Testing species distribution models across space and time: high latitude butterflies and recent warming

Abstract

Aim To quantify whether species distribution models (SDMs) can reliably forecast species distributions under observed climate change. In particular, to test whether the predictive ability of SDMs depends on species traits or the inclusion of land cover and soil type, and whether distributional changes at expanding range margins can be predicted accurately. Location FinlandMethods Using 10-km resolution butterfly atlas data from two periods, 1992-99 (t1) and 2002-09 (t2), with a significant between-period temperature increase, we modelled the effects of climatic warming on butterfly distributions with boosted regression trees (BRTs) and generalized additive models (GAMs). We evaluated model performance by using the split-sample approach with data from t1 ('nonindependent validation'), and then compared model projections based on data from t1 with species' observed distributions in t2 ('independent validation'). We compared climate-only SDMs to SDMs including land cover, soil type, or both. Finally, we related model performance to species traits and compared observed and predicted distributional shifts at northern range margins. ResultsSDMs showed fair to good model fits when modelling butterfly distributions under climate change. Model performance was lower with independent compared with non-independent validation and improved when land cover and soil type variables were included, compared with climate-only models. SDMs performed less well for highly mobile species and for species with long flight seasons and large ranges. When forecasting changes at northern range margins, correlations between observed and predicted range shifts were predominantly low.Main conclusions SDMs accurately describe current distributions of most species, yet their performance varies with species traits and the inclusion of land cover and soil type variables. Moreover, their ability to predict range shifts under climate change is limited, especially at the expanding edge. More tests with independent validations are needed to fully understand the predictive potential of SDMs across taxa and biomes.

Glacial refugia and modern genetic diversity of 22 western North American tree species

“…Other completely independent model validations (e.g. [26,27]) yield validation statistics in line with our results (electronic supplementary material, table S1).…”

“…Palaeoclimate data were generated with the delta method, overlaying low-resolution palaeoclimatic GCM hindcasts expressed as anomalies on high-resolution current climate grids. We use hindcasts from the Community Climate Model (CCM1) for the periods 6000, 11 000, 14 000, 16 000 and 21 000 years ago and the Geophysical Fluid Dynamics Laboratory model (GFDL) for the periods 6000, 9000, 14 000, 16 000 and 21 000 years ago (as described in [26,27]). …”

Tundra arthropods provide key insights into ecological responses to environmental change

“…Because of these deficiencies, red lists for Arctic arthropods are nowhere near complete, which would form the basis for targeted conservation actions. Although many Arctic arthropod species appear widespread, they are likely to suffer substantial range contractions as species from lower latitudes expand their ranges northwards (Eskildsen et al 2013). Finally, we are only just beginning to grasps the intricate species-specific responses to environmental change, the implications for food web dynamics and the detailed biotic interactions in which arthropods play a role (Schmidt et al 2017).…”

Reducción de la incertidumbre de los modelos de distribución de especies con datos ecológicos y paleoecológicos.

“…En el presente los modelos pueden validarse con datos actuales diferentes a los utilizados para calibrar el modelo como son por ejemplo, los que proceden de otros muestreos diferentes e independientes (Eskildsen et al, 2013) o imágenes de satélite. En otros periodos temporales, sí es posible realizar esta validación con datos independientes, mediante la comparación, por ejemplo, de los modelos con datos paleobotánicos (Rodríguez-Sánchez y Arroyo, 2008), permitiendo evaluar la transferibilidad temporal de los modelos hacia el pasado.…”

“…Los MDE necesitan datos independientes para ser validados estadísticamente . Algunos estudios utilizan datos de independientes para comprobar la capacidad discriminativa de los modelos (Eskildsen et al, 2013), por ejemplo, nuevos datos de observación de diferentes de los datos utilizados para calibrar los modelos ) mientras que otros comparan los resultados de los modelos con expertos en las distribuciones de las especies . En este trabajo se plantea el uso de las imágenes de satélite como fuente independiente de datos de la presencia de una especie para validar la capacidad predictiva de los modelos de distribución potencial de la especie generados con Maxent en la actualidad.…”