Patricia Villie

Marc Dommergues

Isabelle Brocheriou

Barbara Giorgina

Jérôme Piccoli

Alexandre Tourret

Hertig

Mar Dommergue

Giorgina Barbara Piccoli

Jérôme Tourret

Alexandre Hertig
email: alexandre.hertig@sorbonne-universite.fr

Why kidneys fail post-partum: a tubulocentric viewpoint

Keywords: Acute kidney injury, Acute tubular necrosis, Pregnancy, Preeclampsia, Post-partum haemorrhage, sFlt-1, PlGF AKI Acute kidney injury ATN Acute tubular necrosis RBP Retinol

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction: the kidney at the end of pregnancy

Maternal endothelium is exposed physiologically to placenta-produced anti-angiogenic factors. Among the most popular and best characterized, are the soluble forms of fms-like tyrosine kinase 1 (sFlt-1) and of endoglin (sEng) [START_REF] Maynard | Excess placental soluble fms-like tyrosine kinase 1 (sFlt1) may contribute to endothelial dysfunction, hypertension, and proteinuria in preeclampsia[END_REF][START_REF] Venkatesha | Soluble endoglin contributes to the pathogenesis of preeclampsia[END_REF]. In the mother, sFlt-1 acts by capturing placental growth factor (PlGF), produced by the placenta, and free vascular endothelial growth factor (VEGF), a cytokine continuously provided by the adjacent podocytes [START_REF] Eremina | VEGF inhibition and renal thrombotic microangiopathy[END_REF]. As with all fenestrated endothelia, the glomerular capillary is critically dependent on VEGF [START_REF] Eremina | Glomerular-specific alterations of VEGF-A expression lead to distinct congenital and acquired renal diseases[END_REF]. During preeclampsia, these proteins are produced in excess and they largely contribute to proteinuria and glomerular endotheliosis (swollen cytoplasm of endothelial cells). During normal pregnancies, sFlt-1 increases from 29 weeks of gestation to term [START_REF] Levine | Circulating angiogenic factors and the risk of preeclampsia[END_REF], and albuminuria also increases [START_REF] Taylor | Albumin excretion in normal pregnancy[END_REF], to a much lesser extent than in preeclampsia, but in proportions closely correlated with concentrations of sFlt-1 in the maternal blood [START_REF] Yoshimatsu | Relationship between urinary albumin and serum soluble fms-like tyrosine kinase 1 (sFlt-1) in normal pregnancy[END_REF]. Thus, while the physiological increase in albuminuria in the first trimester of pregnancy is explained by hyperfiltration [START_REF] Taylor | Albumin excretion in normal pregnancy[END_REF], a second increase occurs during the third trimester, probably because of endotheliosis: Indeed, in 2003 a report by Strevens et al. established that healthy pregnant volunteers, subjected to a kidney biopsy at the end of their gestation, may also have mild endotheliosis [START_REF] Strevens | Glomerular endotheliosis in normal pregnancy and pre-eclampsia[END_REF]. From a renal perspective, normal pregnancy and preeclampsia are not separated by a clear line of demarcation, and are rather a continuum. Representative images of glomerular endotheliosis are shown in Fig. 1a,b.

Full blown endotheliosis may lead to nephrotic-range proteinuria. It is reasonable to assume that glomerular endotheliosis may also contribute to tubular hypoxia, because glomerular capillaries eventually give rise to capillaries-the vasa recta-that provide oxygen to the tubules. The third segment of the proximal tubule, also known as the pars recta, is located at the junction between the cortex and the medulla of the kidney, where oxygen tension (PO 2 ) is physiologically low (10-20 mmHg, as opposed to 40-50 in the cortex) while energetic consumption is high [START_REF] Aukland | Renal oxygen tension[END_REF]. Although human red blood cells are to some extent deformable, a decrease in the capillary lumen-such as seen during endotheliosis-will undoubtedly impair their flow. Overall, this probably explains why, regardless of the obstetric circumstance causing AKI (c.f. Fig. 1c), (ischemic) acute Fig. 1 Endotheliosis is defined by endothelial changes in renal glomeruli, combined with swollen endothelial cells leading to narrowed capillary lumen (a, b). There is a continuum between normal pregnancy and preeclampsia as far as these endothelial changes are concerned. In many cases of post-partum AKI, acute tubular necrosis is seen (c). Electronic microscopy is the best method to objectivize endothelial changes (d) tubular necrosis (ATN) is often the predominant lesion seen in post-partum kidney biopsies [START_REF] Sheikh | Acute renal failure and HELLP syndrome: a single center's experience[END_REF][START_REF] Abraham | Pathogenesis of acute renal failure associated with the HELLP syndrome: a case report and review of the literature[END_REF][START_REF] Sibai | Acute renal failure in hypertensive disorders of pregnancy. Pregnancy outcome and remote prognosis in thirty-one consecutive cases[END_REF]. If severe, preeclampsia alone is sufficient to induce ATN [START_REF] Sibai | Acute renal failure in hypertensive disorders of pregnancy. Pregnancy outcome and remote prognosis in thirty-one consecutive cases[END_REF]. A further insult is constituted if blood loss occurs due to post-partum haemorrhage (for instance, a 40% blood loss makes the PO 2 drop as low as 8-12 mm Hg [START_REF] Nelimarkka | Distribution of renal cortical and Medullary tissue oxygenation in hemorrhagic shock[END_REF]). Overall, in the kidney a gravid endothelium probably increases the vulnerability of proximal tubules, and specific complications such as preeclampsia, post-partum haemorrhage or hypotension will precipitate renal failure. Consequences are particularly tragic in poor income countries [START_REF] Ibarra-Hernández | Acute kidney injury in pregnancy and the role of underlying CKD: a point of view from México[END_REF][START_REF] Fakhouri | Pregnancy-related acute kidney injury in high income countries: still a critical issue[END_REF].

Challenges in the early definition of peripartum AKI

A general definition of AKI (outside of the context of pregnancy) has been available since 2011 [16]. It is based on the degree of increase in serum creatinine, or of decrease in urinary output (Table 1). Any serum creatinine increase of > 0.3 mg/dl, or a urinary output < 0.5 ml/kg/h over 6 h, is now sufficient to define AKI stage 1. Is this "dynamic" definition appropriate in the setting of pregnancy? The absolute value of creatinine is indeed an unsatisfactory measure because both hyper-filtration, which settles in the first weeks of gestation, and haemodilution, decrease the concentration of creatinine in the serum [START_REF] Odutayo | Obstetric nephrology: renal hemodynamic and metabolic physiology in normal pregnancy[END_REF]. Thus, despite intrinsic variability of the different biochemical tests, and while the absolute value of serum creatinine may be decreased physiologically in pregnancy, an increase above baseline remains of interest (of note, hyperfiltration is attenuated in the last phases of pregnancy). However, glomerular filtration returns to baseline and serum creatinine increases in the immediate post-partum, returning to pre-pregnancy values within 2 weeks following delivery, thus making early diagnosis of post partum AKI occasionally challenging [START_REF] Odutayo | Obstetric nephrology: renal hemodynamic and metabolic physiology in normal pregnancy[END_REF][START_REF] Hladunewich | The dynamics of glomerular filtration in the puerperium[END_REF].

A similar course has been observed in women with preeclampsia, even though physiological hyper-filtration is usually less intense in this group [START_REF] Pritchard | The Parkland Memorial Hospital protocol for treatment of eclampsia: evaluation of 245 cases[END_REF].

Circumstances of post-partum AKI

Post-partum haemorrhage

Post-partum haemorrhage represents a classical primum movens for AKI, being a classical cause of acute tubular necrosis. The incidence had been decreasing at least in high-income countries until recently, when the use of procoagulant and anti-fibrinolytic agents such as tranexamic acid (sometimes used at up to 10 g in several hours) has been associated with the resurgence of renal cortical necrosis in France [START_REF] Frimat | A study on the generalized Shwartzman reaction in pregnant rats induced by bacterial endotoxin[END_REF]. It is long known that kidneys from gravid mammals are exceptionally susceptible to coagulation [21]. Thus, a single injection of lipopolysaccharide induces renal cortical necrosis in gravid rabbits, while it generally takes two injections in non-gravid animals [START_REF] Apitz | Die Wirkung bakterieller kulturfiltrate nach umstimmung des gesamten endothels beim kanichen[END_REF]. Used at low dose (1-2 g total) in a recent international trial enrolling women with post-partum haemorrhage, however, the use of tranexamic acid was reported to be safe [START_REF]Effect of early tranexamic acid administration on mortality, hysterectomy, and other morbidities in women with post-partum haemorrhage (WOMAN): an international, randomised, double-blind, placebo-controlled trial[END_REF]. Since its elimination depends on glomerular filtration (the molecular weight of tranexamic acid is 157 Da), we would like to underline the importance of a cautious use in the case of oliguria. Overall, even though ATN is the most probable cause of AKI, it is of the utmost importance to rapidly rule out a diagnosis of cortical necrosis in the aftermath of a post-partum haemorrhage if tranexamic acid or pro-coagulant factors (such as fibrinogen, or recombinant factor VII) have been employed, a fortiori if a disseminated intravascular coagulation has been noted. In contrast to ATN, which typically resolves in 2-3 weeks, cortical necrosis, whilst also ischemic in origin, is irreversible. Magnetic resonance imaging (MRI), especially T2-weighted and gadolinium contrastenhanced T1-weighted acquisitions, is now the gold standard for diagnosis, showing bilateral lack of cortical enhancement involving the cortex at the arterial excretory phase (c.f. Fig. 2) [START_REF] Jeong | MR findings of renal cortical necrosis[END_REF][START_REF] François | MR imaging features of acute bilateral renal cortical necrosis[END_REF]. However, MRI may overestimate the spread of necrosis, and prognostic information should be provided with caution [START_REF] Frimat | A study on the generalized Shwartzman reaction in pregnant rats induced by bacterial endotoxin[END_REF]. Contrast-enhanced ultrasound seems equally sensitive [START_REF] Mckay | Contrast enhanced ultrasound (CEUS) in the diagnosis of post-partum bilateral renal cortical necrosis: a case report and review of the literature[END_REF], but no large series are available.

Preeclamptic syndromes

Renal function is usually "abnormally normal" in women developing preeclampsia: hyperfiltration is less intense, and consequently serum creatinine is not as low as during normal pregnancy [START_REF] Pritchard | The Parkland Memorial Hospital protocol for treatment of eclampsia: evaluation of 245 cases[END_REF][START_REF] Assali | Renal function studies in toxemia of pregnancy; excretion of solutes and renal hemodynamics during osmotic diuresis in hydropenia[END_REF]. Here again glomerular endotheliosis probably abrogates the capacity to maintain pregnancy-related hyperfiltration [START_REF] Lafayette | Nature of glomerular dysfunction in pre-eclampsia[END_REF]. In severe cases, AKI may occur [START_REF] Sibai | Acute renal failure in hypertensive disorders of pregnancy. Pregnancy outcome and remote prognosis in thirty-one consecutive cases[END_REF][START_REF] Prakash | Acute kidney injury in pregnancy with special reference to pregnancy-specific disorders: a hospital based study (2014-2016)[END_REF], but attributing AKI to preeclampsia requires first to ascertain the diagnosis of preeclampsia and, second, to rule out differential diagnoses.

Diagnosis of preeclampsia, a heterogeneous syndrome, can be supported by the measurement of sFlt-1 and PlGF in maternal serum [START_REF] Zeisler | Predictive value of the sFlt-1:PlGF ratio in women with suspected preeclampsia[END_REF], but at present it is still essentially clinical (onset of hypertension and proteinuria after 20 weeks of gestation), including in the latest definition [START_REF] College | Task Force on Hypertension in Pregnancy. Hypertension in pregnancy. Report of the American College of Obstetricians and Gynecologists' Task Force on Hypertension in Pregnancy[END_REF].

The relationship between the degree of endotheliosis and of proteinuria is not clear and, from an ethical point of view, preeclampsia is not an indication for a kidney biopsy (it is generally performed in cases where an underlying disease such as focal and segmental glomerulosclerosis, or IgA nephropathy, is suspected). Thus, the definition of the glomerular lesions independently associated with preeclampsia is difficult if not impossible. Still, severe endotheliosis is probably sufficient to induce ATN [START_REF] Sibai | Acute renal failure in hypertensive disorders of pregnancy. Pregnancy outcome and remote prognosis in thirty-one consecutive cases[END_REF], through the impairment of the blood flow in glomerular capillaries and downstream. Mild endotheliosis is also present in the kidneys from healthy pregnant women [START_REF] Strevens | Glomerular endotheliosis in normal pregnancy and pre-eclampsia[END_REF]: any second hit (pro-ischemic) could thus precipitate ATN. Importantly, the underlying maternal condition could also be at stake: a chronic vascular disease preceding the pregnancy, such as diabetes or hypertension, would be an additional facilitating factor for tubular injury (in so called maternal, as opposed to placental, preeclampsia) [START_REF] Piccoli | A best practice position statement on the role of the nephrologist in the prevention and follow-up of preeclampsia: the Italian study group on kidney and pregnancy[END_REF]. The measure of low-molecular weight proteins in urine is non-invasive and may be informative with regard to tubular damage: retinol-binding protein (RBP), a small protein produced by the liver, is normally reabsorbed by the proximal tubule [START_REF] Xiao | Combined biomarkers evaluation for diagnosing kidney injury in preeclampsia[END_REF][START_REF] Sass | PP019 The role of renal markers in women with and without preeclampsia: evaluation of urinary excretion of podocytes and proteins[END_REF]. Urinary RBP will increase if an ischemic injury has led to ATN and can be similarly expressed as a ratio, over urinary creatinine, in a spot sample. RBP urinary concentration has been reported to be increased in preeclamptic patients [START_REF] Xiao | Combined biomarkers evaluation for diagnosing kidney injury in preeclampsia[END_REF]. Urinary alpha 1 microglobulin, another marker of tubular injury, has also been found a promising biomarker in the setting of normal or complicated pregnancies [START_REF] Codsi | Longitudinal characterization of renal proximal tubular markers in normotensive and preeclamptic pregnancies[END_REF].

Thrombotic microangiopathy

Thrombotic microangiopathy (TMA) is one of the most important conditions to be considered in the differential diagnosis of AKI in pregnancy. The renal endothelial bed is the main victim of haemolytic and uremic syndrome (HUS), whether caused by shigatoxins (typical HUS), or by a genetically determined over-activation of the complement alternative pathway (atypical HUS). The cerebral endothelial bed is particularly affected during the thrombotic thrombocytopenic purpura (TTP), caused by a deficiency in ADAMTS-13 activity (ADAMTS-13 is the protease that cleaves multimers of the von Willebrand factor). Furthermore, the liver endothelial bed can be severely injured by the anti-angiogenic factors produced by placentae from preeclamptic women (sFlt-1 and sEng seem to act synergistically to induce HELLP [haemolysis, elevated liver-enzymes, and low platelets] syndrome) [START_REF] Venkatesha | Soluble endoglin contributes to the pathogenesis of preeclampsia[END_REF][START_REF] George | Syndromes of thrombotic microangiopathy[END_REF].

During pregnancy, it may be extremely complicated to distinguish preeclampsia (and HELLP syndrome) from a flare of TMA due to TTP or HUS, all the more since atypical HUS (aHUS) frequently occurs in the post-partum period and may also cause hypertension, proteinuria and AKI [START_REF] Fakhouri | Pregnancy-associated hemolytic uremic syndrome revisited in the era of complement gene mutations[END_REF]. Since an increase in aspartate aminotransferase is not specific to liver injury, the differential diagnosis may be a real challenge. A common syllogism is to infer AKI due to a renal localization of TMA when patients develop HELLP syndrome. During the classical HELLP syndrome, the thrombotic microangiopathy is limited to the liver [START_REF] Hecht | The pathology of eclampsia: an autopsy series[END_REF]. Although a French study had suggested that HELLP syndrome was frequently associated with genetic mutations in the alternate complement pathway, this was not confirmed by an Italian study published later [START_REF] Fakhouri | Factor H, membrane cofactor protein, and factor I mutations in patients with hemolysis, elevated liver enzymes, and low platelet count syndrome[END_REF][START_REF] Crovetto | The genetics of the alternative pathway of complement in the pathogenesis of HELLP syndrome[END_REF]. The role of variants (not mutants) is debatable, but HELLP and HUS are likely to be different entities.

Although renal TMA has been reported in preeclampsiarelated AKI, such cases are rare: during preeclampsia the activation of the coagulation in the kidney is typically limited to sub-endothelial fibrin deposition, that regresses after delivery [START_REF] Fisher | Hypertension in pregnancy: clinical-pathological correlations and remote prognosis[END_REF]. In many cases, it is the persistence of TMA features after delivery that should compel the clinician to consider aHUS or TTP. Here, the main difficulty is to decide how much time a woman with isolated preeclampsia reasonably needs to recover from HELLP-related TMA. Studies are needed to address this major issue, limiting invasive or costly procedures, such as plasma exchanges or eculizumab.

Acute fatty liver of pregnancy

Acute fatty liver of pregnancy is a rare condition occasionally associated with AKI. Here again, ATN is the predominant lesion and can be explained by the additive effect of severe hyper-bilirubinaemia, now a recognized cause of tubular injury, and accumulation of lipids within tubular epithelial cells [START_REF] Slater | Renal morphological changes in idiopathic acute fatty liver of pregnancy[END_REF]. The latter is, however, debatable as a primary cause: experimentally, it is possible to induce an isolated overload of fatty acids in tubular epithelial cells without inducing AKI [START_REF] Kang | Defective fatty acid oxidation in renal tubular epithelial cells has a key role in kidney fibrosis development[END_REF].

Amniotic fluid embolism

AKI due to ATN is one feature of amniotic fluid embolism, an often fatal condition causing multi-organ failure. In addition, amniotic debris have been reported to occlude renal vessels, which implies that debris reach the left ventricle via veins or a patent foramen ovale. Autopsy findings have indeed demonstrated the presence of amniotic emboli (acellular debris) in many organs, including the kidney, where they may advance as far as the glomerular tuft [START_REF] Liban | A clinicopathologic study of fourteen cases of amniotic fluid embolism[END_REF].

Septic abortion

Clostridium perfringens is a gram-positive bacterium and a saprophytic organism of the human vaginal flora found in an estimated 20% of women. As with other members of the genus Clostridium, it produces a highly dangerous toxin. In the case of a septic abortion (very rarely, in the aftermath of a normal delivery), C. perfringens bacteraemia may rapidly induce severe shock with disseminated intravascular coagulation and renal cortical necrosis [START_REF] Pritchard | Abortion complicated by Clostridium perfringens infection[END_REF]. In that case, in contrast to sepsis-related AKI, renal failure is typically irreversible. As explained previously, MRI or contrast-enhanced ultrasounds are helpful for diagnosis.

Urological disorders

When AKI develops after obstetrical surgery, it is cautious to rule out an unintentional ureteral ligature or a wound in the bladder or ureters causing a urinary leak. Both these conditions are typically painful, but pain may be considered a normal side effect of surgery, and imaging is needed to rule out this complication, especially if surgery was performed in the context of a life-threatening condition such as postpartum haemorrhage.

Conclusions

AKI is a rare but potentially severe complication of different conditions occurring during pregnancy, in particular in the last phases and post-partum. The obstetrician and the nephrologist should be aware of the extreme sensitivity of the tubular epithelium to hypoxia, exacerbated at the end of pregnancy by the abundant concentration of anti-angiogenic factors produced by the placenta.

Fig. 2

 2 Fig. 2 MRI T1 weighted with gadolinium contrast. When compared to the normal aspect of the kidney (a), renal cortical necrosis is characterized by the absence of enhanced contrast in the cortex (b). MRI magnetic resonance imaging

Table 1

 1 Definition of AKI according to the 2011 kidney disease/ improving global outcome (KDIGO) guidelines

	73
	74
	75
	76
	77
	78
	79
	80
	81
	82
	83
	84
	85
	86
	87
	88
	89
	90
	91
	92
	93
	94
	95
	96
	97
	98
	99
	100
	101
	102
	Stage Serum creatinine	Urinary output
	1	1.5-1.9 times baseline	< 0.5 ml/kg/h for 6-12 h
		OR
		≥ 0.3 mg/dl (≥ 26.5 µmol/l)
		increase
	2	2.0-2.9 times baseline	< 0.5 ml/kg/h for ≥ 24 h
	3	3.0 times baseline	< 0.3 ml/kg/h for ≥ 24 h
		OR	OR
		Increase in serum creatinine to	Anuria for ≥ 12 h
		≥ 4.0 mg/dl (≥ 353.6 µmol/l)
		OR
		Initiation of renal replacement
		therapy
		OR, in patients < 18 years, decrease
		in eGFR to < 35 ml/min per 1.73 m 2
	eGFR estimated glomerular filtration rate

Funding This work received no financial support.

Compliance with ethical standards

Conflict of interest

The authors report no conflicts of interest.


Daniel Contreras
email: danielalexandercontreras@gmail.com

Joel Guiot

Romain Suarez

Alan Kirman

REACHING THE HUMAN SCALE: A SPATIAL AND TEMPORAL DOWNSCALING APPROACH TO THE ARCHAEOLOGICAL IMPLICATIONS OF PALEOCLIMATE DATA

Keywords: downscaling, resolution, scale, paleoclimate, climate change, humanenvironment interactions 1. Introduction

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300m spatial scale and with annual resolution. These results, it must be emphasized, are not reconstructions: they are single realizations consistent with coarse-grained data, but individual values are not directly empirically derived. Downscaling generates one set of values consistent with the coarse-grained input data, but the results are not unique solutions (Bierkens et al., 2000, p. 111;Wu and Li, 2006, p. 35). However, they provide a more suitable basis for assessing the human consequences of climate change than can coarsegrained data, as analyses of past human-environment interaction grounded in anthropological archaeology require high spatial and temporal resolution. Anthropological archaeological explanation relies on theoretical models of human behavior and decision-making that are necessarily grounded in human experience: spatial and temporal scales measured in hectares and years rather than regions and centuries.

In this paper we review these issues of scale and resolution in the study of past humanenvironment interactions before demonstrating how spatial and temporal downscaling has the potential to address the challenge of relating spatially and temporally coarse-grained paleoclimate data to fine-grained anthropologically-grounded explanations of past human behavior. We explore the application of spatial downscaling of paleoclimate data to provide high spatial resolution, and temporal downscaling to provide high temporal resolution. This combined approach enables consideration of landscape-scale spatial variability in past climates (vital in topographically diverse landscapes in which climate effects would not have been spatially uniform) as well as consideration of interannual variability. Such downscaling is a necessary tool for considering the human consequences of climate changes documented in spatial and temporal aggregate.

Scale and Resolution in the study of past human-environment interactions

Description and analysis of past human-environment interactions, particularly over the long-term, comprises a fundamental goal of archaeology. This focus underlies several of the recently-articulated "grand challenges for archaeology" [START_REF] Kintigh | Grand challenges for archaeology[END_REF], and has been singled out in 21 st century discussions of the discipline as central to archaeology's contribution to interdisciplinary efforts to understand past and present socioenvironmental systems, as well as of pressing modern relevance (e.g., [START_REF] Van Der Leeuw | Placing archaeology at the center of socio-natural studies[END_REF][START_REF] Smith | Archaeology as a social science[END_REF].

Analysis of long-term human-environment interactions promises improved understanding of both cultural and environmental trajectories, and provides a tool for examining the anthropogenic component of past and modern environment and climate. It is fundamental to ongoing debates over the Anthropocene, in which archaeologists, paleoenvironmental scientists, and geologists dispute the antiquity, character, and significance of that period (e.g., [START_REF] Braje | Earth systems, human agency, and the Anthropocene: Planet Earth in the human age[END_REF][START_REF] Crutzen | How long have we been in the Anthropocene era?[END_REF][START_REF] Erlandson | Archeology and the Anthropocene[END_REF][START_REF] Morrison | Provincializing the Anthropocene[END_REF][START_REF] Ruddiman | The Anthropocene[END_REF][START_REF] Smith | The onset of the Anthropocene[END_REF][START_REF] Zalasiewicz | When did the Anthropocene begin? A mid-twentieth century boundary level is stratigraphically optimal[END_REF].

However, such analysis continues to be challenged by problems of spatial and temporal scale and resolution (cf. [START_REF] Contreras | Correlation is Not Enough -Building Better Arguments in the Archaeology of Human-Environment Interactions[END_REF]. The problem is not unique to archaeology, but central also to modern discussions of climate change: what are the local consequences of global climate? In analytical terms, how can we move from global summary data to local characterizations that enable consideration of the human consequences of climate change? Moreover, as the global effects of local behaviors can also be significant for largescale modeling, the inverse problem is also an important focus: in order to estimate the aggregate global impact of local behaviors, those behaviors must themselves be modeled, taking into account how diverse actors respond to local conditions.

The need to reconcile contrasting scales and resolutions results partly from evidentiary constraints, and partly from contrasting foci and explanatory mechanisms of archaeology on the one hand and paleoclimatic and paleoenvironmental science on the other. Paleoclimatic and paleoenvironmental science often strives to achieve regional and long-term relevance, resulting in coarser (regional and centennial) scales of analysis. In contrast, archaeological explanation relies fundamentally on anthropological models of behaviori.e., understandings of human activity that are grounded in decision-making at local and annual scales. As a result, linking analyses that focus on distinct scales, with varying resolutions, is vital to relating archaeological and paleoclimatic and paleoenvironmental data, and has been the focus of both practical and theoretical consideration in archaeology (e.g., [START_REF] Stein | Scale in archaeology, geosciences, and geoarchaeology[END_REF][START_REF] Lock | Confronting Scale in Archaeology: Issues of Theory and Practice[END_REF][START_REF] Robb | From moments to millennia: Theorizing scale and change in human history[END_REF][START_REF] Kintigh | Was the drought really responsible? Assessing statistical relationships between climate extremes and cultural transitions[END_REF]. Nevertheless, analysis (and even description) of human-environment interaction remains difficult at best with coarse-grained data, and must confront basic questions of scale and resolution: In space, what do regional-scale data mean for landscape-scale experience, and in time, what do centennial-scale data mean for annual or seasonal experience?

This problem is endemic to applications of regional modeling to archaeological explanation (cf. Brayshaw et al., 2011, p. 28): even when they succeed in revealing interesting patterning, coarse-grained models can suggest broad correlations but require finer-grained analyses if explanatory linking mechanisms are to be pursued. High-resolution empirical data might be ideal, but it is (given the character of paleoclimatic, paleoenvironmental, and archaeological archives) rare and spatially and temporally uneven. In their absence, when only a limited number of observations for a broad area with varied topography may be available from recorded and/or modeled data, it is possible to take modern data from that area and, presuming the climate-geography relationships to have remained relatively constant over time, reconstruct realistically spatially variable climate data. Similarly, modern (recorded) interannual variability can serve as the basis for realistically modeling temporal variability in climate variables. Spatial and temporal downscaling thus offer a way of taking advantage of uneven data to explore potential linking mechanisms between climate variables and human behavior, and ultimately to develop arguments that move from correlation to explanation.

2.1 Downscaling Downscaling addresses the problem of deriving small-scale values from large-scale aggregates (Bierkens et al., 2000, pp. 111-118;[START_REF] Wilby | Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods[END_REF]Wu and Li, 2006, pp. 34-36). The principle is that any summary value is by its definition a product of a number of possible individual values that even when not precisely known can be probabilistically estimated. We focus here on statistical downscaling of low-resolution climatic data to enable generation of climate variables at the landscape scale. This is based on applying relationships between high-resolution and low-resolution fields, calibrated based on time periods where both exist, to the target low-resolution field.

The climate-modeling community has explored downscaling of climate data, stimulated by the desire to address regional impacts of climate change in scenarios where global climate models (GCMs) are the primary data source (cf. [START_REF] Fowler | Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling[END_REF][START_REF] Wilby | Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods[END_REF]. The focus has primarily been on future impacts, but the paleoclimate community (e.g., [START_REF] Korhonen | Statistical downscaling of a climate simulation of the last glacial cycle: temperature and precipitation over Northern Europe[END_REF][START_REF] Levavasseur | Present and LGM permafrost from climate simulations: contribution of statistical downscaling[END_REF][START_REF] Vrac | Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe[END_REF] has also begun to explore the potential of downscaling methods as means of examining regional or implications of global models of past climate. Geographically-based downscaling (e.g., [START_REF] Joly | Temperature interpolation based on local information: the example of France[END_REF][START_REF] Martin | Downscaling À Fine Résolution Spatiale des Températures Actuelles et Futures par Modélisation Statistique des Sorties Aladin-Climat sur les Alpes-Maritimes (France)[END_REF][START_REF] Vrac | Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe[END_REF] is one means of dealing with spatially heterogeneous landscapes, and is particularly valuable for applications to past climates, as geographic variables are generally stable over archaeological timescales, whereas regional climate relationships to GCMs may have been significantly different in the past (cf. Vrac et al., 2007, p. 670).

Geographically-based methods which have been applied to paleoclimatological data are based on the calibration of potentially non-linear relationships between the target highresolution variable and its low-resolution version completed by high-resolution geographical variables (topography, distance to sea, etc.; see [START_REF] Vrac | Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe[END_REF]. The most appropriate calibration technique is generally recognized to be a generalized additive model (GAM) [START_REF] Hastie | Generalized Additive Models[END_REF] or a multinominial logistic GAM when the variable to interpolate is categorical [START_REF] Levavasseur | Present and LGM permafrost from climate simulations: contribution of statistical downscaling[END_REF][START_REF] Levavasseur | An objective methodology for potential vegetation reconstruction constrained by climate[END_REF], but other geostatistical methods have also been explored (e.g., [START_REF] Joly | Temperature interpolation based on local information: the example of France[END_REF][START_REF] Martin | Downscaling À Fine Résolution Spatiale des Températures Actuelles et Futures par Modélisation Statistique des Sorties Aladin-Climat sur les Alpes-Maritimes (France)[END_REF]. With fewer potential predictors available at higher spatial resolution and for the past, we have used simple regression to select predictor variables (described in Section 3.2.1, below).

2.2 Downscaling for Archaeology -Potentials and Limitations Archaeologists, given their field's long interest in human-environment interactions, are often avid consumers of paleoclimate data. However, the potential of downscaling has been largely neglected (with important recent exceptions; see [START_REF] Burke | Exploring the impact of climate variability during the Last Glacial Maximum on the pattern of human occupation of Iberia[END_REF][START_REF] Gauthier | The spatial pattern of climate change during the spread of farming into the Aegean[END_REF]. When downscaling has been explored the target scales have, following the climate work, been regional (with the notable recent exception of [START_REF] Bocinsky | A 2,000-year reconstruction of the rain-fed maize agricultural niche in the US Southwest[END_REF].

Spatial and temporal downscaling produces values for climate variables that, for any given pixel in any given year, are in all probability inaccurate: they are single realizations and not unique solutions. However, in aggregate the fidelity to the areal and temporal means represented by the input paleoclimate data is high, and is based on the reasonable assumptions that 1) modern relationships between climate and geographic variables applied also in the past, and 2) 20 th century interannual variability resembles past interannual variability. While the second assumption in particular may be questionable, in the absence of a local annuallyresolved paleoclimate archive a better model for interannual variability is unavailable.

As in any modeling exercise, the data employed might also be critiqued. The spatial and temporal downscaling approach presented here can be applied to virtually any input data, but the accuracy of the results is wholly dependent on the accuracy of those data. Comparisons across space and time within the same dataset, however, can minimize the problem of absolute accuracy of results, and in principle one might also vary the input data if multiple sources were available.

As archaeologists are commonly consumers of paleoclimate data, the archaeological use of climate datawhether from GCMs or derived (as in our case study below) from paleoclimatic reconstructionsis likely to be offline (using previously generated results) rather than coupled to runs of global and/or regional models. Inasmuch as that is the case, archaeologists are more likely to employ statistical downscaling than dynamical downscaling (cf. [START_REF] Fowler | Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling[END_REF]Fowler et al., , pp. 1548Fowler et al., -1552)). Although the lattercoupled models able to both incorporate and enable investigation of feedbacks between human activity and climate dynamicsperhaps have the most analytical promise (cf. Wilby et al., 2004, p. 11 on humanclimate feedbacks for contemporary and future models and [START_REF] Kaplan | Holocene carbon emissions as a result of anthropogenic land cover change[END_REF] on the significance of past human activity for regional and global climate), they are also the most complex conceptually and computationally. We address here the less optimal but nevertheless vital statistical downscaling of pre-existing climate data, which represents the more likely scenario for most archaeological practitioners and still promises to enhance archaeological interpretation of the local consequences of past climates.

Even offline, working with extant paleoclimate data/reconstructions, spatial and temporal downscaling has significant potential to enable analytical consideration of humanenvironment interactions at the scale and resolution necessary to consider the human consequences of climate change. Box's dictum that "all models are wrong" [START_REF] Box | Robustness in the strategy of scientific model building[END_REF] is apropos, and we argue that a downscaling approach produces data that are more useful in archaeological interpretation, and less misleading, than implicit models that posit uniform climate over a large area and over long timespans. It is important to emphasize that using paleoclimate data in archaeological interpretations without downscaling is also an exercise in modeling: it posits a direct one-to-one relationship between local and annual climates and spatially and temporally averaged regional climate data. That being the case, we suggest that consideration of the implications virtually any method of downscaling is likely to improve archaeological interpretation.

The human experience of climate is fundamentally local and annual (if not in fact seasonal), and the consequences of changes in climate are quotidian even if they are measured in aggregate. While the use of global or regional paleoclimate data that is rarely sub-decadal (and often much coarser) reflects the reality of data availability for most archaeological research, a downscaling approach makes it possible to explicitly consider the local and annual implications of such data. This can also provide the requisite spatially explicit and quantitative basis for further modeling that addresses particular questions about the human past, especially past human-environment interactions, including agricultural niche modeling (e.g., [START_REF] Bocinsky | A 2,000-year reconstruction of the rain-fed maize agricultural niche in the US Southwest[END_REF]d'Alpoim Guedes et al., 2016), agroecosystem modeling (e.g., Contreras et al., in press), agent-based modeling of subsistence activity (e.g., [START_REF] Barton | Land use, water and Mediterranean landscapes: modelling long-term dynamics of complex socio-ecological systems[END_REF][START_REF] Kohler | Modelling prehispanic Pueblo societies in their ecosystems[END_REF], and isoscape modeling (e.g., [START_REF] Kootker | Strontium isoscapes in The Netherlands. Spatial variations in 87 Sr/86 Sr as a proxy for palaeomobility[END_REF][START_REF] Willmes | Mapping of bioavailable strontium isotope ratios in France for archaeological provenance studies[END_REF]. The higher resolution produced by downscaling can enable models suited to construction of more robust arguments about the implications of past environmental change for human experience.

Preindustrial agriculture is a likely mechanism linking changing climates to socioeconomic change [START_REF] Currie | Agricultural productivity in past societies: Toward an empirically informed model for testing cultural evolutionary hypotheses[END_REF][START_REF] Schwindt | The Social Consequences of Climate Change in the Central Mesa Verde Region[END_REF], making the relationship of settlement distributions to climate variables a potential means of examining human ecodynamics. Archaeologists have attempted to reconstruct past ecodynamics by, for example, comparing archaeological settlement patterns against spatial patterning of modern maize productivity in Central Mexico [START_REF] Gorenflo | Population and productivity in the Teotihuacan Valley: changing patterns of spatial association in prehispanic central Mexico[END_REF] or against potato and maize productivity in the Central Andes [START_REF] Seltzer | Climatic Change and Its Effect on Prehispanic Agriculture in the Central Peruvian Andes[END_REF]. More recent efforts have involved sophisticated digital modeling of precipitation-limited maize agriculture in the U.S. Southwest [START_REF] Bocinsky | A 2,000-year reconstruction of the rain-fed maize agricultural niche in the US Southwest[END_REF] or temperature-limited cereal agriculture on the Tibetan Plateau (d'Alpoim Guedes et al., 2016). Questions of scale and resolution are critical to the employment of these models, as topographic and climatic diversity can combine to create viable niches within larger areas that are apparently unsuitable. As the example of the Central Andes demonstrates, the potential exploitation (as well as creation and management, cf. [START_REF] Erickson | The Lake Titicaca Basin: A Precolumbian Built Landscape[END_REF][START_REF] Mamani Pati | Geospatial Management of Andean Technology by the Inca Empire[END_REF]) of microclimates as agricultural niches suggests the importance of fine-grained analysis and consideration of the potential plasticity of thresholds.

A fourth dimension of variability can also be critical: both interannual variability and change over time can be vital parameters for inhabitants. Temporal downscaling enables some consideration of interannual variability, potentially vital in areas where long-term means are poor summaries of annual experience (e.g., where interannual variability is high). In areas where agricultural or foraged resources are near biological thermal or hydrologic limits (or even economic ones), long-term means may be poor indicators of subsistence viability, as periodic low minima may be an unacceptable risk. Re-aggregration of climate data over various timespans can also enable direct comparison of one archaeological period to another, for instance across archaeologically significant thresholds.

Applying Downscaling in Archaeology: A Case Study in Holocene Provence (France)

We illustrate the data requirements, spatial and temporal downscaling methods, and interpretive payoffs with an example from Holocene Provence.

Data

We present here a computational approach that uses modern (20 th -21 st century) CNRM20141 and CRU TS v. 3.232 climate data to relate climatic variables (temperature, precipitation, and cloudiness) to geographic variables (primarily elevation and distance-fromthe-sea3 ) through geographically-weighted regression. As the geographic variables are of high spatial resolution where the climate variables are coarse (even for modern data), this relationship can then be used to predict values of climate variables at high spatial resolution.

The problem of temporal resolution is in turn addressed by generating interannual variability within reconstructed trends based on the estimated past seasonal amplitudes and the interannual variability of the modern data. For case study region in Provence that we use here (a topographically diverse 40 km x 40 km area; see Figure 1), [START_REF] Guiot | The Mediterranean Basin and Southern Europe in a warmer world: what can we learn from the past?[END_REF] Holocene climate reconstruction (HolCR) based on inverse vegetation modeling with data from 295 pollen cores provides monthly reconstructions of average daily temperature (ADT), total monthly precipitation (TMP), and % cloudiness (CLD) (see Figure 2). These monthly values for temperature, precipitation, and % cloudiness are provided at centennial steps throughout the Holocene4 , but the 2 (latitude) by 4 (longitude) spatial resolution (approximately 225 x 450 km cells at Mediterranean latitudes) means that for the study area we use here only a single value for each variable is available. Modern data are higher resolution: for average daily temperature (TAV) and average daily precipitation (PAV) data are available on an 8km grid from the CNRM2014 simulation for the period 1951-2005, while cloudiness (CLD) data is available for the period 1951-2010 at 10' resolution (approximately 18 km at the latitude of the study area) from the CRU model. Calculating mean TAV and cumulative PAV for each month is necessary to relate the CRNM2014 and HolCR data.

The data sources and their spatial and temporal resolutions are summarized in Table 2.

Methodology: A spatial and temporal downscaling approach

We downscale in two dimensions, addressing both spatial and temporal scales. Following the hierarchical typology established by Bierkens and colleagues (2000, pp. 111-144), these comprise distinct problems.

For spatial downscaling, the modern geographic and climate data described above are used to calculate relationships of geographic variables to climate variables at data points known from modern data through geographically weighted linear regression using the spgwr package [START_REF] Bivand | spgwr: Geographically Weighted Regression[END_REF] in R (R Core Team, 2016). All raster processing is also carried out in R, using the raster package [START_REF] Hijmans | raster: Geographic data analysis and modeling[END_REF] in R.

For temporal downscaling, the mean, trend, seasonal, and interannual values from modern data for the study area are used to generate monthly values with a modified version of the greenbrown package [START_REF] Forkel | Trend change detection in NDVI time series: Effects of inter-annual variability and methodology[END_REF][START_REF] Forkel | greenbrown -land surface phenology and trend analysis. A package for the R software[END_REF] in R. R code for the procedures detailed below, with reference to the data sources described in Section 3.1 and Table 2, is available in the supplementary online material.

Spatial downscaling

The spatial downscaling that we develop here empirically relates fine-scale auxiliary information to the coarse-grained data available to derive a deterministic model. Geographically-weighted regression of modern climate and geographic time-series data is used to establish functions that relate auxiliary information (geographic characteristics) to coarse-grained paleoclimate data (temperature, precipitation, and cloudiness). As even for past time periods geographic data are available at high resolution, they can be used to derive high-resolution climate variables from the existing low-resolution paleoclimate data.

The spatial downscaling procedure, with the input of spatially homogenous data, produces a set of spatial relationships between location and climate variables that can be used to calculate spatially variable rasters of climate variables at temporal resolution that matches the input data. In our case study, this makes possible high-spatial-resolution climate data at centennial steps throughout the Holocene (following the resolution of Guiot and Kaniewski's dataset).

Using a DEM larger than the study area (~3100 km 2 rather than ~1400 km 2 ), in order to increase the sample of CNRM2014 points, relationships of geographic variables to climate variables at each point are calculated by geographically weighted linear regression. After extracting values from the rasters of the geographic variables at each point where there are CNRM2014 values for climate variables, regressions are calculated to test the value of various geographic variables as predictors of climate variables, and then to estimate the climatic variables using the values of the selected geographic variables.

Geographic variables that are the strongest predictors for our case study (determined by linear regression using the entire dataset of 54 climate datapoints in the ~3100 km 2 area) are elevation and distance from the sea. Irradiancecalculated in GRASS GIS (GRASS Development Team, 2016) with r.sun.dailyand latitude were also tested; neither is a significant predictor, likely as the CNRM2014 data are too spatially sparse to correlate with highly locally-variant environmental characteristics such as irradiance, aspect, topographic roughness, etc. Modern climate data of higher spatial-resolution would allow incorporation of more predictive variables, but even with only two predictor variables that the predictive values are fairly high: mean R 2 values (across all months) are .93 for temperature, .80 for precipitation, and .77 for cloudiness. These regressions, in other words, can predict climate variables at an 8km resolution with a reasonable degree of confidence, and can thus be used to predict values of climate variables on the basis of geographic variables at finer spatial resolutionsi.e., limited in spatial resolution by the latter but not the former.

The selected geographic variables are then used in a geographically-weighted regression to predict values of climate variables for each cell in a 300m pixel raster (spatial resolution could be increased to the limits [30m] of the original DEM with a concomitant increase in computing time5 ). For months and/or climate variables when linear regression indicates that distance from the sea is not a significant predictor, elevation alone is used (see Table 1).

For each cell the value of the target climate variable is predicted based on the specified geographic variables, taking into account all points for which both values are available within a specified search radius. Geographically weighted regression (gwr) works to limit the smoothing of spatial variation in the data by "moving a weighted window over the data, estimating one set of coefficient values at every chosen 'fit' point." [START_REF] Bivand | Geographically Weighted Regression[END_REF]; that is, relationships between geographic and climate variables can vary locally rather than being based necessarily on a regression across the entire dataset. In the case study here the difference between gwr and other methods is not large, but with denser data or a more spatially variable dataset gwr would in principle be preferable, as it would mirror, rather than smoothing, spatial heterogeneity in the input data.

The resulting raster is cropped to the study area. Following this method a raster is produced for each month for each climate variable. The rasters produced by this process -300m resolution, for each monthserve as reference datasets that can be adjusted according to paleoclimate data, producing high spatial-resolution estimates of paleoclimatic conditions.

Temporal downscaling

Temporal downscaling, as we employ it here, is a distinct procedure because it must operate without fine-scale auxiliary information, using a mechanistic model and conditionally stochastic methods. These comprise harmonic models with parameters derived from modern interannual variability and the long-term trends and seasonal amplitudes in the coarse-grained data. These are used to generate time-series that constitutes single realizations of the possible solutions within the parameters for the temporal scale. The resultthe generation of monthly values that are consistent with the coarse-grained averages though individual values are not directly empirically derivedrequires consideration of long-term trends, seasonal amplitudes, and interannual variability.

Centennial means of climate variables for the study area throughout the Holocene are calculated from HolCR and modern reference values for the area calculated from CNRM2014 and CRU data. Centennial trends are provided by linear interpolation from the HolCR data; any three values from HolCR thus produce a continuous 200-year series, while the varying annual means of the HolCR data capture the longer-term Holocene trends. Seasonal amplitudes are calculated from HolCR by linearly interpolating the monthly values from each centennial step and fitting two-term harmonics to each decade. Temperature, precipitation, and cloudiness are calculated independently from one another. Although in principle these variables are likely to be coupled, modeling those complex and dynamic relationships (the region is influenced by both Atlantic and Mediterranean climate systems) would itself be a considerable task [START_REF] Fowler | Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling[END_REF](Fowler et al., , p. 1563)). We have not attempted to model these couplings, but the covariance of these variables with the predictors should limit their divergence except in rare (stochastic) cases.

These trend and seasonal components are combined with interannual variability calculated from CNRM2014 and CRU data. For CNRM2014 standard deviation and range of ADT and TMP are calculated from TAV and PAV for the area from 1951 -2005, and for CRU standard deviation and range of cloudiness values are calculated from the data for 1961 -1990 by subtracting the CRU 'cld' values from 100. Interannual variability throughout the Holocene apparently did not always match modern magnitudes in the region (cf. [START_REF] Büntgen | Years of European Climate Variability and Human Susceptibility[END_REF][START_REF] Luterbacher | Mediterranean climate variability over the last centuries: a review[END_REF], but in the absence of specific proxy data of resolution sufficient to reconstruct interannual variability we use modern data.

The mean, trend, seasonal, and interannual values for the study area are used to generate monthly values for a selected time period. The SimTs() function from the greenbrown package generates monthly values for each climate variable for each year of the specified period by building a time series from multiple time-series components: the mean of the time series, the trend slope, the standard deviation of annual means, the range of annual means, the seasonal amplitude, and randomly-generated short-term intra-annual variation. The sum of these components describes a time series for the selected variable (cf. [START_REF] Forkel | Trend change detection in NDVI time series: Effects of inter-annual variability and methodology[END_REF]Forkel et al., , pp. 2118Forkel et al., -2122)). In order to fit the seasonal patterns in climate variables in the study area, we replace the cosine harmonic that SimTs() uses to generate a seasonal distribution with harmonics fitted to the HolCR values for the period as described above.

The modified SimTs() results are monthly values over a 200-year segment, from which the target segment can be extracted if it is shorter. The monthly values for ADT, TMP, and % cloudiness for that segment are used to calculate monthly anomalies from the HolCR reference values, and new rasters are calculated from the reference rasters by adjusting temperature (average daily temperature in C), precipitation (total monthly precipitation in mm), and cloudiness (% cloudcover) using the monthly anomalies for each year of the selected time window.

As a period of interest is defined and the data for those dates extracted from the Guiot and Kaniewski (2015) dataset, anomalies from the modern data are calculated, and the reference rasters can be used to derive rasters at 300m-resolution for any year of the Holocene for the three climate variables, all by month. To capture the trend in annual means and seasonal amplitude across a target window 200 years of data (three datapoints) are the minimum to work with. Using these in the temporal downscaling process, a time-series of spatially-downscaled rasters can be generated, from which a smaller segment can subsequently be extracted.

Results: From Centennial Means to a Year in Provence

Mediterranean climate variation during the Holocene is modest compared to that of the Pleistocene, but nonetheless paleoclimate data underpins a large number of studies positing relationships between climate changes and cultural developments (see partial reviews in [START_REF] Finné | Climate in the eastern Mediterranean, and adjacent regions, during the past 6000 years -A review[END_REF][START_REF] Roberts | Holocene climate, environment and cultural change in the circum-Mediterranean region[END_REF][START_REF] Robinson | A review of palaeoclimates and palaeoenvironments in the Levant and Eastern Mediterranean from 25,000 to 5000 years BP: setting the environmental background for the evolution of human civilisation[END_REF]. This is particularly true in the eastern Mediterranean (e.g., [START_REF] Kaniewski | Drought and societal collapse 3200 years ago in the Eastern Mediterranean: a review[END_REF][START_REF] Weninger | The Impact of Rapid Climate Change on Prehistoric Societies during the Holocene in the Eastern Mediterranean[END_REF][START_REF] Wiener | The Interaction of Climate Change and Agency in the Collapse of Civilizations ca. 2300-2000 BC[END_REF], reflecting greater abundance of archaeological and paleoclimatic research, but Holocene climate-culture links have also been suggested in the western Mediterranean (e.g., [START_REF] Berger | The 8200 cal BP abrupt environmental change and the Neolithic transition: A Mediterranean perspective[END_REF][START_REF] Carozza | Society and environment in Southern France from the 3rd millennium BC to the beginning of the 2nd millennium BC: 2200 BC a tipping point?[END_REF][START_REF] Weinelt | Climate and societal change in the western Mediterranean area around 4.2 ka BP[END_REF]. As discussed above, the elucidation of these links is limited by chronological resolution and the often incommensurate scales of analysis and explanation pursued by paleoclimatologists and archaeologists.

In the Mediterranean, the diversity of microenvironments characteristic of such a topographically complex region historically has significantly complicated generalization from paleoclimate data, and further complicates the exploration of the human consequences of climate change. In Provence, geographic variability is one of the principle drivers of the region's significant environmental diversity (cf. Blondel and colleagues [2010, p. 13], who single out, "slope, exposition, distance from the sea, steepness, and parent rock type"). Although of course other variables (e.g., water availability, soil depth, etc.) are also influential, environmental contrasts apparent over short distances reflect in large part the interaction of topographic variability and climatic variability. Climate changes may thus affect the spatial distribution of environmental variability as well as the environment in aggregate; both can impact the human inhabitants of a landscape. Interannual variability, which can be obscured by long-term means, may also be particularly significant for inhabitants.

Employing a spatial and temporal downscaling approach to explore the human consequences of past climate changes at large spatial scale and high temporal resolution to a Mediterranean case provides a means of addressing the challenges of a) reconciling scales and resolutions, and b) exploring the implications of geographic and interannual variability. The case study area in Provence explores this across an approximately 1400 km 2 study area (Figure 1) that spans significant topographic variability: elevations range between 50 and 1200 masl and the area includes both the floodplain of the Durance River and the steep limestone ridge of the Luberon. For the period for which instrumental data are available (or modeled data based directly on instrumental data; namely the CNRM2014 and CRU datasets), average daily temperatures (TAV) vary in space by 4-5 C in each month of the year, and total monthly precipitation (TMP) by 12-32 mm (see Figure 3). Long-term temporal variation, by comparison, assessed from the HolCR dataset across the entire Holocene for the cell including the study area, is generally more modest: AMT has varied by approximately 1-2.5 C, depending on the month, and TMP has ranged by 10-20 mm, depending on the month (see Figure 4).

The combination of spatial and interannual variability produces marked contrasts across the study area, belying the homogeneity fundamental to a coarse-grained reconstruction. Downscaling of AMT in the study area at 2400 BPthe coolest period of the Holocenefor instance, demonstrates both the strong seasonality recorded in the input data and the spatial variability in temperature produced by elevation gradients that is absent in the input data but produced by the downscaling process (Figure 5). AMT values from the HolCR dataset for 2400 BP range from 3.1 to 21.6 C, while the downscaled rasters display lower minima and higher maxima, reflecting spatially variable values for each month (Table 3).

The addition of temporal downscaling makes it possible to move from spatiallyvariable but static climate reconstructions to time-series of spatially-variable reconstructions that better reflect the variable and dynamic environments that inhabitants of the region would have experienced. The addition of temporal variability following centennial trends is illustrated in Figure 6, while Figure 7 demonstrates the results of temporal downscaling to generate variability following centennial trends and modern interannual variability, in this case precipitation in the month of March for the period 4004 BP -3096 BP, the driest period of the Holocene. Where HolCR provided a single TMP value of 36.2 mm and spatial downscaling produced a spatial range of 25.2 -69.2 mm (Figure 7a), temporal downscaling to generate interannual variability produced a sequence of rasters whose minima range from 0 -33.9 mm and whose maxima range from 27.5 -77.8 mm (Figure 7b; this is a single realization illustrating one possible solution).

These downscaled data open new analytical possibilities, particularly regarding human-environment interactions and potential impacts of climate change. Variability of the magnitude and at the spatial and temporal scales visible in Figure 7b can be vital to archaeological interpretation, and downscaling enables consideration, for instance, of whether site distributions are random with respect to climate variables. Various other factorsnotably chronological resolution, landscape taphonomy, and recovery biasmake assessment of settlement pattern data in the study area an analytical challenge, but even with such challenges downscaled paleoclimate data have the potential to generate hypotheses that would have otherwise remained inaccessible.

The Late Iron Age expansion of settlement in the study area illustrated in Figure 8, for example, might represent a simple infilling of the landscape as population increased (push factors: local population increase, political and/or economic imperatives, etc.), and/or it might represent the results of the opening up of previously unused areas (pull factors: changes in agricultural practices or technologies [irrigation, iron plowshares, etc.)], shifts in crop preference, willingness to accept less productive land, changes in climate, etc.). Downscaled climate data make it possible to evaluate the hypothesis that changing climate enabled agricultural expansion into areas previous insufficiently productive to be exploited: comparison of the quantities and variability of precipitation in the areas settled (Figure 8) suggest little change from the Early to Late Iron Age, and the summarized values around each settlement do not show any strong contrast from one period to the next (see boxplots at left in Figure 8; the climate-driven contrast in standard deviations is clearly statistically significant, but the magnitude of difference [a decline of 2% in TMP] is probably too low to suggest any notable shift in agricultural potential).

Higher-resolution cultural chronology, as well as specific consideration of the hydrologic needs of particular crops, might enable further evaluation of climate impacts in the 3 rd millennium BP. For that or any other period, resolution of the cultural chronology is a limiting factor in interpreting any effects of climate change: although the downscaled data enable tracking changing spatial patterns of climate variability, the settlement data do not always allow tracking of changes in settlement patterns at comparable temporal resolution, and aggregation of the climate data over archaeological periods (each of approximately four centuries here) may efface important variability. However, this evidence of broad consistency in climatic conditions and niches exploited suggests that climate was not a strong driver of settlement pattern in the study area during this period (and moreover the apparent sudden increase in site density in the Late Iron Age is in fact an artifact of time-averaging and was rather the result of gradual growth (cf. [START_REF] Isoardi | Archéodémographie des sociétés protohistoriques de Sud-Est de la France[END_REF]). Such (preliminary) negative evidence only becomes possible with downscaled data; with only coarse-grained data like that in Figure 7a, questions like these, vital to considering potential impacts of past climate changes, cannot even be asked.

Conclusions

The simulated data produced by spatial and temporal downscaling capture both spatial variability and interannual variation in climatic factors, parameters fundamental to assessing the human consequences of climate changes. Examining such consequences at high resolution is necessary to analysis of the significance of climatic factors for such fundamental human activities as agriculture, and thus vital to the articulation of mechanisms linking climate and cultural change. There are drawbacks: downscaling adds a further layer of analysis, and can create a seductive precision when in fact it produces non-unique solutions that should be understand as reasonable but not necessarily accurate. However, not downscaling is also dangerous: it represents an implicit downscaling, in which coarse-grained data are presumed to indicate homogeneity within each granule, and understood as relevant at scales finer than those measured but without explicit mechanisms to relate regional to local or time-averaged to temporally-variable.

The methodology that we have presented here is straightforward to apply for any portion of the Holocene anywhere in the Mediterranean Basin using the same datasets, and the approach is adaptable to other regions and input data. The value of such data manipulation is analogous to what Lake (2015, p. 9) describes with reference to archaeological simulation modeling: it enables the virtual disaggregation of spatially and temporally coarse-grained data, and thus constitutes an important tool in shifting to a human scale of analysis. It can provide the raw material for further modeling and analysis focused on socioecological systems (advocated as a unique and significant contribution of archaeology to studies of sustainability and resilience; cf. [START_REF] Barton | Looking for the future in the past: Long-term change in socioecological systems[END_REF][START_REF] Kohler | The Model-based Archaeology of Socionatural Systems[END_REF][START_REF] Van Der Leeuw | Toward an integrated history to guide the future[END_REF]. Such modeling often requires higherresolution and larger-scale data than that generally available from paleoclimate archives; indeed one of the benefits of such models is that they mandate explicit consideration of data requirements. The problem is not uniquely archaeological: developing agent-based models, agroecosystem models, or erosion models at scales directly relatable to human experience and decision-making is as much a challenge for socioecological science of the present as of the past. Downscaling tools are thus as needed in present-day modeling as in archaeological simulation, and are vital for considering, for instance, the specific implications at local scales i.e., the human impactsof the 1.5 -2 °C of global warming targeted by the COP21 agreement. Methodologies like that presented here thus add needed components to the analytical toolkit for past human-environment dynamics, and potentially contribute to exploration of present and future human-environment dynamics as well.

List of Figures

Figure 1: Area of the case study. Figure 2: Annual means (calculated from monthly values) of Holocene temperature (ADT) and precipitation (TMP) for the study area in centennial steps, from the HolCR dataset [START_REF] Guiot | The Mediterranean Basin and Southern Europe in a warmer world: what can we learn from the past?[END_REF] adjusted by using CNRM2014 data as a modern reference. Data on % cloudiness is also included, but not plotted here. Figure 3: Mean values (ADT and TMP) of each of the 24 CNRM2014 datapoints within the study area for all points within the study area for the period 1950-2005i.e., the spatial variability in temperature and precipitation over a 56-year span. Labels denote the range of variability for each month. Figure 4: Diachronic variability in ADT and TMP in the HolCR dataset for the cell including the study area, throughout the Holocene (data centered but not scaled). Figure 5: Spatially downscaled monthly rasters for 2400 BP, the coolest period of the Holocene, with 100m contours derived from the SRTM30 DEM. Table 1: Geographic variables and their predictive utility in the spatial downscaling process.

List of Tables

Figure 6 :

 6 Temporally downscaled annual means of temperature (ADT) and precipitation (TMP) for the period 4200-4000 BP in the study area, with HolCR datapoints (solid circles) for reference. Inter-annual variability is based on the range and standard deviation of modern (CNRM2014) data, and the means and trends of the time series provided by the HolCR values (see Section 3.2.2).

Figure 7 :

 7 a) Example of HolCR data for October 4000 BP, with CNRM2014 datapoints (black circles) and 100m elevation contours derived from the SRTM30 DEM. The source data provides monthly values like this in centennial steps. b) After spatial downscaling, values for March 4000 BP (middle panel) are spatially variable, and can be temporally downscaled for intervening years following centennial trends and modern interannual variability (e.g., illustrated in the nine panels here, the month of March for 4004 BP -3096 BP).

Figure 8 :

 8 Early (green diamonds) and newly established Late (red triangles) Iron Age occupation and agricultural sites, plotted on 300m raster of mean annual TMP and standard deviation in TMP for the Early Iron Age and Late Iron Age (approximately 2700-2400 and 2400-2002 BP, respectively). Boxplots indicate precipitation niches occupied by occupation and agricultural sites in the Early and Late Iron Age: each datapoint represents the mean TMP over the period within a 200m buffer around a site.

Table 2 :

 2 Data sources.

Table 3 :

 3 Monthly temperatures from HolCR and downscaling results for the study area at 2400 BP. Acknowledgements Research has been funded by Labex OT-Med (ANR-11-LABEX-0061), the AMEDEES project, supported by A*MIDEX project 11-IDEX-0001-02, "Investissements d'Avenir"

A simulation model based on instrumental data, described at http://www.cnrm.meteo.fr/spip.php?article125 and available from the DRIAS Portal: http://www.drias-climat.fr/

Global coverage climate data at 0.5° resolution from 1901-2014, described in[START_REF] Harris | Updated high-resolution grids of monthly climatic observations-the CRU TS3. 10 Dataset[END_REF][START_REF] New | A high-resolution data set of surface climate over global land areas[END_REF], and available at https://5 data.uea.ac.uk/cru/data/hrg/cru_ts_3.23/cruts.1506241137.v3.23/

Derived from the SRTM 30m digital elevation model (DEM)(NASA JPL, 2013). As detailed below, more environmental variables could in principle be included. In fact, for the case study, for each month and each climatic variable multiple environmental variables were tested and those with the strongest predictive value used (see Table1).

Available in the OT-Med data catalog at http://database.otmed.fr/geonetworkotmed/srv/eng/search -|54b9bf34-57ae-45ea-b455-9f90351e538f

The target resolutionhere 300mdepends on analytic needs and practical concerns about computing time and subsequent data management.

French Government project of the French National Research Agency (ANR) and by ECCOREV (AMEDEES-DB project). We thank the other members of the AMENOPHYS and AMEDEES projects, particularly Alberte Bondeau, Loup Bernard, Eneko Hiriart, Nobi Hanaki, and Sylvie Thoron. The paper has been considerably improved thanks to the thoughtful and constructive comments of two anonymous reviewers. The archaeological data in Figure 8, from the Patriarche database (http://www.culturecommunication.gouv.fr/Politiques-ministerielles/Archeologie/Etuderecherche/Carte-archeologique-nationale), were kindly provided by the Ministère de la Culture et de la Communication (DRAC PACA/SRA).

Data source

Summary description Variables used

Spatial resolution

Temporal resolution and span

Reference and data url

CNRM2014

Simulated dataset based on the limited-area aladin-Climate model (Aire Limited Adaptation Dynamic development InterNational) and corrected by a quantile-quantile method to SAFRAN [START_REF] Vidal | A 50-year high-resolution atmospheric reanalysis over France with the Safran system[END_REF].