Biodiversity modeling advances will improve predictions of nature's contributions to people.

Accurate predictions of ecosystem functions and nature's contributions to people (NCP) are needed to prioritize environmental protection and restoration in the Anthropocene. However, our ability to predict NCP is undermined by approaches that rely on biophysical variables and ignore those describing biodiversity, which have strong links to NCP. To foster predictive mapping of NCP, we should harness the latest methods in biodiversity modeling. This field advances rapidly, and new techniques with promising applications for predicting NCP are still underutilized. Here, we argue that employing recent advances in biodiversity modeling can enhance the accuracy and scope of NCP maps and predictions. This enhancement will contribute significantly to the achievement of global objectives to preserve NCP, for both the present and an unpredictable future.

Breakdown in seasonal dynamics of subtropical ant communities with land-cover change.

Concerns about widespread human-induced declines in insect populations are mounting, yet little is known about how land-use change modifies both the trends and variability of insect communities, particularly in understudied regions. Here, we examine how the seasonal activity patterns of ants-key drivers of terrestrial ecosystem functioning-vary with anthropogenic land-cover change on a subtropical island landscape, and whether differences in temperature or species composition can explain observed patterns. Using trap captures sampled biweekly over 2 years from a biodiversity monitoring network covering Okinawa Island, Japan, we processed 1.2 million individuals and reconstructed activity patterns within and across habitat types. Forest communities exhibited greater temporal variability of activity than those in more developed areas. Using time-series decomposition to deconstruct this pattern, we found that sites with greater human development exhibited ant communities with diminished seasonality, reduced synchrony and higher stochasticity compared with sites with greater forest cover. Our results cannot be explained by variation in regional or site temperature patterns, or by differences in species richness or composition among sites. Our study raises the possibility that disruptions to natural seasonal patterns of functionally key insect communities may comprise an important and underappreciated consequence of global environmental change that must be better understood across Earth's biomes.

Global determinants of insect mitochondrial genetic diversity.

Understanding global patterns of genetic diversity is essential for describing, monitoring, and preserving life on Earth. To date, efforts to map macrogenetic patterns have been restricted to vertebrates, which comprise only a small fraction of Earth's biodiversity. Here, we construct a global map of predicted insect mitochondrial genetic diversity from cytochrome c oxidase subunit 1 sequences, derived from open data. We calculate the mitochondrial genetic diversity mean and genetic diversity evenness of insect assemblages across the globe, identify their environmental correlates, and make predictions of mitochondrial genetic diversity levels in unsampled areas based on environmental data. Using a large single-locus genetic dataset of over 2 million globally distributed and georeferenced mtDNA sequences, we find that mitochondrial genetic diversity evenness follows a quadratic latitudinal gradient peaking in the subtropics. Both mitochondrial genetic diversity mean and evenness positively correlate with seasonally hot temperatures, as well as climate stability since the last glacial maximum. Our models explain 27.9% and 24.0% of the observed variation in mitochondrial genetic diversity mean and evenness in insects, respectively, making an important step towards understanding global biodiversity patterns in the most diverse animal taxon.

The global distribution of known and undiscovered ant biodiversity.

Invertebrates constitute the majority of animal species and are critical for ecosystem functioning and services. Nonetheless, global invertebrate biodiversity patterns and their congruences with vertebrates remain largely unknown. We resolve the first high-resolution (~20-km) global diversity map for a major invertebrate clade, ants, using biodiversity informatics, range modeling, and machine learning to synthesize existing knowledge and predict the distribution of undiscovered diversity. We find that ants and different vertebrate groups have distinct features in their patterns of richness and rarity, underscoring the need to consider a diversity of taxa in conservation. However, despite their phylogenetic and physiological divergence, ant distributions are not highly anomalous relative to variation among vertebrate clades. Furthermore, our models predict that rarity centers largely overlap (78%), suggesting that general forces shape endemism patterns across taxa. This raises confidence that conservation of areas important for small-ranged vertebrates will benefit invertebrates while providing a "treasure map" to guide future discovery.

Linking ecological niche models and common garden experiments to predict phenotypic differentiation in stressful environments: Assessing the adaptive value of marginal populations in an alpine plant.

Environmental variation within a species' range can create contrasting selective pressures, leading to divergent selection and novel adaptations. The conservation value of populations inhabiting environmentally marginal areas remains in debate and is closely related to the adaptive potential in changing environments. Strong selection caused by stressful conditions may generate novel adaptations, conferring these populations distinct evolutionary potential and high conservation value under climate change. On the other hand, environmentally marginal populations may be genetically depauperate, with little potential for new adaptations to emerge. Here, we explored the use of ecological niche models (ENMs) linked with common garden experiments to predict and test for genetically determined phenotypic differentiation related to contrasting environmental conditions. To do so, we built an ENM for the alpine plant Silene ciliata in central Spain and conducted common garden experiments, assessing flowering phenology changes and differences in leaf cell resistance to extreme temperatures. The suitability patterns and response curves of the ENM led to the predictions that: (1) the environmentally marginal populations experiencing less snowpack and higher minimum temperatures would have delayed flowering to avoid risks of late-spring frosts and (2) those with higher minimum temperatures and greater potential evapotranspiration would show enhanced cell resistance to high temperatures to deal with physiological stress related to desiccation and heat. The common garden experiments revealed the expected genetically based phenotypic differentiation in flowering phenology. In contrast, they did not show the expected differentiation for cell resistance, but these latter experiments had high variance and hence lower statistical power. The results highlight ENMs as useful tools to identify contrasting putative selective pressures across species ranges. Linking ENMs with common garden experiments provides a theoretically justified and practical way to study adaptive processes, including insights regarding the conservation value of populations inhabiting environmentally marginal areas under ongoing climate change.

Intraspecific genetic variation matters when predicting seagrass distribution under climate change.

Seagrasses play a vital role in structuring coastal marine ecosystems, but their distributional range and genetic diversity have declined rapidly in recent decades. To improve conservation of seagrass species, it is important to predict how climate change may impact their ranges. Such predictions are typically made with correlative species distribution models (SDMs), which can estimate a species' potential distribution under present and future climatic scenarios given species' presence data and climatic predictor variables. However, these models are typically constructed with species-level data, and thus ignore intraspecific genetic variability, which can give rise to populations with adaptations to heterogeneous climatic conditions. Here, we explore the link between intraspecific adaptation and niche differentiation in Thalassia hemprichii, a seagrass broadly distributed in the tropical Indo-Pacific Ocean and a crucial provider of habitat for numerous marine species. By retrieving and re-analysing microsatellite data from previous studies, we delimited two distinct phylogeographical lineages within the nominal species and found an intermediate level of differentiation in their multidimensional environmental niches, suggesting the possibility for local adaptation. We then compared projections of the species' habitat suitability under climate change scenarios using species-level and lineage-level SDMs. In the Central Tropical Indo-Pacific region, models for both levels predicted considerable range contraction in the future, but the lineage-level models predicted more severe habitat loss. Importantly, the two modelling approaches predicted opposite patterns of habitat change in the Western Tropical Indo-Pacific region. Our results highlight the necessity of conserving distinct populations and genetic pools to avoid regional extinction due to climate change and have important implications for guiding future management of seagrasses.

Improving area of occupancy estimates for parapatric species using distribution models and support vector machines.

As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN's extent of occurrence (EOO) is the general region including the species' range, while area of occupancy (AOO) is the subset of EOO occupied by the species. Data-poor species with incomplete sampling present particular difficulties, but species distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) and do not explicitly account for biotic interactions that can impose range constraints. We sought to improve range estimates for data-poor, parapatric species by masking out areas under inferred competitive exclusion. We did so for two South American spiny pocket mice: Heteromys australis (Least Concern) and Heteromys teleus (Vulnerable due to especially poor sampling), whose ranges appear restricted by competition. For both species, we estimated EOO using SDMs and AOO with four approaches: occupied grid cells, abiotic SDM prediction, and this prediction masked by approximations of the areas occupied by each species' congener. We made the masks using support vector machines (SVMs) fit with two data types: occurrence coordinates alone; and coordinates along with SDM predictions of suitability. Given the uncertainty in calculating AOO for low-data species, we made estimates for the lower and upper bounds for AOO, but only make recommendations for H. teleus as its full known range was considered. The SVM approaches (especially the second one) had lower classification error and made more ecologically realistic delineations of the contact zone. For H. teleus, the lower AOO bound (a strongly biased underestimate) corresponded to Endangered (occupied grid cells), while the upper bounds (other approaches) led to Near Threatened. As we currently lack data to determine the species' true occupancy within the post-processed SDM prediction, we recommend that an updated listing for H. teleus include these bounds for AOO. This study advances methods for estimating the upper bound of AOO and highlights the need for better ways to produce unbiased estimates of lower bounds. More generally, the SVM approaches for post-processing SDM predictions hold promise for improving range estimates for other uses in biogeography and conservation.

Toward ecologically realistic predictions of species distributions: A cross-time example from tropical montane cloud forests.

There is an urgent need for more ecologically realistic models for better predicting the effects of climate change on species' potential geographic distributions. Here we build ecological niche models using MAXENT and test whether selecting predictor variables based on biological knowledge and selecting ecologically realistic response curves can improve cross-time distributional predictions. We also evaluate how the method chosen for extrapolation into nonanalog conditions affects the prediction. We do so by estimating the potential distribution of a montane shrew (Mammalia, Soricidae, Cryptotis mexicanus) at present and the Last Glacial Maximum (LGM). Because it is tightly associated with cloud forests (with climatically determined upper and lower limits) whose distributional shifts are well characterized, this species provides clear expectations of plausible vs. implausible results. Response curves for the MAXENT model made using variables selected via biological justification were ecologically more realistic compared with those of the model made using many potential predictors. This strategy also led to much more plausible geographic predictions for upper and lower elevational limits of the species both for the present and during the LGM. By inspecting the modeled response curves, we also determined the most appropriate way to extrapolate into nonanalog environments, a previously overlooked factor in studies involving model transfer. This study provides intuitive context for recommendations that should promote more realistic ecological niche models for transfer across space and time.

Polychlorinated biphenyls in the exterior caulk of San Francisco Bay Area buildings, California, USA.

Extensive evidence of the adverse impacts of polychlorinated biphenyls (PCBs) to wildlife, domestic animals, and humans has now been documented for over 40 years. Despite the ban on production and new use of PCBs in the United States in 1979, a number of fish consumption advisories remain in effect, and there remains considerable uncertainty regarding ongoing environmental sources and management alternatives. Using a blind sampling approach, 25 caulk samples were collected from the exterior of ten buildings in the San Francisco Bay Area and analyzed for PCBs using congener-specific gas chromatography-mass spectrometry (GC-MS) and chlorine using portable X-ray fluorescence (XRF). PCBs were detected in 88% of the caulk samples collected from the study area buildings, with 40% exceeding 50 ppm. Detectable PCB concentrations ranged from 1 to 220,000 ppm. These data are consistent with previous studies in other cities that have identified relatively high concentrations of PCBs in concrete and masonry buildings built between 1950 and 1980. Portable XRF was not a good predictor of the PCB content in caulk and the results indicate that portable XRF analysis may only be useful for identifying caulk that contains low concentrations of Cl (≤ 10,000 ppm) and by extension low or no PCBs. A geographic information system-based approach was used to estimate that 10,500 kg of PCBs remain in interior and exterior caulk in buildings located in the study area, which equates to an average of 4.7 kg PCBs per building. The presence of high concentrations in the exterior caulk of currently standing buildings suggests that building caulk may be an ongoing source of PCBs to the San Francisco Bay Area environment. Further studies to expand the currently small international dataset on PCBs in caulking materials in buildings of countries that produced or imported PCBs appear justified in the context of both human health and possible ongoing environmental release.