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.

Effect on food intake and body weight of lesions in and adjacent to the posterodorsal amygdala in rats

Bilateral lesions centered in the posterodorsal amygdala of female rats resulted in hyperphagia and excessive weight gain. The brain damage extended posteriorly through the amygdalohippocampal area and into the ventral hippocampus and subiculum. Lesions centered in the ventral hippocampal formation had a less pronounced, although still significant, effect on body weight. Damage to large areas of the amygdala, including the anterior, basolateral, and corticomedial groups of nuclei, was without effect. A serial reconstruction of overlapping areas of destruction for both effective and ineffective lesions is presented.

Level of corticosterone replacement determines body weight gain in adrenalectomized rats with VMH lesions

Adrenalectomized female rats with lesions of the ventromedial hypothalamus or sham lesions were given SC implants of wax pellets or a fused mixture of corticosterone-cholesterol (40, 75, or 130% by weight). In animals with sham lesions, high dosages of corticosterone proved to be catabolic (r = -0.61 between plasma corticosterone and weight change). In marked contrast, animals with VMH lesions displayed substantial weight gains at all circulating levels of corticosterone, with a significant positive correlation (r = +0.48) between these two variables. It is concluded that: a) damage to the basomedial hypothalamus alters an organism's response to corticosterone at both ends of the dose-response curve, and b) both Type I and Type II corticosterone receptors in the brain play a role in hypothalamic obesity.

Hyperphagia and obesity in female rats with temporal lobe lesions

Damage to the temporal lobes in cats, dogs, and primates has long been known to result in hyperphagia and obesity, but research into the role of this area of the brain in feeding behavior has largely been neglected because of an inability to produce similar results in rats. The present study reports hyperphagia and obesity in female rats with small electrolytic lesions centered in the posterodorsal amygdala. Daily food intake more than doubled in the first few days after surgery and mean weight gain was more than four times that observed in animals with sham lesions during the first 26 days. The rats with lesions were not hyperresponsive to a switch in diets (lab chow to high-fat, and back). In all animals that gained abnormal amounts of weight, the posterior extent of the lesions extended through the amygdalohippocampal area into the ventral hippocampal formation. The results suggest that the temporal lobe is an important extrahypothalamic site for the regulation of food intake in rodents.

Abnormal weight gain in rats with amygdaloid lesions

Marked weight gain was observed in female rats given small electrolytic lesions in the dorsal posterior portion of the amygdala. With a standard lab pellet diet, weight gains typically ranged between 20-30 g during the first 3 postoperative days, and between 60-100 g over the first 20 days. Rats with sham lesions generally gained only 5-15 g in 20 days. The results are consistent with much older studies that reported obesity in cats, dogs, and primates with lesions of the amygdala.