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Engaging the debate regarding the appropriate level of geographic diversification for multinational enterprises (MNEs), we examine a critical, yet unresolved, question: How is performance impacted by the MNE's level of intra-and inter-regional diversification versus the total level of geographic diversification? Using data from 123 U.S.-based MNEs over a seven-year period and leveraging both sales-based and subsidiary-based measures for diversification, we find that performance increases at an increasingly higher rate as firms concentrate more heavily on intra-regional diversification. Regarding inter-regional diversification and total geographic diversification, we find inverted-U relationships to exist between firm performance and the level of geographic diversification. Different from recent research on multinationality, our robustness checks indicate no evidence of a sigmoidal relationship between the degree of regional diversification and performance.

AIM: Models of species niches and distributions have become invaluable to biogeographers over the past decade, yet several outstanding methodological issues remain. Here we address three critical ones: selecting appropriate evaluation data, detecting overfitting, and tuning program settings to approximate optimal model complexity. We integrate solutions to these issues for Maxent models, using the Caribbean spiny pocket mouse, Heteromys anomalus, as an example. LOCATION: North‐western South America. METHODS: We partitioned data into calibration and evaluation datasets via three variations of k‐fold cross‐validation: randomly partitioned, geographically structured and masked geographically structured (which restricts background data to regions corresponding to calibration localities). Then, we carried out tuning experiments by varying the level of regularization, which controls model complexity. Finally, we gauged performance by quantifying discriminatory ability and overfitting, as well as via visual inspections of maps of the predictions in geography. RESULTS: Performance varied among data‐partitioning approaches and among regularization multipliers. The randomly partitioned approach inflated estimates of model performance and the geographically structured approach showed high overfitting. In contrast, the masked geographically structured approach allowed selection of high‐performing models based on all criteria. Discriminatory ability showed a slight peak in performance around the default regularization multiplier. However, regularization levels two to four times higher than the default yielded substantially lower overfitting. Visual inspection of maps of model predictions coincided with the quantitative evaluations. MAIN CONCLUSIONS: Species‐specific tuning of model parameters can improve the performance of Maxent models. Further, accurate estimates of model performance and overfitting depend on using independent evaluation data. These strategies for model evaluation may be useful for other modelling methods as well.

The late Quaternary megafauna extinction was a severe global-scale event. Two factors, climate change and modern humans, have received broad support as the primary drivers, but their absolute and relative importance remains controversial. To date, focus has been on the extinction chronology of individual or small groups of species, specific geographical regions or macroscale studies at very coarse geographical and taxonomic resolution, limiting the possibility of adequately testing the proposed hypotheses. We present, to our knowledge, the first global analysis of this extinction based on comprehensive country-level data on the geographical distribution of all large mammal species (more than or equal to 10 kg) that have gone globally or continentally extinct between the beginning of the Last Interglacial at 132 000 years BP and the late Holocene 1000 years BP, testing the relative roles played by glacial–interglacial climate change and humans. We show that the severity of extinction is strongly tied to hominin palaeobiogeography, with at most a weak, Eurasia-specific link to climate change. This first species-level macroscale analysis at relatively high geographical resolution provides strong support for modern humans as the primary driver of the worldwide megafauna losses during the late Quaternary.

During the last decade, scientists have given increasingly frequent warnings about global warming, linking it to mycotoxin-producing moulds in various geographical regions across the world. In the future, more pronounced climate change could alter host resilience and host–pathogen interaction and have a significant impact on the development of toxicogenic moulds and the production of their secondary metabolites, known as mycotoxins. The current climate attracts attention and calls for novel diagnostic tools and notions about the biological features of agricultural cultivars and toxicogenic moulds. Since European climate environments offer steadily rising opportunities for Aspergillus flavus growth, an increased risk of cereal contamination with highly toxic aflatoxins shall be witnessed in the future. On top of that, the profile (representation) of certain mycotoxigenic Fusarium species is changing ever more substantially, while the rise in frequency of Fusarium graminearum contamination, as a species which is able to produce several toxic mycotoxins, seen in northern and central Europe, is becoming a major concern. In the following paper, a high-quality approach to a preventative strategy is tailored to put a stop to the toxicogenic mould- and mycotoxin-induced contamination of foods and feeds in the foreseeable future.

Farmland abandonment is changing rural landscapes worldwide, but its impacts on biodiversity are still being debated in the scientific literature. While some researchers see it as a threat to biodiversity, others view it as an opportunity for habitat regeneration. We reviewed 276 published studies describing various effects of farmland abandonment on biodiversity and found that a study's geographic region, selected metrics, assessed taxa, and conservation focus significantly affected how those impacts were reported. Countries in Eurasia and the New World reported mainly negative and positive effects of farmland abandonment on biodiversity, respectively. Notably, contrasting impacts were recorded in different agricultural regions of the world that were otherwise similar in land-use and biodiversity characteristics. We showed that the conservation focus (pre- or post-abandonment) in different regions is an important factor influencing how scientists address the abandonment issue, and this may affect how land-use policies are defined in agricultural landscapes.

Natural selection can drive the repeated evolution of reproductive isolation, but the genomic basis of parallel speciation remains poorly understood. We analyzed whole-genome divergence between replicate pairs of stick insect populations that are adapted to different host plants and undergoing parallel speciation. We found thousands of modest-sized genomic regions of accentuated divergence between populations, most of which are unique to individual population pairs. We also detected parallel genomic divergence across population pairs involving an excess of coding genes with specific molecular functions. Regions of parallel genomic divergence in nature exhibited exceptional allele frequency changes between hosts in a field transplant experiment. The results advance understanding of biological diversification by providing convergent observational and experimental evidence for selection's role in driving repeatable genomic divergence.

The existence, sources, distribution, circulation, and physicochemical nature of macroscale oceanic water bodies have long been a focus of oceanographic inquiry. Building on that work, this paper describes an objectively derived and globally comprehensive set of 37 distinct volumetric region units, called ecological marine units (EMUs). They are constructed on a regularly spaced ocean point-mesh grid, from sea surface to seafloor, and attributed with data from the 2013 World Ocean Atlas version 2. The point attribute data are the means of the decadal averages from a 57-year climatology of six physical and chemical environment parameters (temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate). The database includes over 52 million points that depict the global ocean in x, y, and z dimensions. The point data were statistically clustered to define the 37 EMUs, which represent physically and chemically distinct water volumes based on spatial variation in the six marine environmental characteristics used. The aspatial clustering to produce the 37 EMUs did not include point location or depth as a determinant, yet strong geographic and vertical separation was observed. Twenty-two of the 37 EMUs are globally or regionally extensive, and account for 99% of the ocean volume, while the remaining 15 are smaller and shallower, and occur around coastal features. We assessed the vertical distribution of EMUs in the water column and placed them into classical depth zones representing epipelagic (0 m to 200 m), mesopelagic (200 m to 1,000 m), bathypelagic (1,000 m to 4,000 m) and abyssopelagic (>4,000 m) layers. The mapping and characterization of the EMUs represent a new spatial framework for organizing and understanding the physical, chemical, and ultimately biological properties and processes of oceanic water bodies. The EMUs are an initial objective partitioning of the ocean using longterm historical average data, and could be extended in the future by adding new classification variables and by introducing functionality to develop time-specific EMU distribution maps. The EMUs are an open-access resource, and as both a standardized geographic framework and a baseline physicochemical characterization of the oceanic environment, they are intended to be useful for disturbance assessments, ecosystem accounting exercises, conservation priority setting, and marine protected area network design, along with other research and management applications.

There are two competing hypotheses for the origin of the Indo-European language family. The conventional view places the homeland in the Pontic steppes about 6000 years ago. An alternative hypothesis claims that the languages spread from Anatolia with the expansion of farming 8000 to 9500 years ago. We used Bayesian phylogeographic approaches, together with basic vocabulary data from 103 ancient and contemporary Indo-European languages, to explicitly model the expansion of the family and test these hypotheses. We found decisive support for an Anatolian origin over a steppe origin. Both the inferred timing and root location of the Indo-European language trees fit with an agricultural expansion from Anatolia beginning 8000 to 9500 years ago. These results highlight the critical role that phylogeographic inference can play in resolving debates about human prehistory.

We present new data and analyses revealing fundamental flaws in a critique of two recent meta-analyses of local-scale temporal biodiversity change. First, the conclusion that short-term time series lead to biased estimates of long-term change was based on two errors in the simulations used to support it. Second, the conclusion of negative relationships between temporal biodiversity change and study duration was entirely dependent on unrealistic model assumptions, the use of a subset of data, and inclusion of one outlier data point in one study. Third, the finding of a decline in local biodiversity, after eliminating post-disturbance studies, is not robust to alternative analyses on the original data set, and is absent in a larger, updated data set. Finally, the undebatable point, noted in both original papers, that studies in the ecological literature are geographically biased, was used to cast doubt on the conclusion that, outside of areas converted to croplands or asphalt, the distribution of biodiversity trends is centered approximately on zero. Future studies may modify conclusions, but at present, alternative conclusions based on the geographic-bias argument rely on speculation. In sum, the critique raises points of uncertainty typical of all ecological studies, but does not provide an evidence-based alternative interpretation.

Modeling results incorporating several distinct urban expansion futures for the United States in 2100 show that, in the absence of any adaptive urban design, megapolitan expansion, alone and separate from greenhouse gas-induced forcing, can be expected to raise near-surface temperatures 1–2 °C not just at the scale of individual cities but over large regional swaths of the country. This warming is a significant fraction of the 21st century greenhouse gas-induced climate change simulated by global climate models. Using a suite of regional climate simulations, we assessed the efficacy of commonly proposed urban adaptation strategies, such as green, cool roof, and hybrid approaches, to ameliorate the warming. Our results quantify how judicious choices in urban planning and design cannot only counteract the climatological impacts of the urban expansion itself but also, can, in fact, even offset a significant percentage of future greenhouse warming over large scales. Our results also reveal tradeoffs among different adaptation options for some regions, showing the need for geographically appropriate strategies rather than one size fits all solutions.