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The potential utility of Species Distribution Models (SDMs) in conservation is apparent. One application for rare or highly cryptic taxa is using model predictions to increase the efficiency of sampling effort. Though this method is potentially powerful, the accuracy of model predictions is rarely tested in the field. Further, uncertainty remains about whether validation statistics reflect true model performance, particularly for species of high conservation concern. We assessed the usefulness of SDMs for predicting the distribution of six species of lizards in the Mackenzie Basin (Te Manahuna), Aotearoa New Zealand (NZ). We built MaxEnt models using readily available occurrence data and a publicly available suite of environmental predictors. We validated model performance using both data partitioning and independent occurrence records collected in the 2022/23 austral summer. Cross‐validation suggested that the top models for each species generated reasonably accurate predictions; however, for common species, predictive accuracy decreased notably when validating with independent data. Models for rare species performed more variably when validated with independent data; however, these models were overfit and based on few data, making it difficult to have confidence in the resulting ions. We suggest that limitations in historical occurrence data, current knowledge of species ecology and low resolution of predictor data likely restrict the relevance of predictive modelling for NZ lizard species. Whilst attractive to species managers and easy to generate, predictive models should be subject to ground‐truthing with temporally relevant data prior to being used to inform sampling effort. We ground‐truthed MaxEnt Species Distribution Models for six species of New Zealand lizards. Cross‐validation suggested that top models for each species generated reasonably accurate predictions; however, predictive accuracy was low for common species when validating with independent data. Whilst attractive to species managers and easy to generate, models should be ground‐truthed with temporally relevant data prior to their use in informing species management of sampling effort.

Species distribution models (SDMs) can be used to predict distributions in novel times or space (termed transferability) and fill knowledge gaps for areas that are data poor. In conservation, this can be used to determine the extent of spatial protection required. To understand how well a model transfers spatially, it needs to be independently tested, using data from novel habitats. Here, we test the transferability of SDMs for Hector's dolphin (Cephalorhynchus hectori), a culturally important (taonga) and endangered, coastal delphinid, endemic to Aotearoa New Zealand. We collected summer distribution data from three populations from 2021 to 2023. Using Generalised Additive Models, we built presence/absence SDMs for each population and validated the predictive ability of the top models (with TSS and AUC). Then, we tested the transferability of each top model by predicting the distribution of the remaining two populations. SDMs for two populations showed useful performance within their respective areas (Banks Peninsula and Otago), but when used to predict the two areas outside the models' source data, performance declined markedly. SDMs from the third area (Timaru) performed poorly, both for prediction within the source area and when transferred spatially. When data for model building were combined from two areas, results were mixed. Model interpolation was better when presence/absence data from Otago, an area of low density, were combined with data from areas of higher density, but was otherwise poor. The overall poor transferability of SDMs suggests that habitat preferences of Hector's dolphins vary between areas. For these dolphins, population‐specific distribution data should be used for conservation planning. More generally, we demonstrate that a one model fits all approach is not always suitable. When SDMs are used to predict distribution in data‐poor areas an assessment of performance in the new habitat is required, and results should be interpreted with caution. Species distribution models (SDMs) are commonly used to predict habitat and distribution for species in data poor areas, under the assumption that habitat use is the same across space. We tested the transferability of SDMs for Hector's dolphin, revealing differences in habitat use between populations. These results indicate that to understand habitat use of Hector's dolphin, local data is required for the model building process.

Prediction of future population trajectories is vital in the management of threatened species but requires accurate estimates of demographic rates. One such parameter is fecundity, which is commonly expressed as the number of offspring produced per female per year. The endangered Hector's dolphin (Cephalorhynchus hectori) is Aotearoa New Zealand's only endemic cetacean and is threatened by bycatch from inshore trawl and gillnet fisheries. Here, we take advantage of 40 years of continued photo‐identification effort at Banks Peninsula to construct a Bayesian open‐population multi‐event capture–recapture model. We estimated fecundity for Hector's dolphins at 0.29 (95% credible interval [CI]: 0.22–0.39) which corresponds to an average calving frequency of one calf every 3.4 years (95% CI: 2.5–4.7 years). This new estimate is substantially lower and more precise than the previous estimate of fecundity for Hector's dolphins (e.g., 0.409, 95% CI: 0.267–0.635), but is based on a larger dataset, and aligns closely with estimates from other dolphin species. This updated estimate of fecundity indicates a lower capacity for population growth and reduced resilience to anthropogenic threats, including bycatch in fisheries. Updated estimate of fecundity for Hector's dolphin is lower and more precise than prior estimation. This latest estimate suggests a lower capacity for population growth than previously thought and a reduced resilience to anthropogenic threats.