Measures of resilience: the response of coastal sage scrub to fire

Measures of four components of resilience are developed and used to quantify the response of coastal sage scrub to fire in southern California: (1) elasticity (rate of recovery following disturbance), (2) amplitude (threshold of disturbance beyond which recovery to the original state no longer occurs), (3) malleability (extent of alteration of the new stable-state from the original) and (4) damping (extent and duration of oscillation in an ecosystem parameter following disturbance). Vegetation and soil properties measured before fire, and for the first 5–6 yr after fire on four coastal (Venturan association) and four inland (Riversidian association) sites of coastal sage were used to follow changes. In addition, results from a simulation model of post-fire succession in Venturan coastal sage scrub (the FINICS model of Malanson) were used to examine resilience behavior over a 200 yr period. Resilience behavior of coastal sage scrub is critically influenced by the presence of a competitive mix of inherently strongly and weakly resprouting species. Sites dominated by weak resprouters exhibit lower elasticity and less damping of year-to-year fluctuations in composition in the early post-fire years. Sites with a mixture of weak and strong resprouters have a lower threshold of disturbance (amplitude) before species extirpation occurs, a result intensified by a higher frequency of disturbance. Malleability is also greater in these systems under higher disturbance frequency.Nomenclature follows. P.A. Munz, 1974. A Flora of Southern California. Univ. California Press.     

Foraging ecology of the California gnatcatcher deduced from fecal samples

The California gnatcatcher is a threatened species essentially restricted to coastal sage scrub habitat in southern California. Its distribution and population dynamics have been studied intensely, but little is known about its diet. We identified arthropod fragments in 33 fecal samples of the California gnatcatcher to gain insight into its foraging ecology and diet. Fecal samples were collected from adult males, adult females, fledglings, and nestlings. Leaf- and planthoppers (Homoptera) and spiders (Araneae) predominated numerically in samples. Spider prey was most diverse, with eight families represented. True bugs (Hemiptera) and wasps, bees, and ants (Hymenoptera) were only minor components of the gnatcatcher diet. Gnatcatcher adults selected prey to feed their young that was larger than expected given the distribution of arthropod size available in their environment, and chicks were provisioned with larger prey items and significantly more grasshoppers and crickets (Orthoptera) and spiders than adults consumed themselves. Both adults and young consumed more sessile than active prey. Further studies are needed to determine whether arthropods sampled in coastal sage scrub that are common in fecal samples are good indicators of California gnatcatcher habitat. Received: 30 December 1998 / Accepted: 28 April 1999     

Geographical distribution modelling of the bronze bug: a worldwide invasion

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Biotic interactions improve prediction of boreal bird distributions at macro-scales

Aim The role of biotic interactions in influencing species distributions at macro‐scales remains poorly understood. Here we test whether predictions of distributions for four boreal owl species at two macro‐scales (10 × 10 km and 40 × 40 km grid resolutions) are improved by incorporating interactions with woodpeckers into climate envelope models. Location Finland, northern Europe. Methods Distribution data for four owl and six woodpecker species, along with data for six land cover and three climatic variables, were collated from 2861 10 × 10 km grid cells. Generalized additive models were calibrated using a 50% random sample of the species data from western Finland, and by repeating this procedure 20 times for each of the four owl species. Models were fitted using three sets of explanatory variables: (1) climate only; (2) climate and land cover; and (3) climate, land cover and two woodpecker interaction variables. Models were evaluated using three approaches: (1) examination of explained deviance; (2) four‐fold cross‐validation using the model calibration data; and (3) comparison of predicted and observed values for independent grid cells in eastern Finland. The model accuracy for approaches (2) and (3) was measured using the area under the curve of a receiver operating characteristic plot. Results At 10‐km resolution, inclusion of the distribution of woodpeckers as a predictor variable significantly improved the explanatory power, cross‐validation statistics and the predictive accuracy of the models. Inclusion of land cover led to similar improvements at 10‐km resolution, although these improvements were less apparent at 40‐km resolution for both land cover and biotic interactions. Main conclusions Predictions of species distributions at macro‐scales may be significantly improved by incorporating biotic interactions and land cover variables into models. Our results are important for models used to predict the impacts of climate change, and emphasize the need for comprehensive evaluation of the reliability of species–climate impact models.     

Prediction of the distribution of Arctic-nesting pink-footed geese under a warmer climate scenario

Global climate change is expected to shift species ranges polewards, with a risk of range contractions and population declines of especially high-Arctic species. We built species distribution models for Svalbard-nesting pink-footed geese to relate their occurrence to environmental and climatic variables, and used the models to predict their distribution under a warmer climate scenario. The most parsimonious model included mean May temperature, the number of frost-free months and the proportion of moist and wet moss-dominated vegetation in the area. The two climate variables are indicators for whether geese can physiologically fulfil the breeding cycle or not and the moss vegetation is an indicator of suitable feeding conditions. Projections of the distribution to warmer climate scenarios propose a large north- and eastward expansion of the potential breeding range on Svalbard even at modest temperature increases (1 and 2 °C increase in summer temperature, respectively). Contrary to recent suggestions regarding future distributions of Arctic wildlife, we predict that warming may lead to a further growth in population size of, at least some, Arctic breeding geese.     

Habitat distribution modelling,under the present climatic scenario,of the threatened endemic Iberian Delphinium fissum subsp. sordidum (Ranunculaceae) and implications for its conservation

We modelled the potential habitat of a threatened species D. fissum subsp. sordidum, an endemic hemicryptophyte with a disjunct distribution in the Iberian Peninsula. Maxent was used to predict the subspecies habitat suitability by relating field sample-based distributional information with environmental and topographic variables. Our results suggest that the model performed well, predicting with high accuracy the current distribution of the species. The variables that most contributed to the model were Mean Temperature of Wettest Quarter (MTWtQ), Precipitation of Warmest Quarter (PWmQ), Temperature Annual Range (TAR) and Slope (Slo). These variables are biological significant for the taxon, as they have decisive influence in the critical stages of germination and fruiting. The current and potential distributional areas identified by the model fall mainly in regions with some degree of environmental protection, with some exceptions. A recovery plan for the species should be considered. Species Distribution Modelling cannot substitute long-term monitoring programmes, yet it is a useful tool for identifying appropriate areas of taxon occurrence, and thus allow for efficient use of the economic and human resources.     

MigClim: Predicting plant distribution and dispersal in a changing climate

Aim Many studies have forecasted the possible impact of climate change on plant distributions using models based on ecological niche theory, but most of them have ignored dispersal‐limitations, assuming dispersal to be either unlimited or null. Depending on the rate of climatic change, the landscape fragmentation and the dispersal capabilities of individual species, these assumptions are likely to prove inaccurate, leading to under‐ or overestimation of future species distributions and yielding large uncertainty between these two extremes. As a result, the concepts of ‘potentially suitable’ and ‘potentially colonizable’ habitat are expected to differ significantly. To quantify to what extent these two concepts can differ, we developed Mig Clim, a model simulating plant dispersal under climate change and landscape fragmentation scenarios. Mig Clim implements various parameters, such as dispersal distance, increase in reproductive potential over time, landscape fragmentation or long‐distance dispersal. Location Western Swiss Alps. Methods Using our Mig Clim model, several simulations were run for two virtual species by varying dispersal distance and other parameters. Each simulation covered the 100‐year period 2001–2100 and three different IPCC‐based temperature warming scenarios were considered. Results of dispersal‐limited projections were compared with unlimited and no‐dispersal projections. Results Our simulations indicate that: (1) using realistic parameter values, the future potential distributions generated using Mig Clim can differ significantly (up to more than 95% difference in colonized surface) from those that ignore dispersal; (2) this divergence increases under more extreme climate warming scenarios and over longer time periods; and (3) the uncertainty associated with the warming scenario can be as large as the one related to dispersal parameters. Main conclusions Accounting for dispersal, even roughly, can importantly reduce uncertainty in projections of species distribution under climate change scenarios.     

Modelling biome shifts and tree cover change for 2050 in West Africa

Aim Africa is expected to face severe changes in climatic conditions. Our objectives are: (1) to model trends and the extent of future biome shifts that may occur by 2050, (2) to model a trend in tree cover change, while accounting for human impact, and (3) to evaluate uncertainty in future climate projections. Location West Africa. Methods We modelled the potential future spatial distribution of desert, grassland, savanna, deciduous and evergreen forest in West Africa using six bioclimatic models. Future tree cover change was analysed with generalized additive models (GAMs). We used climate data from 17 general circulation models (GCMs) and included human population density and fire intensity to model tree cover. Consensus projections were derived via weighted averages to: (1) reduce inter‐model variability, and (2) describe trends extracted from different GCM projections. Results The strongest predicted effect of climate change was on desert and grasslands, where the bioclimatic envelope of grassland is projected to expand into the desert by an area of 2 million km². While savannas are predicted to contract in the south (by 54 ± 22 × 10⁴ km²), deciduous and evergreen forest biomes are expected to expand (64 ± 13 × 10⁴ km² and 77 ± 26 × 10⁴ km²). However, uncertainty due to different GCMs was particularly high for the grassland and the evergreen biome shift. Increasing tree cover (1–10%) was projected for large parts of Benin, Burkina Faso, Côte d’Ivoire, Ghana and Togo, but a decrease was projected for coastal areas (1–20%). Furthermore, human impact negatively affected tree cover and partly changed the direction of the projected change from increase to decrease. Main conclusions Considering climate change alone, the model results of potential vegetation (biomes) show a ‘greening’ trend by 2050. However, the modelled effects of human impact suggest future forest degradation. Thus, it is essential to consider both climate change and human impact in order to generate realistic future tree cover projections.     

An ecosystem model-based estimate of changes in water availability differs from water proxies that are commonly used in species distribution models

Aim  To assess whether the water availability measures commonly used in species distribution models might be misleading because they do not account for the hydrological effects of changes in vegetation structure and functioning.
Location  Europe.
Methods  We compared different methods for estimating water availability in species distribution models with the soil water content predicted by a process-based ecosystem model. The latter also accounted for the hydrological effects of dynamic changes in vegetation structure and functioning, including potential physiological effects of increasing CO₂.
Results  All proxies showed similar patterns of water availability across Europe for current climate, but when projected into the future, the changes in the simpler water availability measures showed no correlation with those projected by the more complex ecosystem model, even if CO₂ effects were switched off.
Main conclusions  Results from species distribution modelling studies concerning future changes in species ranges and biodiversity should be interpreted with caution, and more process-based representations of the water balance of terrestrial ecosystems should be considered within these models.     

Improving species distribution models for climate change studies: variable selection and scale

Statistical species distribution models (SDMs) are widely used to predict the potential changes in species distributions under climate change scenarios. We suggest that we need to revisit the conceptual framework and ecological assumptions on which the relationship between species distributions and environment is based. We present a simple conceptual framework to examine the selection of environmental predictors and data resolution scales. These vary widely in recent papers, with light inconsistently included in the models. Focusing on light as a necessary component of plant SDMs, we briefly review its dependence on aspect and slope and existing knowledge of its influence on plant distribution. Differences in light regimes between north‐ and south‐facing aspects in temperate latitudes can produce differences in temperature equivalent to moves 200 km polewards. Local topography may create refugia that are not recognized in many climate change SDMs using coarse‐scale data. We argue that current assumptions about the selection of predictors and data resolution need further testing. Application of these ideas can clarify many issues of scale, extent and choice of predictors, and potentially improve the use of SDMs for climate change modelling of biodiversity.     

Climate controls the distribution of a widespread invasive species: implications for future range expansion

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Choice of predictor variables as a source of uncertainty in continental‐scale species distribution modelling under climate change

Aim Species distribution modelling is commonly used to guide future conservation policies in the light of potential climate change. However, arbitrary decisions during the model‐building process can affect predictions and contribute to uncertainty about where suitable climate space will exist. For many species, the key climatic factors limiting distributions are unknown. This paper assesses the uncertainty generated by using different climate predictor variable sets for modelling the impacts of climate change. Location Europe, 10° W to 50° E and 30° N to 60° N. Methods Using 1453 presence pixels at 30 arcsec resolution for the great bustard (Otis tarda), predictions of future distribution were made based on two emissions scenarios, three general climate models and 26 sets of predictor variables. Twenty‐six current models were created, and 156 for both 2050 and 2080. Map comparison techniques were used to compare predictions in terms of the quantity and the location of presences (map comparison kappa, MCK) and using a range change index (RCI). Generalized linear models (GLMs) were used to partition explained deviance in MCK and RCI among sources of uncertainty. Results The 26 different variable sets achieved high values of AUC (area under the receiver operating characteristic curve) and yet introduced substantial variation into maps of current distribution. Differences between maps were even greater when distributions were projected into the future. Some 64–78% of the variation between future maps was attributable to choice of predictor variable set alone. Choice of general climate model and emissions scenario contributed a maximum of 15% variation and their order of importance differed for MCK and RCI. Main conclusions Generalized variable sets produce an unmanageable level of uncertainty in species distribution models which cannot be ignored. The use of sound ecological theory and statistical methods to check predictor variables can reduce this uncertainty, but our knowledge of species may be too limited to make more than arbitrary choices. When all sources of modelling uncertainty are considered together, it is doubtful whether ensemble methods offer an adequate solution. Future studies should explicitly acknowledge uncertainty due to arbitrary choices in the model‐building process and develop ways to convey the results to decision‐makers.     

Climate versus weather extremes: Temporal predictor resolution matters for future rather than current regional species distribution models

Aim

Climate is considered a major driver of species distributions. Long‐term climatic means are commonly used as predictors in correlative species distribution models (SDMs). However, this coarse temporal resolution does not reflect local conditions that populations experience, such as short‐term weather extremes, which may have a strong impact on population dynamics and local distributions. We here compare the performance of climate‐ and weather‐based predictors in regional SDMs and their influence on future predictions, which are increasingly used in conservation planning.

Location

South‐western Germany.

Methods

We built different SDMs for 20 Orthoptera species based on three predictor sets at a regional scale for current and future climate scenarios. We calculated standard bioclimatic variables and yearly and seasonal sets of climate change indicating variables of weather extremes. As the impact of extreme events may be stronger for habitat specialists than for generalists, we distinguished species’ degrees of specialization. We computed linear mixed‐effects models to identify significant effects of algorithm, predictor set and specialization on model performance and calculated correlations and geographical niche overlap between spatial predictions.

Results

Current predictions were rather similar among all predictor sets, but highly variable for future climate scenarios. Bioclimatic and seasonal weather predictors performed slightly better than yearly weather predictors, though performance differences were minor. We found no evidence that specialists are more sensitive to weather extremes than generalists.

Main conclusions

For future projections of species distributions, SDM predictor selection should not solely be based on current performances and predictions. As long‐term climate and short‐term weather predictors represent different environmental drivers of a species’ distribution, we argue to interpret diverging future projections as complements. Even if similar current performances and predictions might imply their equivalency, favouring one predictor set neglects important aspects of future distributions and might mislead conservation decisions based on them.