactions reveals that plant-plant interactions exert control over the fine-scale distribution of vascular species that is comparable to abiotic drivers and considerably stronger than herbivores in this low-energy system.

Soil moisture's underestimated role in climate change impact modelling in low-energy systems.

Shifts in precipitation regimes are an inherent component of climate change, but in low-energy systems are often assumed to be less important than changes in temperature. Because soil moisture is the hydrological variable most proximally linked to plant performance during the growing season in arctic-alpine habitats, it may offer the most useful perspective on the influence of changes in precipitation on vegetation. Here we quantify the influence of soil moisture for multiple vegetation properties at fine spatial scales, to determine the potential importance of soil moisture under changing climatic conditions. A fine-scale data set, comprising vascular species cover and field-quantified ecologically relevant environmental parameters, was analysed to determine the influence of soil moisture relative to other key abiotic predictors. Soil moisture was strongly related to community composition, species richness and the occurrence patterns of individual species, having a similar or greater influence than soil temperature, pH and solar radiation. Soil moisture varied considerably over short distances, and this fine-scale heterogeneity may contribute to offsetting the ecological impacts of changes in precipitation for species not limited to extreme soil moisture conditions. In conclusion, soil moisture is a key driver of vegetation properties, both at the species and community level, even in this low-energy system. Soil moisture conditions represent an important mechanism through which changing climatic conditions impact vegetation, and advancing our predictive capability will therefore require a better understanding of how soil moisture mediates the effects of climate change on biota.

Bioclimatic atlas of the terrestrial Arctic.

The Arctic is the region on Earth that is warming at the fastest rate. In addition to rising means of temperature-related variables, Arctic ecosystems are affected by increasingly frequent extreme weather events causing disturbance to Arctic ecosystems. Here, we introduce a new dataset of bioclimatic indices relevant for investigating the changes of Arctic terrestrial ecosystems. The dataset, called ARCLIM, consists of several climate and event-type indices for the northern high-latitude land areas > 45°N. The indices are calculated from the hourly ERA5-Land reanalysis data for 1950-2021 in a spatial grid of 0.1 degree (~9 km) resolution. The indices are provided in three subsets: (1) the annual values during 1950-2021; (2) the average conditions for the 1991-2020 climatology; and (3) temporal trends over 1951-2021. The 72-year time series of various climate and event-type indices draws a comprehensive picture of the occurrence and recurrence of extreme weather events and climate variability of the changing Arctic bioclimate.

Plant traits poorly predict winner and loser shrub species in a warming tundra biome.

Climate change is leading to species redistributions. In the tundra biome, shrubs are generally expanding, but not all tundra shrub species will benefit from warming. Winner and loser species, and the characteristics that may determine success or failure, have not yet been fully identified. Here, we investigate whether past abundance changes, current range sizes and projected range shifts derived from species distribution models are related to plant trait values and intraspecific trait variation. We combined 17,921 trait records with observed past and modelled future distributions from 62 tundra shrub species across three continents. We found that species with greater variation in seed mass and specific leaf area had larger projected range shifts, and projected winner species had greater seed mass values. However, trait values and variation were not consistently related to current and projected ranges, nor to past abundance change. Overall, our findings indicate that abundance change and range shifts will not lead to directional modifications in shrub trait composition, since winner and loser species share relatively similar trait spaces.

Is catchment productivity a useful predictor of taxa richness in lake plankton communities?

The influence of catchment variables on lake organisms is understudied. The terrestrial zone in the vicinity of lakes is, however, probably highly important for biota due to the effects on water chemistry and to various processes operating across ecosystem boundaries. We examined the relative importance of lake and catchment variables, as well as large-scale geographical factors, on the taxa richness of phyto- and zooplankton in 100 small lakes in Finland. In variation partitioning, the variability of phytoplankton richness was most strongly related to the effects of lake variables, the joint effects of lake and catchment variables, and the joint effects of all three groups of variables. Zooplankton richness, in turn, was most strongly related to the effects of lake and catchment variables and the joint effect of lake and catchment variables. The exact results of the variation partitioning depended on the catchment sizes considered in the regression models. Among lake variables, planktonic richness was strongly related to variables indicating productivity. Among catchment variables, the normalized difference vegetation index (NDVI), indicating catchment productivity, showed a relatively strong association with planktonic richness. These results provide evidence that catchment variables such as the NDVI may be efficient predictors of planktonic richness in small lakes. It is possible that individual lakes embedded in a highly productive landscape have higher taxa richness than solitary, potentially productive lakes because of the high influx of dispersing propagules from the regional pool. We also suggest that catchment variables may respond to environmental changes at different scales than the lake variables, and explicit consideration of catchment productivity would therefore be useful when planning research and monitoring programs for freshwater organisms.