The effects of scale and sample size on the accuracy of spatial predictions of tiger beetle (Cicindelidae) species richness

We apply geostatistical modeling techniques to investigate spatial patterns of species richness. Unlike most other statistical modeling techniques that are valid only when observations are independent, geostatistical methods are designed for applications involving spatially dependent observations. When spatial dependencies, which are sometimes called autocorrelations, exist, geostatistical techniques can be applied to produce optimal predictions in areas (typically proximate to observed data) where no observed data exist. Using tiger beetle species (Cicindelidae) data collected in western North America, we investigate the characteristics of spatial relationships in species numbers data, First, we compare the accuracy of spatial predictions of species richness when data from grid squares of two different sizes (scales) are used to form the predictions. Next we examine how prediction accuracy varies as a function of areal extent of the region under investigation. Then we explore the relationship between the number of observations used to build spatial prediction models and prediction accuracy. Our results indicate that, within the taxon of tiger beetles and for the two scales we investigate, the accuracy of spatial predictions is unrelated to scale and that prediction accuracy is not obviously related lo the areal extent of the region under investigation. We also provide information about the relationship between sample size and prediction accuracy, and, finally, we show that prediction accuracy may be substantially diminished if spatial correlations in the data are ignored.     

Analysis of determinants of mammalian species richness in South America using spatial autoregressive models

Classically, hypotheses concerning the distribution of species have been explored by evaluating the relationship between species richness and environmental variables using ordinary least squares (OLS) regression. However, environmental and ecological data generally show spatial autocorrelation, thus violating the assumption of independently distributed errors. When spatial autocorrelation exists, an alternative is to use autoregressive models that assume spatially autocorrelated errors. We examined the relationship between mammalian species richness in South America and environmental variables, thereby evaluating the relative importance of four competing hypotheses to explain mammalian species richness. Additionally, we compared the results of ordinary least squares (OLS) regression and spatial autoregressive models using Conditional and Simultaneous Autoregressive (CAR and SAR, respectively) models. Variables associated with productivity were the most important at determining mammalian species richness at the scale analyzed. Whereas OLS residuals between species richness and environmental variables were strongly autocorrelated, those from autoregressive models showed less spatial autocorrelation, particularly the SAR model, indicating its suitability for these data. Autoregressive models also fit the data better than the OLS model (increasing R² by 5–14%), and the relative importance of the explanatory variables shifted under CAR and SAR models. These analyses underscore the importance of controlling for spatial autocorrelation in biogeographical studies.     

Null models and spatial patterns of species richness in South American birds of prey

In this paper, we used geostatistical approaches to describe bi-dimensional spatial patterns in species richness of South American birds of prey (Falconiformes and Strigiformes). They indicated strong spatial patterns both across latitude and longitude, for the two groups. These patterns were then correlated with those expected by a bi-dimensional null model constructed to take into account South America continental edges. As considerable departures from the null model were observed, there may be other ecological or evolutionary explanations for spatial patterns in species richness. Variation seems to be related to habitat heterogeneity across the continent, especially when considering differences between habitats in the central and south-eastern portion of the continent and in the Andean region. This supports previous conclusions that habitat type and heterogeneity affect species richness and abundance at different spatial scales.     

The influence of spatial scale on cross-taxon congruence patterns and prediction accuracy of species richness

A comparison of species richness patterns of butterflies and birds was made using data from two grids of squares (small squares 137.5 km on a side and large squares 275 km on a side) covering western North America. Using geostatistical procedures, we found that the spatial patterns of species richness of these two taxa were related. The influence of grain size on the strength of this relationship was investigated by analysing the two data sets. For both data sets, the number of butterfly species in a square was a statistically significant predictor of the corresponding number of bird species. However, cross-validation techniques showed that the marginal improvement in prediction accuracy due to including butterflies as a predictor was greater in the large-square data. We explored the effect of areal extent on cross-taxon congruencies by investigating species richness patterns in four subsets of the small-square data. In regions with smaller areal extent, the cross-taxon congruence patterns were not substantially different from the pattern found in the full data set. Finally, using data-splitting techniques, we explored the relationships between prediction accuracy of species richness, sample size, areal extent of the sample, and grain size.     

Historical legacies in the geographical diversity patterns of New World palm (Arecaceae) subfamilies

The extent to which species richness patterns of the major palm subfamilies in the Americas are controlled by lineage history was studied. Based on the fossil record, we suggest that the subfamily Coryphoideae has followed a boreotropical dispersal route into Central and South America, whereas Calamoideae (tribe Lepidocaryeae), Ceroxyloideae and Arecoideae have Gondwana/South America-biased histories. However, Arecoideae has been present and diverse in both South and Central America at least since the early Tertiary. We used regression analyses to evaluate the relative importance of environmental factors and spatial variables (as substitutes for historical or other non-environmental factors) as determinants of geographical variation in species richness for each subfamily. Given the different lineage histories, we hypothesized that: (1) coryphoid richness should be least strongly controlled by the modern environment and exhibit a strong non-environmental bias towards Central and North America, reflecting its boreotropical invasion route, (2) calamoid species richness should exhibit a non-environmental bias towards South America, reflecting its long African–South American history, and (3) arecoid species richness should be most strongly environmentally determined, reflecting the long arecoid residency in both Central and South America. The regression analyses confirmed the hypothesized effects of lineage history on the geographical patterns in species richness. Hence, modern species richness patterns in the New World palm subfamilies strongly reflect their divergent biogeographical histories.  © 2006 The Linnean Society of London, Botanical Journal of the Linnean Society , 2006, 151 , 113–125.     

Species distribution modelling as a macroecological tool: a case study using New World amphibians

Although species distribution modelling (SDM) is widely accepted among the scientific community and is increasingly used in ecology, conservation biology and biogeography, methodological limitations generate potential problems for its application in macroecology. Using amphibian species richness in North and South America, we compare species richness patterns derived from SDM maps and ‘expert’ maps to evaluate if: 1) richness patterns derived from SDM are biased toward climate‐based explanations for diversity when compared to expert maps, since SDM methods are typically based on climatic variables; and 2) SDM is a reliable tool for generating richness maps in hyperrich regions where point occurrence data are limited for many species. We found that although three widely used SDM methods overestimated amphibian species richness in grid cells when compared to expert richness maps in both North and South America due to systematic overestimation of range sizes, diversity gradients were reasonably robust at broad scales. Further, climatic variables statistically explained patterns of richness at similar levels among the different richness sources, although climatic relationships were stronger in the much better known North America than in South America. We conclude that in the face of the high deforestation rates coupled with incomplete data on species distributions, especially in the tropics, SDM represents a useful macroecological tool for investigating broad‐scale richness patterns and the dynamics between species richness and climate.     

Towards a generalized biogeography of the Southern Ocean benthos

Aim To investigate whether the biogeographical regions proposed by J. W. Hedgpeth and widely adopted by other authors hold true, are an oversimplification or with further data might show a unified Antarctic province. Location Southern Hemisphere. Methods The distributions of 1318 species of bivalves, 4656 species of gastropods, 1465 species of cheilostome and 167 species of cyclostome bryozoans were analysed for 29 regions in the Southern Hemisphere, including South American, South African, Tasmanian, New Zealand, sub‐Antarctic and Antarctic regions. We present data on species richness, rates of endemism, patterns of radiation, faunal similarities and multivariate biogeographical analyses. Results The most striking pattern to emerge from our data set of species counts per region was a strong east–west hemispheric asymmetry, with high species numbers in New Zealand, Tasmania and South Africa and low numbers in South America. In contrast, no difference was found in richness between the east and west parts of the Southern Ocean. We compared findings in our model taxa with published data on ascidians, cephalopods and pycnogonids. Further evidence of strong faunal links between the Antarctic and South America is reported in this study, although we found little evidence for a biogeographical relationship between the Antarctic or South America and New Zealand/Tasmania. Strong evidence exists for a long‐term influence of the Antarctic Circumpolar Current upon the distribution of Southern Ocean benthos. This is demonstrated by the reduced prevalence of South American species in the Antarctic and sub‐Antarctic with increasing distance from South America in the direction of the current. Three of our four study taxa (bivalves, cheilostomes and cyclostomes) show the Southern Ocean as a ‘single functional unit’ with no evidence for a biogeographical split between east and west. Main conclusions Unlike the biogeographical schemes previously proposed, we show that biogeographical regions in the Southern Ocean differ depending upon the class of animals being considered. Despite this we suggest that some general rules are viable, including species endemism rates of around 50%, a single Antarctic province and a definite distinction between the sub‐Antarctic islands influenced by South America and those of New Zealand.     

Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology

There have been numerous claims in the ecological literature that spatial autocorrelation in the residuals of ordinary least squares (OLS) regression models results in shifts in the partial coefficients, which bias the interpretation of factors influencing geographical patterns. We evaluate the validity of these claims using gridded species richness data for the birds of North America, South America, Europe, Africa, the ex‐USSR, and Australia. We used richness in 110×110 km cells and environmental predictor variables to generate OLS and simultaneous autoregressive (SAR) multiple regression models for each region. Spatial correlograms of the residuals from each OLS model were then used to identify the minimum distance between cells necessary to avoid short‐distance residual spatial autocorrelation in each data set. This distance was used to subsample cells to generate spatially independent data. The partial OLS coefficients estimated with the full dataset were then compared to the distributions of coefficients created with the subsamples. We found that OLS coefficients generated from data containing residual spatial autocorrelation were statistically indistinguishable from coefficients generated from the same data sets in which short‐distance spatial autocorrelation was not present in all 22 coefficients tested. Consistent with the statistical literature on this subject, we conclude that coefficients estimated from OLS regression are not seriously affected by the presence of spatial autocorrelation in gridded geographical data. Further, shifts in coefficients that occurred when using SAR tended to be correlated with levels of uncertainty in the OLS coefficients. Thus, shifts in the relative importance of the predictors between OLS and SAR models are expected when small‐scale patterns for these predictors create weaker and more unstable broad‐scale coefficients. Our results indicate both that OLS regression is unbiased and that differences between spatial and nonspatial regression models should be interpreted with an explicit awareness of spatial scale.     

Modelling geographical patterns in species richness using eigenvector-based spatial filters

Aim To test the mechanisms driving bird species richness at broad spatial scales using eigenvector‐based spatial filtering. Location South America. Methods An eigenvector‐based spatial filtering was applied to evaluate spatial patterns in South American bird species richness, taking into account spatial autocorrelation in the data. The method consists of using the geographical coordinates of a region, based on eigenanalyses of geographical distances, to establish a set of spatial filters (eigenvectors) expressing the spatial structure of the region at different spatial scales. These filters can then be used as predictors in multiple and partial regression analyses, taking into account spatial autocorrelation. Autocorrelation in filters and in the regression residuals can be used as stopping rules to define which filters will be used in the analyses. Results Environmental component alone explained 8% of variation in richness, whereas 77% of the variation could be attributed to an interaction between environment and geography expressed by the filters (which include mainly broad‐scale climatic factors). Regression coefficients of environmental component were highest for AET. These results were unbiased by short‐scale spatial autocorrelation. Also, there was a significant interaction between topographic heterogeneity and minimum temperature. Conclusion Eigenvector‐based spatial filtering is a simple and suitable statistical protocol that can be used to analyse patterns in species richness taking into account spatial autocorrelation at different spatial scales. The results for South American birds are consistent with the climatic hypothesis, in general, and energy hypothesis, in particular. Habitat heterogeneity also has a significant effect on variation in species richness in warm tropical regions.     

Are local patterns of anthropoid primate diversity related to patterns of diversity at a larger scale?

Aims (1) To determine the relationship between local and regional anthropoid primate species richness. (2) To establish the spatial and temporal scale at which the ultimate processes influencing patterns of primate species coexistence operate. Location Continental landmasses of Africa, South America and Asia (India to China, and all islands as far south as New Guinea). Methods The local–regional species richness relationship for anthropoid primates is estimated by regressing local richness against regional richness (independent variable). Local richness is estimated in small, replicate local assemblages sampled in regions that vary in total species richness. A strong linear relationship is taken as evidence that local assemblages are unsaturated and local richness results from proportional sampling of the regional pool. An asymptotic curvilinear relationship is interpreted to reflect saturated communities, where strong biotic interactions limit local richness and local processes structure the species assemblage. As a further test of the assumption of local assemblage saturation, we looked for density compensation in high‐density local primate assemblages. Results The local–regional species richness relationship was linear for Africa and South America, and the slope of the relationship did not differ between the two continents. For Asia, curvilinearity best described the relationship between local and regional richness. Asian primate assemblages appear to be saturated and this is confirmed by density compensation among Asian primates. However, density compensation was also observed among African primates. The apparent assemblage saturation in Asia is not a species–area phenomenon related to the small size of the isolated islands and their forest blocks, since similar low local species richness occurs in large forests on mainland and/or peninsular Asia. Main conclusions In Africa and South America local primate assemblage composition appears to reflect the influence of biogeographic processes operating on regional spatial scales and historical time scales. In Asia the composition of primate assemblages are by‐and‐large subject to ecological constraint operating over a relatively small spatial and temporal scale. The possible local influence of the El Niño Southern Oscillations on the evolution and selection of life‐history characteristics among Asian primates, and in determining local patterns of primate species coexistence, warrants closer inspection.     

Latitudinal patterns of mammalian species richness in the New World: the effects of sampling method and faunal group

Abstract. Although the latitudinal gradient of species richness for mammals in North America is well documented, few investigators have quantified the relationship in South America. We examined the pattern in North and South America, at two spatial scales (2.5° and 5°) for each of two sampling methods (quadrats and latitudinal bands). A scale effect was evident for quadrats but not for bands. Significant linear relationships between species richness and latitude were found for three faunal groups: all mammals, nonvolant species, and bats. Effects of area confound the latitudinal relationship. By statistically removing such effects, we found that the latitudinal gradient is not an artifact of the species-area relationship, and that the latitudinal gradients for North and South America were statistically indistinguishable. Our data suggest that both faunal subgroups, nonvolant species and bats, contributed substantially to the overall mammalian pattern. Further, multiple regression analyses showed that only latitude is a necessary variable to explain bat richness; for nonvolant species, in addition to latitude, area and longitude may be important.     

How to describe species richness patterns for bryophyte conservation?

A large amount of data for inconspicuous taxa is stored in natural history collections; however, this information is often neglected for biodiversity patterns studies. Here, we evaluate the performance of direct interpolation of museum collections data, equivalent to the traditional approach used in bryophyte conservation planning, and stacked species distribution models (S‐SDMs) to produce reliable reconstructions of species richness patterns, given that differences between these methods have been insufficiently evaluated for inconspicuous taxa. Our objective was to contrast if species distribution models produce better inferences of diversity richness than simply selecting areas with the higher species numbers. As model species, we selected Iberian species of the genus Grimmia (Bryophyta), and we used four well‐collected areas to compare and validate the following models: 1) four Maxent richness models, each generated without the data from one of the four areas, and a reference model created using all of the data and 2) four richness models obtained through direct spatial interpolation, each generated without the data from one area, and a reference model created with all of the data. The correlations between the partial and reference Maxent models were higher in all cases (0.45 to 0.99), whereas the correlations between the spatial interpolation models were negative and weak (−0.3 to −0.06). Our results demonstrate for the first time that S‐SDMs offer a useful tool for identifying detailed richness patterns for inconspicuous taxa such as bryophytes and improving incomplete distributions by assessing the potential richness of under‐surveyed areas, filling major gaps in the available data. In addition, the proposed strategy would enhance the value of the vast number of specimens housed in biological collections.     

Biodiversity loss under existing land use and climate change: an illustration using northern South America

Aim Species richness depends on climate and land use. Maintaining locations with favourable climate and land‐use patterns is critical for protecting biodiversity because the loss of either can reduce the species richness that an area supports. Currently, the Guiana Shield (GS) receives abundant precipitation and has relatively light land use. For species richness this constitutes a good–good combination of climate and land use, respectively. In contrast, much of eastern Brazil receives low levels of precipitation and has heavy land use, which is a bad–bad combination for species richness. Thus, the current distribution of precipitation and land use in northern South America is relatively favourable for biodiversity. Palaeoclimate and model studies suggest, however, that the precipitation patterns for the two regions have switched before and could switch in response to greenhouse gas emissions. This paper examines the potential consequences of reconfiguring climate with respect to existing land‐use patterns using South America as an example. Location South America north of 20° S and east of the Andes. Methods Ecosystem structure and function are modelled under (1) historical climate and (2) altered precipitation following a shift in the location of the Inter‐Tropical Convergence Zone (ITCZ). The distribution of precipitation, biomes, net primary productivity (NPP) and land use are then used to predict levels of species richness under the two climate scenarios. Results Climate changes could shift the distribution of vegetation and NPP such that conditions favourable for species richness in the GS region disappear. If land‐use patterns were not prohibitive in eastern Brazil, the improved climate conditions there could compensate for the GS loss (assuming migratory barriers are overcome). Instead, existing land‐use patterns cause the combined species richness projected for the two regions to plummet. Main conclusions Human activities will alter current configurations of land use and climate throughout the world. For species richness, new configurations are likely to include both positive and negative combinations of climate and land use. However, the irreversibility of past extinctions due to land‐use patterns loads the dice against species richness.     

Bivalve distribution in hydrographic regions in South America: historical overview and conservation

Based on literature review and malacological collections, 168 native freshwater bivalve and five invasive species have been recorded for 52 hydrographic regions in South America. The higher species richness has been detected in the South Atlantic, Uruguay, Paraguay, and Amazon Brazilian hydrographic regions. Presence or absence data were analysed by Principal Coordinate for Phylogeny-Weighted. The lineage Veneroida was more representative in hydrographic regions that are poorer in species and located West of South America. The Mycetopodidae and Hyriidae lineages were predominant in regions that are richest in species toward the East of the continent. The distribution of invasive species Limnoperna fortunei is not related to species richness in different hydrographic regions there. The species richness and its distribution patterns are closely associated with the geological history of the continent. The hydrographic regions present distinct phylogenetic and species composition regardless of the level of richness. Therefore, not only should the richness be considered to be a criterion for prioritizing areas for conservation, but also the phylogenetic diversity of communities engaged in services and functional aspects relevant to ecosystem maintenance. A plan to the management of this fauna according to particular ecological characteristics and human uses of hydrographic regions is needed.     

Estimated plant richness pattern across northwest South America provides similar support for the species-energy and spatial heterogeneity hypotheses

Explaining geographic variation in plant species richness at broad spatial scales has long been a major challenge. Many hypotheses have been proposed during the last 200 yr, but recent work has focused on a few major alternatives. Among these, two hypotheses contend that plant species richness reflects 1) variation in energy and water availability among sampling units (the species-energy hypothesis) and 2) habitat and topographic heterogeneity within sampling units (the spatial heterogeneity hypothesis). We used a large botanical database and regression models to simultaneously confront the predictions from both hypotheses against an estimate of vascular plant richness across northwest South America. This estimate provided similar support for both hypotheses, a result that may be seen as contrasting with the notion that variation in energy and water availability among sampling units is the main determinant of plant species richness. We discuss potential explanations for this apparent discrepancy. Regression models that incorporated the relative contributions of both hypotheses predicted that the highest plant species richness in northwest South America is found in topographically complex areas. In contrast to several of the most recent mapping efforts, lowland Amazonia was predicted to be a plant richness trough in the study region. We suggest that diverging portrayals of plant richness across northwest South America result from differences in estimates of the relative importance of the species-energy and the spatial heterogeneity hypotheses.     

Evidence of history in explaining diversity patterns in tropical riverine fish

Aim Documentation of the ongoing effect of rain forest refuges at the last glacial maximum (LGM) on patterns of tropical freshwater fish diversity. Location Tropical South and Central America, and West Africa. Methods LGM rain forest regions and species richness by drainage were compiled from published data. GIS mapping was applied to compile drainage area and contemporary primary productivity. We used multiple regression analyses, applied separately for Tropical South America, Central America and West Africa, to assess differences in species richness between drainages that were connected and disconnected to rain forest refuge zones during the LGM. Spatial autocorrelation of the residuals was tested using Moran's I statistic. We added an intercontinental comparison to our analyses to see if a historical signal would persist even when a regional historical effect (climate at the LGM) had already been accounted for. Results Both area and history (contact with LGM rain forest refuge) explained the greatest proportions of variance in the geographical pattern of riverine species richness. In the three examined regions, we found highest richness in drainages that were connected to the rain forest refuges. No significant residual spatial autocorrelation was detected after considering area, primary productivity and LGM rain forest refuges. These results show that past climatic events still affect West African and Latin American regional and continental freshwater fish richness. At the continental scale, we found South American rivers more species‐rich than expected on the basis of their area, productivity and connectedness to rain forest refuge. Conversely, Central American rivers were less species‐diverse than expected by the grouped model. African rivers were intermediate. Therefore, a historical signal persists even when a regional historical effect (climate at the LGM) had already been accounted for. Main conclusions It has been hypothesized that past climatic events have limited impact on species richness because species have tracked environmental changes through range shifts. However, when considering organisms with physically constrained dispersal (such as freshwater fish), past events leave a perceptible imprint on present species diversity. Furthermore, we considered regions that have comparable contemporary climatic and environmental characteristics, explaining the absence of a productivity effect. From the LGM to the present day (a time scale of 18,000 years), extinction processes should have played a predominant role in shaping the current diversity pattern. By contrast, the continental effects could reflect historical contingencies explained by differences in speciation and extinction rates between continents at higher time scales (millions of years).     

The relative importance of environment, human activity and space in explaining species richness of South African bird orders

Aim To assess the relative importance of environmental (climate, habitat heterogeneity and topography), human (population density, economic prosperity and land transformation) and spatial (autocorrelation) influences, and the interactions between these predictor groups, on species richness patterns of various avifaunal orders. Location South Africa. Methods Generalized linear models were used to determine the amount of variation in species richness, for each order, attributable to each of the different predictor groups. To assess the relationships between species richness and the various predictor groups, a deviance statistic (a measure of goodness of fit for each model) and the percentage deviation explained for the best fitting model were calculated. Results Of the 12 avifaunal orders examined, spatially structured environmental deviance accounted for most of the variation in species richness in 11 orders (averaging 28%), and 50% or more in seven orders. However, orders comprising mostly water birds (Charadriiformes, Anseriformes, Ciconiformes) had a relatively large component of purely spatial deviance compared with spatially structured environmental deviance, and much of this spatial deviance was due to higher‐order spatial effects such as patchiness, as opposed to linear gradients in species richness. Although human activity, in general, offered little explanatory power to species richness patterns, it was an important correlate of spatial variation in species of Charadriiformes and Anseriformes. The species richness of these water birds was positively related to the presence of artificial water bodies. Main conclusions Not all bird orders showed similar trends when assessing, simultaneously, the relative importance of environmental, human and spatial influences in affecting bird species richness patterns. Although spatially structured environmental deviance described most of the variation in bird species richness, the explanatory power of purely spatial deviance, mostly due to nonlinear geographical effects such as patchiness, became more apparent in orders representing water birds. This was especially true for Charadriiformes, where the strong anthropogenic relationship has negative implications for the successful conservation of this group.     

Predicting patterns of plant species richness in megadiverse South Africa

Using new tools (boosted regression trees) in predictive biogeography, with extensive spatial 23 distribution data for >19 000 species, we developed predictive models for South African plant species richness patterns. Further, biome level analysis explored possible functional determinants of country‐wide regional species richness. Finally, to test model reliability independently, we predicted potential alien invasive plant species richness with an independent dataset. Amongst the different hypotheses generally invoked to explain species 30 diversity (energy, favorableness, topographic heterogeneity, irregularity and seasonality), results revealed topographic heterogeneity as the most powerful single explanatory variable for indigenous South African plant species richness. Some biome‐specific responses were observed, i.e. two of the five analyzed biomes (Fynbos and Grassland) had richness best explained by the “species‐favorableness” hypothesis, but even in this case, topographic heterogeneity was also a primary predictor. This analysis, the largest conducted on an almost exhaustive species sample in a species‐rich region, demonstrates the preeminence of topographic heterogeneity in shaping the spatial pattern of regional plant species richness. Model reliability was confirmed by the considerable predictive power for alien invasive species richness. It thus appears that topographic heterogeneity controls species richness in two main ways: firstly, by providing an abundance of ecological niches in contemporary space (revealed by alien invasive species richness relationships) and secondly, by facilitating the persistence of ecological niches through time. The extraordinary richness of the South African Fynbos biome, a world‐renowned hotspot of biodiversity with the steepest environmental gradients in South Africa, may thus have arisen through both mechanisms. Comparisons with similar regions of the world outside South Africa are needed to confirm the generality of topographic heterogeneity and favorableness as predictors of plant richness.     

Does climate determine broad‐scale patterns of species richness? A test of the causal link by natural experiment

Aim Broad‐scale spatial patterns of species richness are very strongly correlated with climatic variables. If there is a causal link, i.e. if climate directly or indirectly determines patterns of richness, then when the climatic variables change, richness should change in the manner that spatial correlations between richness and climate would predict. The present study tests this prediction using seasonal changes in climatic variables and bird richness. Location We used a grid of equal area quadrats (37 000 km²) covering North and Central America as far south as Nicaragua. Methods Summer and winter bird distribution data were drawn from monographs and field guides. Climatic data came from published sources. We also used remotely sensed NDVI (normalized difference vegetation index — a measure of greenness). Results Bird species richness changes temporally (between summer and winter) in a manner that is close to, but statistically distinguishable from, the change one would predict from models relating the spatial variation in richness at a single time to climatic variables. If one further takes into account the seasonal changes in NDVI and within‐season variability of temperature and precipitation, then winter and summer richness follow congruent, statistically indistinguishable patterns. Main conclusions Our results are consistent with the hypothesis that climatic variables (temperature and precipitation) and vegetation cover directly or indirectly influence patterns of bird species richness.