Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models.

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions. Predicting the UnknownPredictions facilitate the formulation of quantitative, testable hypotheses that can be refined and validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that interest in transferable models (i.e., those that can be legitimately projected beyond the spatial and temporal bounds of their underlying data [7]) has grown.Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Highlights Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.The determinants of ecological predictability are, however, still insufficiently understood.Predictions from transferred ecological models are affected by species' traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems.We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers.We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mech...

Bird-habitat models are frequently used us predictive modeling tools--for example, to predict how a species will respond to habitat modifications. We investigated the generality of thepredictionsfrom this type of model. Multivariate models were developed for Golden Eagle (Aquila chrysaetos), Raven (Corvus corax), and Buzzard (Buteo buteo) living in northwest Scotland. Data were obtained for all habitat and nest locations within an area of 2349 km 2. This assemblage of species is relatively static with respect to both occupancy and spatial posttioning. The area was split into five geographic subregions: two on the mainland and three on the adjacent Island of Mull, which has one of United Kingdom's richest raptor fauna assemblages. Because data were collected for all nest locations and habitats, it was possible to build models that did not incorporate sampling error. A range of predicttve models was developed using discriminant analysis and logistic regression. The models differed with respect to the geographical origin of the data used for model development. The predictive success of these models was then assessed by applying them to validation data. The models showed a wide range of predictive success, ranging from only 6% of nest sites correctly predicted to 100% correctly predicted. Model validation techniques were used to ensure that the models'predictions were not statistical artefacts. The variability in prediction success seemed to result from methodological and ecological processes, including the data recording scheme and interregional differences in nesting habitat. The resuits from this study suggest that conservation biologists must be very careful about making predictions from such studies because we may be working with systems that are inherently unpredictable.Probando la generalidad de los modelos de hfibitat para aves Resumen: Los modelos de h~bitatpara aves han sldo usados frecuentemente como herramientas predictivas de modelaje, por ejemplo, para predecir como una especie va a responder a modtficaciones en el hd~bitat. En el presente estudio investigamos la generalidad de las predicciones hechas por este ttpo de modelos. Modelos multivariados fueron desarrollados para las ~guilas doradas (Aquila chrysaetos), los cuervos (Corvus corax) y los buitres (Buteo buteo) que habitan el noroeste de Escocia. Se obtuvieron datos para todos los h~bitat y sittos con nidos dentro de un drea de 2349 km 2. Este conjunto de especies es relativamente estd~ttco con respectio a su posesi6n y postci6n espacial. E1 d~rea fue dividida en cinco subregtones geogrdficas; dos en tierra flrme y tres en las islas adyacentes de Mull que poseen una de las asociactones de fauna de ayes de rapifla mds ricas del Reino Unido. Debldo a que se recolectaron datos de todos los sitios con nidos y h~bitats, fue posible construir modelos que no incorporaron errores de muestreo. Se desarroll6 una serie de modelos predtctivos usando an~llsis discrlminante y regresiones logisticas. Los modelos difirieron en 1o que respecta al origen geo-gr~fi...

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Amongst raptor species, individuals with specialized diets are commonly observed to have higher reproductive output than those with general diets. A suggested cause is that foraging efficiency benefits accrue to diet specialists. This diet specificity hypothesis thus predicts that diet breadth and reproductive success should be inversely related within species. We highlight, however, that a prey availability hypothesis also makes the same prediction in some circumstances. Hence, when high diet specificity results from high encounter rates with an abundant, preferred prey, then prey availability may affect reproductive success, with diet specialization as an incidental correlate. Using three insular study areas in western Scotland, we examine diet specificity and reproductive success in Golden Eagles Aquila chrysaetos. Diet breadth and breeding productivity were not negatively related in any of our study areas, even though birds with specific diets did tend to have a higher incidence of preferred prey (grouse and lagomorphs) in the diet. Indeed, in two study areas there was evidence that diet generalists had higher breeding productivity. Our results therefore failed to support the diet specificity hypothesis but were consistent with the prey availability hypothesis. We highlight that although many other studies are superficially consistent with the diet specificity hypothesis, our study is not alone in failing to provide support and that the hypothesis does not provide a generic explanation for all relevant results. Diet specificity in predators can be at least partially a response to prey diversity, availability and distribution, and benefits associated with different prey types, so that being a generalist is not necessarily intrinsically disadvantageous. We suggest that the available evidence is more consistent with variation in prey abundance and availability as a more influential factor explaining spatial and temporal variation in breeding productivity of ‘generalist’ species such as the Golden Eagle. Under this argument, prey abundance and availability are the main drivers of variation in reproductive output. Diet specificity is a consequence of variation in prey availability, rather than a substantial cause of variation in reproductive success.

1.The afforestation of previously open habitats continues to involve conservation organizations in assessing effects on important species. We investigated the effects of commercial afforestation on golden eagles Aquila chrysaetos on the island of Mull, western Scotland, using long-term data on eagle reproductive success and occupancy on 30 home ranges, largely during 1981-99. 2. We modelled home range parameters in a geographical information system (GIS) that gave geographical location and predicted range use as a percentage of the total use. Resolution was to 50 × 50-m (equivalent) pixels, each with a predicted value of percentage use. Forest cover was created as a separate GIS layer set to be temporally dynamic and to reflect the stage at which commercial plantations made open ground unsuitable for golden eagles by canopy closure (12 years). 3. The layers for forest cover and range use were overlapped in the GIS to produce yearon-year estimates of the extent of open canopy forest (trees < 12 years), closed canopy forest (trees ≥ 12 years), semi-natural woodland, and open ground within each golden eagle range. 4. Based on their history of productivity, golden eagle ranges were classified using cluster analysis as either productive or unproductive. These two groups did not differ significantly in range size, mean elevation, variation in elevation, terrain ruggedness or mean cover of closed canopy forest. Nor was productivity related to these measures on ranges unaffected by commercial forestry. 5. Two golden eagle ranges were apparently abandoned by breeding eagles as a result of afforestation, but these losses were balanced by the formation of new ranges elsewhere. However, on ranges where forests were planted, standardized values of golden eagle productivity fell significantly after canopy closure. 6. Temporal trends in eagle productivity on ranges where forest had been planted differed significantly from ranges where no forest was planted. The productivity of forested ranges declined markedly in the mid-1990s when forest cover exceeded 10-15% of the areas probably used by range-holding golden eagles. 7. In a general linear model, using ranges with commercial forestry, productivity after canopy closure was positively associated with productivity before closure. Productivity after canopy closure was unrelated to range size, and only weakly related to the change in forest cover ( P = 0·09, 13 ranges). Changes in eagle productivity due to increased forest cover were thus too variable on individual ranges to be predicted with confidence. 8. This study demonstrates that commercial forestry can adversely affect the productivity of golden eagles but the exact scale of effect is difficult to predict as even small plantations can have an adverse influence. Vacant neighbouring ranges may also influence the response of golden eagles to increasing forest cover. We caution against using set criteria of the extent of forest cover to predict whether a range will be abandoned.